diff --git a/DESCRIPTION b/DESCRIPTION
index c8c8594bf..a8426d509 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: Giotto
 Title: Spatial Single-Cell Transcriptomics Toolbox
-Version: 4.0.6
+Version: 4.0.9
 Authors@R: c(
 	person("Ruben", "Dries", email = "rubendries@gmail.com",
 		 role = c("aut", "cre"), comment = c(ORCID = "0000-0001-7650-7754")),
@@ -27,11 +27,11 @@ URL: https://drieslab.github.io/Giotto/, https://github.com/drieslab/Giotto
 BugReports: https://github.com/drieslab/Giotto/issues
 RoxygenNote: 7.3.1
 Depends:
-    base (>= 3.5.0),
-    utils (>= 3.5.0),
-    R (>= 3.5.0),
+    base (>= 4.1.0),
+    utils (>= 4.1.0),
+    R (>= 4.1.0),
     methods,
-    GiottoClass (>= 0.3.0)
+    GiottoClass (>= 0.3.1)
 Imports:
     BiocParallel,
     BiocSingular,
@@ -41,12 +41,12 @@ Imports:
     dbscan (>= 1.1-3),
     ggplot2 (>= 3.1.1),
     ggrepel,
-    GiottoUtils (>= 0.1.7),
-    GiottoVisuals (>= 0.2.0),
+    GiottoUtils (>= 0.1.9),
+    GiottoVisuals (>= 0.2.2),
     igraph (>= 1.2.4.1),
     jsonlite,
     limma,
-    Matrix (>= 1.6.2),
+    Matrix (>= 1.6-2),
     MatrixGenerics,
     plotly,
     progressr,
diff --git a/NAMESPACE b/NAMESPACE
index 7b3eb6556..818eb8c4d 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,5 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(print,combIcfObject)
+S3method(print,icfObject)
 export("%>%")
 export("activeFeatType<-")
 export("activeSpatUnit<-")
@@ -145,13 +147,12 @@ export(create_jackstrawplot)
 export(create_screeplot)
 export(crop)
 export(cropGiottoLargeImage)
-export(crossSectionGenePlot)
-export(crossSectionGenePlot3D)
+export(crossSectionFeatPlot)
+export(crossSectionFeatPlot3D)
 export(crossSectionPlot)
 export(crossSectionPlot3D)
 export(detectSpatialCorFeats)
 export(detectSpatialCorFeatsMatrix)
-export(detectSpatialCorGenes)
 export(detectSpatialPatterns)
 export(dimCellPlot)
 export(dimCellPlot2D)
@@ -258,7 +259,7 @@ export(heatmSpatialCorGenes)
 export(hexVertices)
 export(hyperGeometricEnrich)
 export(initHMRF_V2)
-export(insertCrossSectionGenePlot3D)
+export(insertCrossSectionFeatPlot3D)
 export(insertCrossSectionSpatPlot3D)
 export(installGiottoEnvironment)
 export(instructions)
diff --git a/NEWS.md b/NEWS.md
index fcc84a1f9..5fe9c1560 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,40 @@
+# Giotto 4.0.10 TBD
+
+## Bug fixes
+* Fix error in `plotInteractivePolygons()` when providing a spatial plot with a continuous scale [#964](https://github.com/drieslab/Giotto/issues/964) by jweis3
+* Fix error in DWLS `find_dampening_constant()` when `S[subset, ]` produces only 1 gene.
+
+# Giotto 4.0.9
+
+## Breaking changes
+* Deprecated `detectSpatialCorGenes()` removed. Use `detectSpatialCorFeats()` instead
+* Deprecated `findInteractionChangedGenes()` removed. Use `findInteractionChangedFeats()` instead
+* Deprecated `findCellProximityGenes()` removed. Use `findInteractionChangedFeats()` instead
+
+## Bug fixes
+* Add catch in `runPCAprojectionBatch()` for when ncp requested exceeds number of feats used
+* Make `spatCellCellcom()` respect `verbose` flag [#949](https://github.com/drieslab/Giotto/issues/949) by rbutleriii
+
+## Enhancements
+* `print()` methods for `icfObject` and `combIcfObject`
+
+## Changes
+* require GiottoUtils (>= 0.1.9)
+
+# Giotto 4.0.8 (2024/05/22)
+
+## Breaking changes
+* `crossSectionGenePlot()` removed. Use `crossSectionFeatPlot()` instead
+* `crossSectionGenePlot3D()` removed. Use `crossSectionFeatPlot3D()` instead
+* `insertCrossSectionGenePlot3D()` removed Use `insertCrossSectionFeatPlot3D()` instead
+
+## Bug fixes
+* `binSpect()` param passing error introduced in _v4.0.6_
+* updated `viewHMRFresults3D()` and `viewHMRFresults2D()`
+* updated `createCrossSections()`, `insertCrossSectionSpatPlot3D()`, `crossSectionPlot()`, `crossSectionFeatPlot3D()`, `insertCrossSectionFeatPlot3D()`, `crossSectionPlot3D()`, `crossSectionFeatPlot()`
+
+## Changes
+* GiottoVisuals (>= 0.2.2), GiottoClass (>= 0.3.1), GiottoUtils (>= 0.1.8) are now required.
 
 # Giotto 4.0.6 (2024/05/13)
 
diff --git a/R/auxiliary_giotto.R b/R/auxiliary_giotto.R
index 3ce6d6296..11dafbe7a 100644
--- a/R/auxiliary_giotto.R
+++ b/R/auxiliary_giotto.R
@@ -113,24 +113,25 @@
 #'
 #' filterDistributions(g)
 #' @export
-filterDistributions <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = c("raw", "normalized", "scaled", "custom"),
-    method = c("threshold", "sum", "mean"),
-    expression_threshold = 1,
-    detection = c("feats", "cells"),
-    plot_type = c("histogram", "violin"),
-    scale_y = NULL,
-    nr_bins = 30,
-    fill_color = "lightblue",
-    scale_axis = "identity",
-    axis_offset = 0,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "filterDistributions") {
+filterDistributions <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        expression_values = c("raw", "normalized", "scaled", "custom"),
+        method = c("threshold", "sum", "mean"),
+        expression_threshold = 1,
+        detection = c("feats", "cells"),
+        plot_type = c("histogram", "violin"),
+        scale_y = NULL,
+        nr_bins = 30,
+        fill_color = "lightblue",
+        scale_axis = "identity",
+        axis_offset = 0,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "filterDistributions") {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -145,7 +146,8 @@ filterDistributions <- function(gobject,
     # expression values to be used
     values <- match.arg(
         expression_values,
-        unique(c("raw", "normalized", "scaled", "custom", expression_values)))
+        unique(c("raw", "normalized", "scaled", "custom", expression_values))
+    )
     expr_values <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -170,15 +172,18 @@ filterDistributions <- function(gobject,
     if (detection == "feats") {
         if (method == "threshold") {
             feat_detection_levels <- data.table::as.data.table(
-                rowSums_flex(expr_values >= expression_threshold))
+                rowSums_flex(expr_values >= expression_threshold)
+            )
             mytitle <- "feat detected in # of cells"
         } else if (method == "sum") {
             feat_detection_levels <- data.table::as.data.table(
-                rowSums_flex(expr_values))
+                rowSums_flex(expr_values)
+            )
             mytitle <- "total sum of feature detected in all cells"
         } else if (method == "mean") {
             feat_detection_levels <- data.table::as.data.table(
-                rowMeans_flex(expr_values))
+                rowMeans_flex(expr_values)
+            )
             mytitle <- "average of feature detected in all cells"
         }
 
@@ -216,15 +221,18 @@ filterDistributions <- function(gobject,
     } else if (detection == "cells") {
         if (method == "threshold") {
             cell_detection_levels <- data.table::as.data.table(
-                colSums_flex(expr_values >= expression_threshold))
+                colSums_flex(expr_values >= expression_threshold)
+            )
             mytitle <- "feats detected per cell"
         } else if (method == "sum") {
             cell_detection_levels <- data.table::as.data.table(
-                colSums_flex(expr_values))
+                colSums_flex(expr_values)
+            )
             mytitle <- "total features per cell"
         } else if (method == "mean") {
             cell_detection_levels <- data.table::as.data.table(
-                colMeans_flex(expr_values))
+                colMeans_flex(expr_values)
+            )
             mytitle <- "average number of features per cell"
         }
 
@@ -302,22 +310,23 @@ filterDistributions <- function(gobject,
 #'
 #' filterCombinations(g)
 #' @export
-filterCombinations <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = c("raw", "normalized", "scaled", "custom"),
-    expression_thresholds = c(1, 2),
-    feat_det_in_min_cells = c(5, 50),
-    min_det_feats_per_cell = c(200, 400),
-    scale_x_axis = "identity",
-    x_axis_offset = 0,
-    scale_y_axis = "identity",
-    y_axis_offset = 0,
-    show_plot = TRUE,
-    return_plot = FALSE,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "filterCombinations") {
+filterCombinations <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        expression_values = c("raw", "normalized", "scaled", "custom"),
+        expression_thresholds = c(1, 2),
+        feat_det_in_min_cells = c(5, 50),
+        min_det_feats_per_cell = c(200, 400),
+        scale_x_axis = "identity",
+        x_axis_offset = 0,
+        scale_y_axis = "identity",
+        y_axis_offset = 0,
+        show_plot = TRUE,
+        return_plot = FALSE,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "filterCombinations") {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -333,7 +342,8 @@ filterCombinations <- function(gobject,
     # expression values to be used
     values <- match.arg(
         expression_values,
-        unique(c("raw", "normalized", "scaled", "custom", expression_values)))
+        unique(c("raw", "normalized", "scaled", "custom", expression_values))
+    )
     expr_values <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -361,16 +371,20 @@ filterCombinations <- function(gobject,
 
             # first remove feats
             filter_index_feats <- rowSums_flex(
-                expr_values >= threshold) >= min_cells_for_feat
+                expr_values >= threshold
+            ) >= min_cells_for_feat
             removed_feats <- length(filter_index_feats[
-                filter_index_feats == FALSE])
+                filter_index_feats == FALSE
+            ])
             det_cells_res[[combn_i]] <- removed_feats
 
             # then remove cells
             filter_index_cells <- colSums_flex(expr_values[
-                filter_index_feats, ] >= threshold) >= min_feats_per_cell
+                filter_index_feats,
+            ] >= threshold) >= min_feats_per_cell
             removed_cells <- length(filter_index_cells[
-                filter_index_cells == FALSE])
+                filter_index_cells == FALSE
+            ])
             det_feats_res[[combn_i]] <- removed_cells
         }
 
@@ -393,7 +407,8 @@ filterCombinations <- function(gobject,
     result_DT[["min_detected_feats_per_cell"]] <- min_det_feats_per_cell
     result_DT[["combination"]] <- paste0(
         result_DT$feat_detected_in_min_cells, "-",
-        result_DT$min_detected_feats_per_cell)
+        result_DT$min_detected_feats_per_cell
+    )
 
     result_DT <- result_DT[, .(
         threshold,
@@ -420,18 +435,22 @@ filterCombinations <- function(gobject,
         color = as.factor(threshold)
     ))
     pl <- pl + scale_color_discrete(
-        guide = guide_legend(title = "threshold(s)"))
+        guide = guide_legend(title = "threshold(s)")
+    )
     pl <- pl + ggrepel::geom_text_repel(data = result_DT, aes(
         x = removed_cells + x_axis_offset,
         y = removed_feats + y_axis_offset,
         label = combination
     ))
     pl <- pl + ggplot2::scale_x_continuous(
-        trans = scale_x_axis, limits = c(0, maximum_x_value))
+        trans = scale_x_axis, limits = c(0, maximum_x_value)
+    )
     pl <- pl + ggplot2::scale_y_continuous(
-        trans = scale_y_axis, limits = c(0, maximum_y_value))
+        trans = scale_y_axis, limits = c(0, maximum_y_value)
+    )
     pl <- pl + ggplot2::labs(
-        x = "number of removed cells", y = "number of removed feats")
+        x = "number of removed cells", y = "number of removed feats"
+    )
 
 
     return(plot_output_handler(
@@ -491,23 +510,24 @@ filterCombinations <- function(gobject,
 #'
 #' filterGiotto(g)
 #' @export
-filterGiotto <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("raw", "normalized", "scaled", "custom"),
-    expression_threshold = 1,
-    feat_det_in_min_cells = 100,
-    min_det_feats_per_cell = 100,
-    spat_unit_fsub = ":all:",
-    feat_type_ssub = ":all:",
-    all_spat_units = NULL,
-    all_feat_types = NULL,
-    poly_info = NULL,
-    tag_cells = FALSE,
-    tag_cell_name = "tag",
-    tag_feats = FALSE,
-    tag_feats_name = "tag",
-    verbose = TRUE) {
+filterGiotto <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("raw", "normalized", "scaled", "custom"),
+        expression_threshold = 1,
+        feat_det_in_min_cells = 100,
+        min_det_feats_per_cell = 100,
+        spat_unit_fsub = ":all:",
+        feat_type_ssub = ":all:",
+        all_spat_units = NULL,
+        all_feat_types = NULL,
+        poly_info = NULL,
+        tag_cells = FALSE,
+        tag_cell_name = "tag",
+        tag_feats = FALSE,
+        tag_feats_name = "tag",
+        verbose = TRUE) {
     # data.table vars
     cell_ID <- feat_ID <- NULL
 
@@ -574,7 +594,8 @@ filterGiotto <- function(gobject,
     # expression values to be used
     values <- match.arg(
         expression_values,
-        unique(c("raw", "normalized", "scaled", "custom", expression_values)))
+        unique(c("raw", "normalized", "scaled", "custom", expression_values))
+    )
 
     # get expression values to perform filtering on
     # Only the first spat_unit and feat_type provided are filtered.
@@ -596,14 +617,16 @@ filterGiotto <- function(gobject,
 
     ## filter features
     filter_index_feats <- rowSums_flex(
-        expr_values >= expression_threshold) >= feat_det_in_min_cells
+        expr_values >= expression_threshold
+    ) >= feat_det_in_min_cells
     selected_feat_ids <- names(filter_index_feats[filter_index_feats == TRUE])
 
 
 
     ## filter cells
     filter_index_cells <- colSums_flex(expr_values[
-        filter_index_feats, ] >= expression_threshold) >= min_det_feats_per_cell
+        filter_index_feats,
+    ] >= expression_threshold) >= min_det_feats_per_cell
     selected_cell_ids <- names(filter_index_cells[filter_index_cells == TRUE])
 
 
@@ -612,7 +635,8 @@ filterGiotto <- function(gobject,
     if (isTRUE(tag_cells)) {
         cell_meta <- getCellMetadata(gobject = gobject, copy_obj = TRUE)
         cell_meta[][, c(tag_cell_name) := ifelse(
-            cell_ID %in% selected_cell_ids, 0, 1)]
+            cell_ID %in% selected_cell_ids, 0, 1
+        )]
         gobject <- setCellMetadata(
             gobject = gobject, x = cell_meta, initialize = FALSE
         )
@@ -624,7 +648,8 @@ filterGiotto <- function(gobject,
     if (isTRUE(tag_feats)) {
         feat_meta <- getFeatureMetadata(gobject = gobject, copy_obj = TRUE)
         feat_meta[][, c(tag_feats_name) := ifelse(
-            feat_ID %in% selected_feat_ids, 0, 1)]
+            feat_ID %in% selected_feat_ids, 0, 1
+        )]
         gobject <- setFeatureMetadata(
             gobject = gobject, x = feat_meta, initialize = FALSE
         )
@@ -660,19 +685,27 @@ filterGiotto <- function(gobject,
         cat("Feature type: ", feat_type, "\n")
 
         if (isTRUE(tag_cells)) {
-            cat("Number of cells tagged: ", removed_cells, " out of ",
-                total_cells, "\n")
+            cat(
+                "Number of cells tagged: ", removed_cells, " out of ",
+                total_cells, "\n"
+            )
         } else {
-            cat("Number of cells removed: ", removed_cells, " out of ",
-                total_cells, "\n")
+            cat(
+                "Number of cells removed: ", removed_cells, " out of ",
+                total_cells, "\n"
+            )
         }
 
         if (isTRUE(tag_feats)) {
-            cat("Number of feats tagged: ", removed_feats, " out of ",
-                total_feats, "\n")
+            cat(
+                "Number of feats tagged: ", removed_feats, " out of ",
+                total_feats, "\n"
+            )
         } else {
-            cat("Number of feats removed: ", removed_feats, " out of ",
-                total_feats, "\n")
+            cat(
+                "Number of feats removed: ", removed_feats, " out of ",
+                total_feats, "\n"
+            )
         }
     }
 
@@ -695,7 +728,9 @@ filterGiotto <- function(gobject,
 
     # If this function call is not downstream of processGiotto, update normally
     newGiottoObject <- update_giotto_params(
-        newGiottoObject, description = "_filter")
+        newGiottoObject,
+        description = "_filter"
+    )
 
     return(newGiottoObject)
 }
@@ -711,19 +746,20 @@ filterGiotto <- function(gobject,
 #' @description standard function for RNA normalization
 #' @returns giotto object
 #' @keywords internal
-.rna_standard_normalization <- function(gobject,
-    raw_expr,
-    feat_type,
-    spat_unit,
-    library_size_norm = TRUE,
-    scalefactor = 6e3,
-    log_norm = TRUE,
-    log_offset = 1,
-    logbase = 2,
-    scale_feats = TRUE,
-    scale_cells = TRUE,
-    scale_order = c("first_feats", "first_cells"),
-    verbose = TRUE) {
+.rna_standard_normalization <- function(
+        gobject,
+        raw_expr,
+        feat_type,
+        spat_unit,
+        library_size_norm = TRUE,
+        scalefactor = 6e3,
+        log_norm = TRUE,
+        log_offset = 1,
+        logbase = 2,
+        scale_feats = TRUE,
+        scale_cells = TRUE,
+        scale_order = c("first_feats", "first_cells"),
+        verbose = TRUE) {
     # check feature type compatibility
     if (!feat_type %in% c("rna", "RNA")) {
         warning("Caution: Standard normalization was developed for RNA data \n")
@@ -765,37 +801,42 @@ filterGiotto <- function(gobject,
     ## 3. scale
     if (scale_feats == TRUE & scale_cells == TRUE) {
         scale_order <- match.arg(
-            arg = scale_order, choices = c("first_feats", "first_cells"))
+            arg = scale_order, choices = c("first_feats", "first_cells")
+        )
 
         if (scale_order == "first_feats") {
-            if (isTRUE(verbose))
+            if (isTRUE(verbose)) {
                 wrap_msg("\n first scale feats and then cells \n")
+            }
 
             norm_scaled_expr <- t_flex(standardise_flex(
-                x = t_flex(norm_expr), center = TRUE, scale = TRUE))
+                x = t_flex(norm_expr), center = TRUE, scale = TRUE
+            ))
             norm_scaled_expr <- standardise_flex(
-                x = norm_scaled_expr, center = TRUE, scale = TRUE)
-
+                x = norm_scaled_expr, center = TRUE, scale = TRUE
+            )
         } else if (scale_order == "first_cells") {
-            if (isTRUE(verbose))
+            if (isTRUE(verbose)) {
                 wrap_msg("\n first scale cells and then feats \n")
+            }
 
             norm_scaled_expr <- standardise_flex(
-                x = norm_expr, center = TRUE, scale = TRUE)
+                x = norm_expr, center = TRUE, scale = TRUE
+            )
             norm_scaled_expr <- t_flex(standardise_flex(
-                x = t_flex(norm_scaled_expr), center = TRUE, scale = TRUE))
-
+                x = t_flex(norm_scaled_expr), center = TRUE, scale = TRUE
+            ))
         } else {
             stop("\n scale order must be given \n")
         }
     } else if (scale_feats == TRUE) {
         norm_scaled_expr <- t_flex(standardise_flex(
-            x = t_flex(norm_expr), center = TRUE, scale = TRUE))
-
+            x = t_flex(norm_expr), center = TRUE, scale = TRUE
+        ))
     } else if (scale_cells == TRUE) {
         norm_scaled_expr <- standardise_flex(
-            x = norm_expr, center = TRUE, scale = TRUE)
-
+            x = norm_expr, center = TRUE, scale = TRUE
+        )
     } else {
         norm_scaled_expr <- NULL
     }
@@ -853,12 +894,13 @@ filterGiotto <- function(gobject,
 #' @description function for RNA normalization according to osmFISH paper
 #' @returns giotto object
 #' @keywords internal
-.rna_osmfish_normalization <- function(gobject,
-    raw_expr,
-    feat_type,
-    spat_unit,
-    name = "custom",
-    verbose = TRUE) {
+.rna_osmfish_normalization <- function(
+        gobject,
+        raw_expr,
+        feat_type,
+        spat_unit,
+        name = "custom",
+        verbose = TRUE) {
     # check feature type compatibility
     if (!feat_type %in% c("rna", "RNA")) {
         warning("Caution: osmFISH normalization was developed for RNA in situ
@@ -869,12 +911,15 @@ filterGiotto <- function(gobject,
     norm_feats <- (raw_expr[] / rowSums_flex(raw_expr[])) * nrow(raw_expr[])
     # 2. normalize per cells with scale-factor equal to number of cells
     norm_feats_cells <- t_flex((t_flex(norm_feats) /
-                                  colSums_flex(norm_feats)) * ncol(raw_expr[]))
+        colSums_flex(norm_feats)) * ncol(raw_expr[]))
 
     # return results to Giotto object
-    if (verbose == TRUE)
-        message("\n osmFISH-like normalized data will be returned to the",
-                name, "Giotto slot \n")
+    if (verbose == TRUE) {
+        message(
+            "\n osmFISH-like normalized data will be returned to the",
+            name, "Giotto slot \n"
+        )
+    }
 
     norm_feats_cells <- create_expr_obj(
         name = name,
@@ -903,20 +948,22 @@ filterGiotto <- function(gobject,
 #' Adapted from https://gist.github.com/hypercompetent/51a3c428745e1c06d826d76c3671797c#file-pearson_residuals-r
 #' @returns giotto object
 #' @keywords internal
-.rna_pears_resid_normalization <- function(gobject,
-    raw_expr,
-    feat_type,
-    spat_unit,
-    theta = 100,
-    name = "scaled",
-    verbose = TRUE) {
+.rna_pears_resid_normalization <- function(
+        gobject,
+        raw_expr,
+        feat_type,
+        spat_unit,
+        theta = 100,
+        name = "scaled",
+        verbose = TRUE) {
     # print message with information #
-    if (verbose)
-      message("using 'Lause/Kobak' method to normalize count matrix If used in
+    if (verbose) {
+        message("using 'Lause/Kobak' method to normalize count matrix If used in
       published research, please cite:
       Jan Lause, Philipp Berens, Dmitry Kobak (2020).
       'Analytic Pearson residuals for normalization of single-cell RNA-seq UMI
       data' ")
+    }
 
 
     # check feature type compatibility
@@ -927,9 +974,13 @@ filterGiotto <- function(gobject,
 
     if (methods::is(raw_expr[], "HDF5Matrix")) {
         counts_sum0 <- methods::as(matrix(
-            MatrixGenerics::colSums2(raw_expr[]), nrow = 1), "HDF5Matrix")
+            MatrixGenerics::colSums2(raw_expr[]),
+            nrow = 1
+        ), "HDF5Matrix")
         counts_sum1 <- methods::as(matrix(
-            MatrixGenerics::rowSums2(raw_expr[]), ncol = 1), "HDF5Matrix")
+            MatrixGenerics::rowSums2(raw_expr[]),
+            ncol = 1
+        ), "HDF5Matrix")
         counts_sum <- sum(raw_expr[])
 
         # get residuals
@@ -942,9 +993,11 @@ filterGiotto <- function(gobject,
         z[z < -sqrt(n)] <- -sqrt(n)
     } else {
         counts_sum0 <- methods::as(matrix(Matrix::colSums(
-            raw_expr[]), nrow = 1), "dgCMatrix")
+            raw_expr[]
+        ), nrow = 1), "dgCMatrix")
         counts_sum1 <- methods::as(matrix(Matrix::rowSums(
-            raw_expr[]), ncol = 1), "dgCMatrix")
+            raw_expr[]
+        ), ncol = 1), "dgCMatrix")
         counts_sum <- sum(raw_expr[])
 
         # get residuals
@@ -958,9 +1011,12 @@ filterGiotto <- function(gobject,
     }
 
     # return results to Giotto object
-    if (verbose == TRUE)
-        message("\n Pearson residual normalized data will be returned to the ",
-                name, " Giotto slot \n")
+    if (verbose == TRUE) {
+        message(
+            "\n Pearson residual normalized data will be returned to the ",
+            name, " Giotto slot \n"
+        )
+    }
 
     z <- create_expr_obj(
         name = name,
@@ -1033,23 +1089,24 @@ filterGiotto <- function(gobject,
 #'
 #' normalizeGiotto(g)
 #' @export
-normalizeGiotto <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = "raw",
-    norm_methods = c("standard", "pearson_resid", "osmFISH"),
-    library_size_norm = TRUE,
-    scalefactor = 6e3,
-    log_norm = TRUE,
-    log_offset = 1,
-    logbase = 2,
-    scale_feats = TRUE,
-    scale_genes = NULL,
-    scale_cells = TRUE,
-    scale_order = c("first_feats", "first_cells"),
-    theta = 100,
-    update_slot = "scaled",
-    verbose = TRUE) {
+normalizeGiotto <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = "raw",
+        norm_methods = c("standard", "pearson_resid", "osmFISH"),
+        library_size_norm = TRUE,
+        scalefactor = 6e3,
+        log_norm = TRUE,
+        log_offset = 1,
+        logbase = 2,
+        scale_feats = TRUE,
+        scale_genes = NULL,
+        scale_cells = TRUE,
+        scale_order = c("first_feats", "first_cells"),
+        theta = 100,
+        update_slot = "scaled",
+        verbose = TRUE) {
     ## deprecated arguments
     if (!is.null(scale_genes)) {
         scale_feats <- scale_genes
@@ -1078,7 +1135,8 @@ normalizeGiotto <- function(gobject,
     )
 
     norm_methods <- match.arg(
-        arg = norm_methods, choices = c("standard", "pearson_resid", "osmFISH"))
+        arg = norm_methods, choices = c("standard", "pearson_resid", "osmFISH")
+    )
 
     # normalization according to standard methods
     if (norm_methods == "standard") {
@@ -1163,14 +1221,15 @@ normalizeGiotto <- function(gobject,
 #'
 #' adjustGiottoMatrix(g, covariate_columns = "leiden_clus")
 #' @export
-adjustGiottoMatrix <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    batch_columns = NULL,
-    covariate_columns = NULL,
-    return_gobject = TRUE,
-    update_slot = c("custom")) {
+adjustGiottoMatrix <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        batch_columns = NULL,
+        covariate_columns = NULL,
+        return_gobject = TRUE,
+        update_slot = c("custom")) {
     # Catch for both batch and covariate being null
     if (is.null(batch_columns) & is.null(covariate_columns)) {
         stop("Metadata for either different batches or covariates must be
@@ -1210,12 +1269,14 @@ adjustGiottoMatrix <- function(gobject,
     }
 
     update_slot <- match.arg(
-        update_slot, c("normalized", "scaled", "custom", update_slot))
+        update_slot, c("normalized", "scaled", "custom", update_slot)
+    )
 
     # expression values to be used
     values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_data <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -1241,7 +1302,8 @@ adjustGiottoMatrix <- function(gobject,
     # covariate columns
     if (!is.null(covariate_columns)) {
         covariates <- as.matrix(
-            cell_metadata[, covariate_columns, with = FALSE])
+            cell_metadata[, covariate_columns, with = FALSE]
+        )
     } else {
         covariates <- NULL
     }
@@ -1318,43 +1380,51 @@ adjustGiottoMatrix <- function(gobject,
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #'
-#' processGiotto(gobject = g,
-#' adjust_params = list(covariate_columns = "leiden_clus"))
+#' processGiotto(
+#'     gobject = g,
+#'     adjust_params = list(covariate_columns = "leiden_clus")
+#' )
 #' @export
-processGiotto <- function(gobject,
-    filter_params = list(),
-    norm_params = list(),
-    stat_params = list(),
-    adjust_params = list(),
-    verbose = TRUE) {
+processGiotto <- function(
+        gobject,
+        filter_params = list(),
+        norm_params = list(),
+        stat_params = list(),
+        adjust_params = list(),
+        verbose = TRUE) {
     # filter Giotto
     if (verbose == TRUE) message("1. start filter step")
-    if (!inherits(filter_params, "list"))
+    if (!inherits(filter_params, "list")) {
         stop("filter_params need to be a list of parameters for filterGiotto")
+    }
     gobject <- do.call("filterGiotto", c(gobject = gobject, filter_params))
 
     # normalize Giotto
     if (verbose == TRUE) message("2. start normalization step")
-    if (!inherits(norm_params, "list"))
+    if (!inherits(norm_params, "list")) {
         stop("norm_params need to be a list of parameters for normalizeGiotto")
+    }
     gobject <- do.call("normalizeGiotto", c(gobject = gobject, norm_params))
 
     # add Statistics
     if (verbose == TRUE) message("3. start cell and gene statistics step")
-    if (!inherits(stat_params, "list"))
+    if (!inherits(stat_params, "list")) {
         stop("stat_params need to be a list of parameters for addStatistics ")
+    }
     stat_params[["return_gobject"]] <- TRUE # force this to be true
     gobject <- do.call("addStatistics", c(gobject = gobject, stat_params))
 
     # adjust Giotto, if applicable
     if (!is.null(adjust_params)) {
         if (verbose == TRUE) message("4. start adjusted matrix step")
-        if (!inherits(adjust_params, "list"))
+        if (!inherits(adjust_params, "list")) {
             stop("adjust_params need to be a list of parameters for
                 adjustGiottoMatrix")
+        }
         adjust_params[["return_gobject"]] <- TRUE # force this to be true
         gobject <- do.call(
-            "adjustGiottoMatrix", c(gobject = gobject, adjust_params))
+            "adjustGiottoMatrix", c(gobject = gobject, adjust_params)
+        )
     }
 
     gobject <- update_giotto_params(gobject, description = "_process")
@@ -1387,6 +1457,7 @@ processGiotto <- function(gobject,
 #' @param expression_values expression values to use
 #' @param detection_threshold detection threshold to consider a gene detected
 #' @param return_gobject boolean: return giotto object (default = TRUE)
+#' @param verbose be verbose
 #' @returns giotto object if return_gobject = TRUE
 #' @details
 #' This function will add the following statistics to feature metadata:
@@ -1406,12 +1477,14 @@ processGiotto <- function(gobject,
 #'
 #' addFeatStatistics(g)
 #' @export
-addFeatStatistics <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    detection_threshold = 0,
-    return_gobject = TRUE) {
+addFeatStatistics <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        detection_threshold = 0,
+        return_gobject = TRUE,
+        verbose = TRUE) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -1426,7 +1499,8 @@ addFeatStatistics <- function(gobject,
     # expression values to be used
     expression_values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_data <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -1441,7 +1515,7 @@ addFeatStatistics <- function(gobject,
         feats = rownames(expr_data[]),
         nr_cells = rowSums_flex(expr_data[] > detection_threshold),
         perc_cells = (rowSums_flex(expr_data[] > detection_threshold) /
-                        ncol(expr_data[])) * 100,
+            ncol(expr_data[])) * 100,
         total_expr = rowSums_flex(expr_data[]),
         mean_expr = rowMeans_flex(expr_data[])
     )
@@ -1450,7 +1524,9 @@ addFeatStatistics <- function(gobject,
     mean_expr_det <- NULL
 
     mean_expr_detected <- .mean_expr_det_test(
-        expr_data[], detection_threshold = detection_threshold)
+        expr_data[],
+        detection_threshold = detection_threshold
+    )
     feat_stats[, mean_expr_det := mean_expr_detected]
 
 
@@ -1475,11 +1551,14 @@ addFeatStatistics <- function(gobject,
         metadata_names <- colnames(feat_metadata[])
 
         if ("nr_cells" %in% metadata_names) {
-            message("feat statistics has already been applied once, will be
-                    overwritten")
+            vmsg(
+                .v = verbose, "feat statistics has already been applied",
+                "once; overwriting"
+            )
             feat_metadata[][, c(
                 "nr_cells", "perc_cells", "total_expr", "mean_expr",
-                "mean_expr_det") := NULL]
+                "mean_expr_det"
+            ) := NULL]
             ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
             gobject <- set_feature_metadata(gobject,
                 metadata = feat_metadata,
@@ -1520,16 +1599,19 @@ addFeatStatistics <- function(gobject,
         # normally
         if (is.null(cl)) {
             gobject <- update_giotto_params(gobject,
-                                            description = "_feat_stats")
+                description = "_feat_stats"
+            )
         } else {
             fname <- as.character(cl[[1]])
             if (fname == "addStatistics") {
                 gobject <- update_giotto_params(gobject,
-                                                description = "_feat_stats",
-                                                toplevel = 3)
+                    description = "_feat_stats",
+                    toplevel = 3
+                )
             } else {
                 gobject <- update_giotto_params(gobject,
-                                                description = "_feat_stats")
+                    description = "_feat_stats"
+                )
             }
         }
 
@@ -1553,6 +1635,7 @@ addFeatStatistics <- function(gobject,
 #' @param expression_values expression values to use
 #' @param detection_threshold detection threshold to consider a gene detected
 #' @param return_gobject boolean: return giotto object (default = TRUE)
+#' @param verbose be verbose
 #' @returns giotto object if return_gobject = TRUE
 #' @details
 #' This function will add the following statistics to cell metadata:
@@ -1569,12 +1652,14 @@ addFeatStatistics <- function(gobject,
 #'
 #' addCellStatistics(g)
 #' @export
-addCellStatistics <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    detection_threshold = 0,
-    return_gobject = TRUE) {
+addCellStatistics <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        detection_threshold = 0,
+        return_gobject = TRUE,
+        verbose = TRUE) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -1589,7 +1674,8 @@ addCellStatistics <- function(gobject,
     # expression values to be used
     expression_values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_data <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -1605,7 +1691,7 @@ addCellStatistics <- function(gobject,
         cells = colnames(expr_data[]),
         nr_feats = colSums_flex(expr_data[] > detection_threshold),
         perc_feats = (colSums_flex(expr_data[] > detection_threshold) /
-                        nrow(expr_data[])) * 100,
+            nrow(expr_data[])) * 100,
         total_expr = colSums_flex(expr_data[])
     )
 
@@ -1628,8 +1714,10 @@ addCellStatistics <- function(gobject,
 
         metadata_names <- colnames(cell_metadata[])
         if ("nr_feats" %in% metadata_names) {
-            message("cells statistics has already been applied once, will be
-                    overwritten")
+            vmsg(
+                .v = verbose, "cells statistics has already been applied",
+                "once; overwriting"
+            )
             cell_metadata[][, c("nr_feats", "perc_feats", "total_expr") := NULL]
             ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
             gobject <- set_cell_metadata(gobject,
@@ -1673,16 +1761,19 @@ addCellStatistics <- function(gobject,
         # normally
         if (is.null(cl)) {
             gobject <- update_giotto_params(gobject,
-                                            description = "_cell_stats")
+                description = "_cell_stats"
+            )
         } else {
             fname <- as.character(cl[[1]])
             if (fname == "addStatistics") {
                 gobject <- update_giotto_params(gobject,
-                                                description = "_cell_stats",
-                                                toplevel = 3)
+                    description = "_cell_stats",
+                    toplevel = 3
+                )
             } else {
                 gobject <- update_giotto_params(gobject,
-                                                description = "_cell_stats")
+                    description = "_cell_stats"
+                )
             }
         }
 
@@ -1703,6 +1794,7 @@ addCellStatistics <- function(gobject,
 #' @param expression_values expression values to use
 #' @param detection_threshold detection threshold to consider a feature detected
 #' @param return_gobject boolean: return giotto object (default = TRUE)
+#' @param verbose be verbose
 #' @returns giotto object if return_gobject = TRUE, else a list with results
 #' @details See \code{\link{addFeatStatistics}} and
 #' \code{\link{addCellStatistics}}
@@ -1711,12 +1803,14 @@ addCellStatistics <- function(gobject,
 #'
 #' addStatistics(g)
 #' @export
-addStatistics <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    detection_threshold = 0,
-    return_gobject = TRUE) {
+addStatistics <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        detection_threshold = 0,
+        return_gobject = TRUE,
+        verbose = TRUE) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -1735,7 +1829,8 @@ addStatistics <- function(gobject,
         spat_unit = spat_unit,
         expression_values = expression_values,
         detection_threshold = detection_threshold,
-        return_gobject = return_gobject
+        return_gobject = return_gobject,
+        verbose = verbose
     )
 
     if (return_gobject == TRUE) {
@@ -1749,7 +1844,8 @@ addStatistics <- function(gobject,
         spat_unit = spat_unit,
         expression_values = expression_values,
         detection_threshold = detection_threshold,
-        return_gobject = return_gobject
+        return_gobject = return_gobject,
+        verbose = verbose
     )
 
     if (return_gobject == TRUE) {
@@ -1781,13 +1877,14 @@ addStatistics <- function(gobject,
 #'
 #' addFeatsPerc(g, feats = c("Gm19935", "9630013A20Rik", "2900040C04Rik"))
 #' @export
-addFeatsPerc <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    feats = NULL,
-    vector_name = "feat_perc",
-    return_gobject = TRUE) {
+addFeatsPerc <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        feats = NULL,
+        vector_name = "feat_perc",
+        return_gobject = TRUE) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -1812,7 +1909,8 @@ addFeatsPerc <- function(gobject,
     # expression values to be used
     expression_values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_data <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -1838,7 +1936,8 @@ addFeatsPerc <- function(gobject,
 
         ## update parameters used ##
         temp_gobj <- update_giotto_params(temp_gobj,
-                                          description = "_feats_perc")
+            description = "_feats_perc"
+        )
 
         return(temp_gobj)
     } else {
@@ -1870,14 +1969,17 @@ addFeatsPerc <- function(gobject,
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #'
-#' findNetworkNeighbors(gobject = g, spatial_network_name = "spatial_network",
-#' source_cell_ids = c("AACTCGATGGCGCAGT-1", "GGCTGGCTAGCTTAAA-1"))
+#' findNetworkNeighbors(
+#'     gobject = g, spatial_network_name = "spatial_network",
+#'     source_cell_ids = c("AACTCGATGGCGCAGT-1", "GGCTGGCTAGCTTAAA-1")
+#' )
 #' @export
-findNetworkNeighbors <- function(gobject,
-    spat_unit = NULL,
-    spatial_network_name = NULL,
-    source_cell_ids = NULL,
-    name = "nb_cells") {
+findNetworkNeighbors <- function(
+        gobject,
+        spat_unit = NULL,
+        spatial_network_name = NULL,
+        source_cell_ids = NULL,
+        name = "nb_cells") {
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
         spat_unit = spat_unit
@@ -1905,11 +2007,14 @@ findNetworkNeighbors <- function(gobject,
 
     full_network_DT <- convert_to_full_spatial_network(spatial_network)
     potential_target_cells <- full_network_DT[
-        source %in% source_cells][["target"]]
+        source %in% source_cells
+    ][["target"]]
     source_and_target_cells <- potential_target_cells[
-        potential_target_cells %in% source_cells]
+        potential_target_cells %in% source_cells
+    ]
     target_cells <- potential_target_cells[
-        !potential_target_cells %in% source_and_target_cells]
+        !potential_target_cells %in% source_and_target_cells
+    ]
 
     cell_meta <- pDataDT(gobject)
 
diff --git a/R/cell_segmentation.R b/R/cell_segmentation.R
index 6c5df37ad..05ff56040 100644
--- a/R/cell_segmentation.R
+++ b/R/cell_segmentation.R
@@ -16,14 +16,15 @@
 #' of the tile: sx (start x), ex (end x), sy, and ey.
 #'
 #' @export
-doCellSegmentation <- function(raster_img,
-    folder_path,
-    reduce_resolution = 4,
-    overlapping_pixels = 50,
-    python_path = NULL) {
+doCellSegmentation <- function(
+        raster_img,
+        folder_path,
+        reduce_resolution = 4,
+        overlapping_pixels = 50,
+        python_path = NULL) {
     package_check("deepcell", repository = "pip")
     package_check("PIL", repository = "pip")
-    
+
     # prepare python path and segmentation script
     reticulate::use_python(required = TRUE, python = python_path)
     python_segmentation_function <- system.file("python",
@@ -51,10 +52,10 @@ doCellSegmentation <- function(raster_img,
     # sliding window
     start_x <- 0
     end_x <- start_x + tile_dim
-    for (i in 1:nxwindow) {
+    for (i in seq_len(nxwindow)) {
         start_y <- 0
         end_y <- start_y + tile_dim
-        for (j in 1:nywindow) {
+        for (j in seq_len(nywindow)) {
             ext_crop <- terra::ext(c(start_x, end_x, start_y, end_y))
             img_crop <- terra::crop(raster_img, ext_crop, snap = "in")
             img_crop_rescaled <- terra::aggregate(img_crop, reduce_resolution)
diff --git a/R/clustering.R b/R/clustering.R
index 625db7e35..950debf62 100644
--- a/R/clustering.R
+++ b/R/clustering.R
@@ -47,24 +47,25 @@
 #'
 #' doLeidenCluster(g)
 #' @export
-doLeidenCluster <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    name = "leiden_clus",
-    nn_network_to_use = "sNN",
-    network_name = "sNN.pca",
-    python_path = NULL,
-    resolution = 1,
-    weight_col = "weight",
-    partition_type = c(
-        "RBConfigurationVertexPartition",
-        "ModularityVertexPartition"
-    ),
-    init_membership = NULL,
-    n_iterations = 1000,
-    return_gobject = TRUE,
-    set_seed = TRUE,
-    seed_number = 1234) {
+doLeidenCluster <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        name = "leiden_clus",
+        nn_network_to_use = "sNN",
+        network_name = "sNN.pca",
+        python_path = NULL,
+        resolution = 1,
+        weight_col = "weight",
+        partition_type = c(
+            "RBConfigurationVertexPartition",
+            "ModularityVertexPartition"
+        ),
+        init_membership = NULL,
+        n_iterations = 1000,
+        return_gobject = TRUE,
+        set_seed = TRUE,
+        seed_number = 1234) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -94,7 +95,8 @@ doLeidenCluster <- function(gobject,
     ## select partition type
     partition_type <- match.arg(partition_type,
         choices = c(
-            "RBConfigurationVertexPartition", "ModularityVertexPartition")
+            "RBConfigurationVertexPartition", "ModularityVertexPartition"
+        )
     )
 
     ## check or make paths
@@ -106,19 +108,21 @@ doLeidenCluster <- function(gobject,
     ## prepare python path and louvain script
     reticulate::use_python(required = TRUE, python = python_path)
     python_leiden_function <- system.file("python", "python_leiden.py",
-                                        package = "Giotto")
+        package = "Giotto"
+    )
     reticulate::source_python(file = python_leiden_function)
 
     ## set seed
     if (isTRUE(set_seed)) {
         seed_number <- as.integer(seed_number)
     } else {
-        seed_number <- as.integer(sample(x = 1:10000, size = 1))
+        seed_number <- as.integer(sample(x = seq(10000), size = 1))
     }
 
     ## extract NN network
     network_edge_dt <- data.table::as.data.table(
-        igraph::as_data_frame(x = igraph_object, what = "edges"))
+        igraph::as_data_frame(x = igraph_object, what = "edges")
+    )
 
     # data.table variables
     weight <- NULL
@@ -130,7 +134,9 @@ doLeidenCluster <- function(gobject,
         } else {
             # weight is defined by attribute of igraph object
             network_edge_dt <- network_edge_dt[
-                , c("from", "to", weight_col), with = FALSE]
+                , c("from", "to", weight_col),
+                with = FALSE
+            ]
             data.table::setnames(network_edge_dt, weight_col, "weight")
         }
     } else {
@@ -143,8 +149,10 @@ doLeidenCluster <- function(gobject,
 
 
     ## do python leiden clustering
-    reticulate::py_set_seed(seed = seed_number,
-                            disable_hash_randomization = TRUE)
+    reticulate::py_set_seed(
+        seed = seed_number,
+        disable_hash_randomization = TRUE
+    )
     pyth_leid_result <- python_leiden(
         df = network_edge_dt,
         partition_type = partition_type,
@@ -156,7 +164,8 @@ doLeidenCluster <- function(gobject,
     )
 
     ident_clusters_DT <- data.table::data.table(
-        cell_ID = pyth_leid_result[[1]], "name" = pyth_leid_result[[2]])
+        cell_ID = pyth_leid_result[[1]], "name" = pyth_leid_result[[2]]
+    )
     data.table::setnames(ident_clusters_DT, "name", name)
 
 
@@ -252,22 +261,23 @@ doLeidenCluster <- function(gobject,
 #'
 #' doLeidenClusterIgraph(g)
 #' @export
-doLeidenClusterIgraph <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    name = "leiden_clus",
-    nn_network_to_use = "sNN",
-    network_name = "sNN.pca",
-    objective_function = c("modularity", "CPM"),
-    weights = NULL,
-    resolution_parameter = 1,
-    beta = 0.01,
-    initial_membership = NULL,
-    n_iterations = 1000,
-    return_gobject = TRUE,
-    set_seed = TRUE,
-    seed_number = 1234,
-    ...) {
+doLeidenClusterIgraph <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        name = "leiden_clus",
+        nn_network_to_use = "sNN",
+        network_name = "sNN.pca",
+        objective_function = c("modularity", "CPM"),
+        weights = NULL,
+        resolution_parameter = 1,
+        beta = 0.01,
+        initial_membership = NULL,
+        n_iterations = 1000,
+        return_gobject = TRUE,
+        set_seed = TRUE,
+        seed_number = 1234,
+        ...) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -321,7 +331,8 @@ doLeidenClusterIgraph <- function(gobject,
 
     # summarize results
     ident_clusters_DT <- data.table::data.table(
-        "cell_ID" = leiden_clusters$names, "name" = leiden_clusters$membership)
+        "cell_ID" = leiden_clusters$names, "name" = leiden_clusters$membership
+    )
     data.table::setnames(ident_clusters_DT, "name", name)
 
 
@@ -410,20 +421,23 @@ doLeidenClusterIgraph <- function(gobject,
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #'
-#' doGiottoClustree(gobject = g, res_vector = c(0.5, 0.8), return_plot = FALSE,
-#' show_plot = FALSE, save_plot = FALSE)
+#' doGiottoClustree(
+#'     gobject = g, res_vector = c(0.5, 0.8), return_plot = FALSE,
+#'     show_plot = FALSE, save_plot = FALSE
+#' )
 #' @export
-doGiottoClustree <- function(gobject,
-    res_vector = NULL,
-    res_seq = NULL,
-    return_gobject = FALSE,
-    show_plot = NULL,
-    save_plot = NULL,
-    return_plot = NULL,
-    save_param = list(),
-    default_save_name = "clustree",
-    verbose = TRUE,
-    ...) {
+doGiottoClustree <- function(
+        gobject,
+        res_vector = NULL,
+        res_seq = NULL,
+        return_gobject = FALSE,
+        show_plot = NULL,
+        save_plot = NULL,
+        return_plot = NULL,
+        save_param = list(),
+        default_save_name = "clustree",
+        verbose = TRUE,
+        ...) {
     package_check(pkg_name = "clustree", repository = "CRAN")
     ## setting resolutions to use
     if (is.null(res_vector)) {
@@ -496,20 +510,21 @@ doGiottoClustree <- function(gobject,
 #' Set \emph{weight_col = NULL} to give equal weight (=1) to each edge.
 #' @md
 #' @keywords internal
-.doLouvainCluster_community <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    name = "louvain_clus",
-    nn_network_to_use = "sNN",
-    network_name = "sNN.pca",
-    python_path = NULL,
-    resolution = 1,
-    weight_col = NULL,
-    louv_random = FALSE,
-    return_gobject = TRUE,
-    set_seed = FALSE,
-    seed_number = 1234,
-    ...) {
+.doLouvainCluster_community <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        name = "louvain_clus",
+        nn_network_to_use = "sNN",
+        network_name = "sNN.pca",
+        python_path = NULL,
+        resolution = 1,
+        weight_col = NULL,
+        louv_random = FALSE,
+        return_gobject = TRUE,
+        set_seed = FALSE,
+        seed_number = 1234,
+        ...) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -544,18 +559,21 @@ doGiottoClustree <- function(gobject,
     # prepare python path and louvain script
     reticulate::use_python(required = TRUE, python = python_path)
     python_louvain_function <- system.file(
-        "python", "python_louvain.py", package = "Giotto")
+        "python", "python_louvain.py",
+        package = "Giotto"
+    )
     reticulate::source_python(file = python_louvain_function)
 
     # set seed
     if (isTRUE(set_seed)) {
         seed_number <- as.integer(seed_number)
     } else {
-        seed_number <- as.integer(sample(x = 1:10000, size = 1))
+        seed_number <- as.integer(sample(x = seq(10000), size = 1))
     }
 
     network_edge_dt <- data.table::as.data.table(igraph::as_data_frame(
-        x = igraph_object, what = "edges"))
+        x = igraph_object, what = "edges"
+    ))
 
     # data.table variables
     weight <- NULL
@@ -566,7 +584,9 @@ doGiottoClustree <- function(gobject,
         } else {
             # weight is defined by attribute of igraph object
             network_edge_dt <- network_edge_dt[
-                , c("from", "to", weight_col), with = FALSE]
+                , c("from", "to", weight_col),
+                with = FALSE
+            ]
             setnames(network_edge_dt, weight_col, "weight")
         }
     } else {
@@ -578,19 +598,24 @@ doGiottoClustree <- function(gobject,
     # do python louvain clustering
     if (louv_random == FALSE) {
         reticulate::py_set_seed(
-            seed = seed_number, disable_hash_randomization = TRUE)
+            seed = seed_number, disable_hash_randomization = TRUE
+        )
         pyth_louv_result <- python_louvain(
-            df = network_edge_dt, resolution = resolution, randomize = FALSE)
+            df = network_edge_dt, resolution = resolution, randomize = FALSE
+        )
     } else {
         reticulate::py_set_seed(
-            seed = seed_number, disable_hash_randomization = TRUE)
+            seed = seed_number, disable_hash_randomization = TRUE
+        )
         pyth_louv_result <- python_louvain(
             df = network_edge_dt,
             resolution = resolution,
-            random_state = seed_number)
+            random_state = seed_number
+        )
     }
     ident_clusters_DT <- data.table::data.table(
-        cell_ID = rownames(pyth_louv_result), "name" = pyth_louv_result[[1]])
+        cell_ID = rownames(pyth_louv_result), "name" = pyth_louv_result[[1]]
+    )
     data.table::setnames(ident_clusters_DT, "name", name)
 
 
@@ -647,11 +672,13 @@ doGiottoClustree <- function(gobject,
             fname <- as.character(cl[[1]])
             if (fname == "doLouvainCluster") {
                 gobject <- update_giotto_params(gobject,
-                                                description = "_cluster",
-                                                toplevel = 3)
+                    description = "_cluster",
+                    toplevel = 3
+                )
             } else {
                 gobject <- update_giotto_params(gobject,
-                                                description = "_cluster")
+                    description = "_cluster"
+                )
             }
         }
 
@@ -686,17 +713,18 @@ doGiottoClustree <- function(gobject,
 #' in R for more information.
 #'
 #' @keywords internal
-.doLouvainCluster_multinet <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    name = "louvain_clus",
-    nn_network_to_use = "sNN",
-    network_name = "sNN.pca",
-    gamma = 1,
-    omega = 1,
-    return_gobject = TRUE,
-    set_seed = FALSE,
-    seed_number = 1234) {
+.doLouvainCluster_multinet <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        name = "louvain_clus",
+        nn_network_to_use = "sNN",
+        network_name = "sNN.pca",
+        gamma = 1,
+        omega = 1,
+        return_gobject = TRUE,
+        set_seed = FALSE,
+        seed_number = 1234) {
     if ("multinet" %in% rownames(installed.packages()) == FALSE) {
         stop(
             "package 'multinet' is not yet installed \n",
@@ -731,10 +759,11 @@ doGiottoClustree <- function(gobject,
 
     # create mlnetworkobject
     mln_object <- multinet::ml_empty()
-    multinet::add_vertices_ml(
-        n = mln_object, vertices = igraph::V(igraph_object))
+    # multinet::add_vertices_ml(
+    #     n = mln_object, vertices = igraph::V(igraph_object))
     multinet::add_igraph_layer_ml(
-        n = mln_object, g = igraph_object, name = name)
+        n = mln_object, g = igraph_object, name = name
+    )
 
     # start seed
     if (isTRUE(set_seed)) {
@@ -745,7 +774,8 @@ doGiottoClustree <- function(gobject,
     cell_ID <- actor <- weight_col <- NULL
 
     louvain_clusters <- multinet::glouvain_ml(
-        n = mln_object, gamma = gamma, omega = omega)
+        n = mln_object, gamma = gamma, omega = omega
+    )
     ident_clusters_DT <- data.table::as.data.table(louvain_clusters)
     ident_clusters_DT[, cell_ID := actor]
     data.table::setnames(ident_clusters_DT, "cid", name)
@@ -808,11 +838,13 @@ doGiottoClustree <- function(gobject,
             fname <- as.character(cl[[1]])
             if (fname == "doLouvainCluster") {
                 gobject <- update_giotto_params(gobject,
-                                                description = "_cluster",
-                                                toplevel = 3)
+                    description = "_cluster",
+                    toplevel = 3
+                )
             } else {
                 gobject <- update_giotto_params(gobject,
-                                                description = "_cluster")
+                    description = "_cluster"
+                )
             }
         }
         return(gobject)
@@ -861,23 +893,24 @@ doGiottoClustree <- function(gobject,
 #'
 #' doLouvainCluster(g)
 #' @export
-doLouvainCluster <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    version = c("community", "multinet"),
-    name = "louvain_clus",
-    nn_network_to_use = "sNN",
-    network_name = "sNN.pca",
-    python_path = NULL,
-    resolution = 1,
-    weight_col = NULL,
-    gamma = 1,
-    omega = 1,
-    louv_random = FALSE,
-    return_gobject = TRUE,
-    set_seed = FALSE,
-    seed_number = 1234,
-    ...) {
+doLouvainCluster <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        version = c("community", "multinet"),
+        name = "louvain_clus",
+        nn_network_to_use = "sNN",
+        network_name = "sNN.pca",
+        python_path = NULL,
+        resolution = 1,
+        weight_col = NULL,
+        gamma = 1,
+        omega = 1,
+        louv_random = FALSE,
+        return_gobject = TRUE,
+        set_seed = FALSE,
+        seed_number = 1234,
+        ...) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -957,18 +990,20 @@ doLouvainCluster <- function(gobject,
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #'
-#' doRandomWalkCluster(g)
+#' g <- doRandomWalkCluster(g)
+#' pDataDT(g)
 #' @export
-doRandomWalkCluster <- function(gobject,
-    name = "random_walk_clus",
-    nn_network_to_use = "sNN",
-    network_name = "sNN.pca",
-    walk_steps = 4,
-    walk_clusters = 10,
-    walk_weights = NA,
-    return_gobject = TRUE,
-    set_seed = FALSE,
-    seed_number = 1234) {
+doRandomWalkCluster <- function(
+        gobject,
+        name = "random_walk_clus",
+        nn_network_to_use = "sNN",
+        network_name = "sNN.pca",
+        walk_steps = 4,
+        walk_clusters = 10,
+        walk_weights = NA,
+        return_gobject = TRUE,
+        set_seed = FALSE,
+        seed_number = 1234) {
     ## get cell IDs ##
     cell_ID_vec <- gobject@cell_ID
 
@@ -976,7 +1011,8 @@ doRandomWalkCluster <- function(gobject,
     igraph_object <- getNearestNetwork(
         gobject,
         nn_type = nn_network_to_use,
-        name = network_name
+        name = network_name,
+        output = "igraph"
     )
 
 
@@ -986,13 +1022,16 @@ doRandomWalkCluster <- function(gobject,
     }
 
     randomwalk_clusters <- igraph::cluster_walktrap(
-        graph = igraph_object, steps = walk_steps, weights = walk_weights)
+        graph = igraph_object, steps = walk_steps, weights = walk_weights
+    )
     randomwalk_clusters <- as.factor(igraph::cut_at(
-        communities = randomwalk_clusters, no = walk_clusters))
+        communities = randomwalk_clusters, no = walk_clusters
+    ))
 
     ident_clusters_DT <- data.table::data.table(
         "cell_ID" = igraph::V(igraph_object)$name,
-        "name" = randomwalk_clusters)
+        "name" = randomwalk_clusters
+    )
     data.table::setnames(ident_clusters_DT, "name", name)
 
     # exit seed
@@ -1000,21 +1039,13 @@ doRandomWalkCluster <- function(gobject,
         set.seed(Sys.time())
     }
 
-
     ## return
     if (return_gobject == TRUE) {
-        cluster_names <- names(gobject@cell_metadata)
-        if (name %in% cluster_names) {
-            cat(name, " has already been used, will be overwritten")
-            cell_metadata <- gobject@cell_metadata
-            cell_metadata[, eval(name) := NULL]
-            gobject@cell_metadata <- cell_metadata
-        }
-
         gobject <- addCellMetadata(
             gobject = gobject,
             new_metadata = ident_clusters_DT[, c("cell_ID", name),
-                                            with = FALSE],
+                with = FALSE
+            ],
             by_column = TRUE,
             column_cell_ID = "cell_ID"
         )
@@ -1022,7 +1053,8 @@ doRandomWalkCluster <- function(gobject,
 
         ## update parameters used ##
         gobject <- update_giotto_params(gobject,
-                                        description = "_randomwalk_cluster")
+            description = "_randomwalk_cluster"
+        )
         return(gobject)
     } else {
         # else return clustering result
@@ -1058,17 +1090,18 @@ doRandomWalkCluster <- function(gobject,
 #'
 #' doSNNCluster(g)
 #' @export
-doSNNCluster <- function(gobject,
-    name = "sNN_clus",
-    nn_network_to_use = "kNN",
-    network_name = "kNN.pca",
-    k = 20,
-    eps = 4,
-    minPts = 16,
-    borderPoints = TRUE,
-    return_gobject = TRUE,
-    set_seed = FALSE,
-    seed_number = 1234) {
+doSNNCluster <- function(
+        gobject,
+        name = "sNN_clus",
+        nn_network_to_use = "kNN",
+        network_name = "kNN.pca",
+        k = 20,
+        eps = 4,
+        minPts = 16,
+        borderPoints = TRUE,
+        return_gobject = TRUE,
+        set_seed = FALSE,
+        seed_number = 1234) {
     ## get cell IDs ##
     cell_ID_vec <- gobject@cell_ID
 
@@ -1096,18 +1129,24 @@ doSNNCluster <- function(gobject,
 
     ## SNN clust
     igraph_DT <- data.table::as.data.table(igraph::as_data_frame(
-        igraph_object, what = "edges"))
+        igraph_object,
+        what = "edges"
+    ))
     igraph_DT <- igraph_DT[order(from)]
 
     cell_id_numeric <- unique(x = c(igraph_DT$from, igraph_DT$to))
     names(cell_id_numeric) <- seq_along(cell_id_numeric)
     igraph_DT[, from_T := as.numeric(names(cell_id_numeric[
-        cell_id_numeric == from])), by = 1:nrow(igraph_DT)]
+        cell_id_numeric == from
+    ])), by = 1:nrow(igraph_DT)]
     igraph_DT[, to_T := as.numeric(names(cell_id_numeric[
-        cell_id_numeric == to])), by = 1:nrow(igraph_DT)]
+        cell_id_numeric == to
+    ])), by = 1:nrow(igraph_DT)]
     temp_igraph_DT <- igraph_DT[, .(from_T, to_T, weight, distance)]
     data.table::setnames(
-        temp_igraph_DT, old = c("from_T", "to_T"), new = c("from", "to"))
+        temp_igraph_DT,
+        old = c("from_T", "to_T"), new = c("from", "to")
+    )
 
     kNN_object <- nnDT_to_kNN(nnDT = temp_igraph_DT)
     sNN_clusters <- dbscan::sNNclust(
@@ -1116,8 +1155,9 @@ doSNNCluster <- function(gobject,
     )
 
     ident_clusters_DT <- data.table::data.table(
-        "cell_ID" = cell_id_numeric[1:nrow(kNN_object$dist)],
-        "name" = sNN_clusters$cluster)
+        "cell_ID" = cell_id_numeric[seq_len(nrow(kNN_object$dist))],
+        "name" = sNN_clusters$cluster
+    )
     data.table::setnames(ident_clusters_DT, "name", name)
 
     # exit seed
@@ -1138,7 +1178,8 @@ doSNNCluster <- function(gobject,
         gobject <- addCellMetadata(
             gobject = gobject,
             new_metadata = ident_clusters_DT[, c("cell_ID", name),
-                                            with = FALSE],
+                with = FALSE
+            ],
             by_column = TRUE,
             column_cell_ID = "cell_ID"
         )
@@ -1189,27 +1230,28 @@ doSNNCluster <- function(gobject,
 #'
 #' doKmeans(g)
 #' @export
-doKmeans <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    feats_to_use = NULL,
-    dim_reduction_to_use = c("cells", "pca", "umap", "tsne"),
-    dim_reduction_name = "pca",
-    dimensions_to_use = 1:10,
-    distance_method = c(
-        "original", "pearson", "spearman",
-        "euclidean", "maximum", "manhattan",
-        "canberra", "binary", "minkowski"
-    ),
-    centers = 10,
-    iter_max = 100,
-    nstart = 1000,
-    algorithm = "Hartigan-Wong",
-    name = "kmeans",
-    return_gobject = TRUE,
-    set_seed = TRUE,
-    seed_number = 1234) {
+doKmeans <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        feats_to_use = NULL,
+        dim_reduction_to_use = c("cells", "pca", "umap", "tsne"),
+        dim_reduction_name = "pca",
+        dimensions_to_use = 1:10,
+        distance_method = c(
+            "original", "pearson", "spearman",
+            "euclidean", "maximum", "manhattan",
+            "canberra", "binary", "minkowski"
+        ),
+        centers = 10,
+        iter_max = 100,
+        nstart = 1000,
+        algorithm = "Hartigan-Wong",
+        name = "kmeans",
+        return_gobject = TRUE,
+        set_seed = TRUE,
+        seed_number = 1234) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -1223,7 +1265,9 @@ doKmeans <- function(gobject,
 
 
     dim_reduction_to_use <- match.arg(
-        dim_reduction_to_use, choices = c("cells", "pca", "umap", "tsne"))
+        dim_reduction_to_use,
+        choices = c("cells", "pca", "umap", "tsne")
+    )
     distance_method <- match.arg(distance_method, choices = c(
         "original", "pearson", "spearman",
         "euclidean", "maximum", "manhattan",
@@ -1247,12 +1291,14 @@ doKmeans <- function(gobject,
         )
 
         dimensions_to_use <- dimensions_to_use[
-            dimensions_to_use %in% 1:ncol(dim_coord[])]
+            dimensions_to_use %in% seq_len(ncol(dim_coord[]))
+        ]
         matrix_to_use <- dim_coord[][, dimensions_to_use]
     } else {
         values <- match.arg(
             expression_values,
-            unique(c("normalized", "scaled", "custom", expression_values)))
+            unique(c("normalized", "scaled", "custom", expression_values))
+        )
 
         ## using original matrix ##
         expr_values <- getExpression(
@@ -1266,7 +1312,8 @@ doKmeans <- function(gobject,
         # subset expression matrix
         if (!is.null(feats_to_use)) {
             expr_values[] <- expr_values[][
-                rownames(expr_values[]) %in% feats_to_use, ]
+                rownames(expr_values[]) %in% feats_to_use,
+            ]
         }
 
         # features as columns
@@ -1280,7 +1327,8 @@ doKmeans <- function(gobject,
         celldist <- matrix_to_use
     } else if (distance_method %in% c("spearman", "pearson")) {
         celldist <- stats::as.dist(1 - cor_flex(
-            x = t_flex(matrix_to_use), method = distance_method))
+            x = t_flex(matrix_to_use), method = distance_method
+        ))
     } else if (distance_method %in% c(
         "euclidean", "maximum", "manhattan",
         "canberra", "binary", "minkowski"
@@ -1317,7 +1365,6 @@ doKmeans <- function(gobject,
 
     ## add clusters to metadata ##
     if (isTRUE(return_gobject)) {
-
         cluster_names <- names(pDataDT(
             gobject = gobject,
             spat_unit = spat_unit,
@@ -1357,7 +1404,8 @@ doKmeans <- function(gobject,
 
         ## update parameters used ##
         gobject <- update_giotto_params(gobject,
-                                        description = "_kmeans_cluster")
+            description = "_kmeans_cluster"
+        )
         return(gobject)
     } else {
         return(ident_clusters_DT)
@@ -1395,30 +1443,31 @@ doKmeans <- function(gobject,
 #'
 #' doHclust(g)
 #' @export
-doHclust <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    feats_to_use = NULL,
-    dim_reduction_to_use = c("cells", "pca", "umap", "tsne"),
-    dim_reduction_name = "pca",
-    dimensions_to_use = 1:10,
-    distance_method = c(
-        "pearson", "spearman", "original",
-        "euclidean", "maximum", "manhattan",
-        "canberra", "binary", "minkowski"
-    ),
-    agglomeration_method = c(
-        "ward.D2", "ward.D", "single",
-        "complete", "average", "mcquitty",
-        "median", "centroid"
-    ),
-    k = 10,
-    h = NULL,
-    name = "hclust",
-    return_gobject = TRUE,
-    set_seed = TRUE,
-    seed_number = 1234) {
+doHclust <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        feats_to_use = NULL,
+        dim_reduction_to_use = c("cells", "pca", "umap", "tsne"),
+        dim_reduction_name = "pca",
+        dimensions_to_use = 1:10,
+        distance_method = c(
+            "pearson", "spearman", "original",
+            "euclidean", "maximum", "manhattan",
+            "canberra", "binary", "minkowski"
+        ),
+        agglomeration_method = c(
+            "ward.D2", "ward.D", "single",
+            "complete", "average", "mcquitty",
+            "median", "centroid"
+        ),
+        k = 10,
+        h = NULL,
+        name = "hclust",
+        return_gobject = TRUE,
+        set_seed = TRUE,
+        seed_number = 1234) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -1470,7 +1519,8 @@ doHclust <- function(gobject,
         )
 
         dimensions_to_use <- dimensions_to_use[
-            dimensions_to_use %in% 1:ncol(dim_coord)]
+            dimensions_to_use %in% seq_len(ncol(dim_coord))
+        ]
         matrix_to_use <- dim_coord[, dimensions_to_use]
     } else {
         ## using original matrix ##
@@ -1485,7 +1535,8 @@ doHclust <- function(gobject,
         # subset expression matrix
         if (!is.null(feats_to_use)) {
             expr_values <- expr_values[
-                rownames(expr_values) %in% feats_to_use, ]
+                rownames(expr_values) %in% feats_to_use,
+            ]
         }
 
         # features as columns
@@ -1498,7 +1549,8 @@ doHclust <- function(gobject,
         celldist <- matrix_to_use
     } else if (distance_method %in% c("spearman", "pearson")) {
         celldist <- stats::as.dist(1 - cor_flex(x = t_flex(
-            matrix_to_use), method = distance_method))
+            matrix_to_use
+        ), method = distance_method))
     } else if (distance_method %in% c(
         "euclidean", "maximum", "manhattan",
         "canberra", "binary", "minkowski"
@@ -1569,7 +1621,8 @@ doHclust <- function(gobject,
 
         ## update parameters used ##
         gobject <- update_giotto_params(gobject,
-                                        description = "_hierarchical_cluster")
+            description = "_hierarchical_cluster"
+        )
         return(gobject)
     } else {
         return(list("hclust" = hclusters, "DT" = ident_clusters_DT))
@@ -1635,59 +1688,62 @@ doHclust <- function(gobject,
 #'
 #' clusterCells(g)
 #' @export
-clusterCells <- function(gobject,
-    cluster_method = c(
-        "leiden",
-        "louvain_community", "louvain_multinet",
-        "randomwalk", "sNNclust",
-        "kmeans", "hierarchical"
-    ),
-    name = "cluster_name",
-    nn_network_to_use = "sNN",
-    network_name = "sNN.pca",
-    pyth_leid_resolution = 1,
-    pyth_leid_weight_col = "weight",
-    pyth_leid_part_type = c("RBConfigurationVertexPartition",
-                            "ModularityVertexPartition"),
-    pyth_leid_init_memb = NULL,
-    pyth_leid_iterations = 1000,
-    pyth_louv_resolution = 1,
-    pyth_louv_weight_col = NULL,
-    python_louv_random = FALSE,
-    python_path = NULL,
-    louvain_gamma = 1,
-    louvain_omega = 1,
-    walk_steps = 4,
-    walk_clusters = 10,
-    walk_weights = NA,
-    sNNclust_k = 20,
-    sNNclust_eps = 4,
-    sNNclust_minPts = 16,
-    borderPoints = TRUE,
-    expression_values = c("normalized", "scaled", "custom"),
-    feats_to_use = NULL,
-    dim_reduction_to_use = c("cells", "pca", "umap", "tsne"),
-    dim_reduction_name = "pca",
-    dimensions_to_use = 1:10,
-    distance_method = c(
-        "original", "pearson", "spearman",
-        "euclidean", "maximum", "manhattan",
-        "canberra", "binary", "minkowski"
-    ),
-    km_centers = 10,
-    km_iter_max = 100,
-    km_nstart = 1000,
-    km_algorithm = "Hartigan-Wong",
-    hc_agglomeration_method = c(
-        "ward.D2", "ward.D", "single",
-        "complete", "average", "mcquitty",
-        "median", "centroid"
-    ),
-    hc_k = 10,
-    hc_h = NULL,
-    return_gobject = TRUE,
-    set_seed = TRUE,
-    seed_number = 1234) {
+clusterCells <- function(
+        gobject,
+        cluster_method = c(
+            "leiden",
+            "louvain_community", "louvain_multinet",
+            "randomwalk", "sNNclust",
+            "kmeans", "hierarchical"
+        ),
+        name = "cluster_name",
+        nn_network_to_use = "sNN",
+        network_name = "sNN.pca",
+        pyth_leid_resolution = 1,
+        pyth_leid_weight_col = "weight",
+        pyth_leid_part_type = c(
+            "RBConfigurationVertexPartition",
+            "ModularityVertexPartition"
+        ),
+        pyth_leid_init_memb = NULL,
+        pyth_leid_iterations = 1000,
+        pyth_louv_resolution = 1,
+        pyth_louv_weight_col = NULL,
+        python_louv_random = FALSE,
+        python_path = NULL,
+        louvain_gamma = 1,
+        louvain_omega = 1,
+        walk_steps = 4,
+        walk_clusters = 10,
+        walk_weights = NA,
+        sNNclust_k = 20,
+        sNNclust_eps = 4,
+        sNNclust_minPts = 16,
+        borderPoints = TRUE,
+        expression_values = c("normalized", "scaled", "custom"),
+        feats_to_use = NULL,
+        dim_reduction_to_use = c("cells", "pca", "umap", "tsne"),
+        dim_reduction_name = "pca",
+        dimensions_to_use = 1:10,
+        distance_method = c(
+            "original", "pearson", "spearman",
+            "euclidean", "maximum", "manhattan",
+            "canberra", "binary", "minkowski"
+        ),
+        km_centers = 10,
+        km_iter_max = 100,
+        km_nstart = 1000,
+        km_algorithm = "Hartigan-Wong",
+        hc_agglomeration_method = c(
+            "ward.D2", "ward.D", "single",
+            "complete", "average", "mcquitty",
+            "median", "centroid"
+        ),
+        hc_k = 10,
+        hc_h = NULL,
+        return_gobject = TRUE,
+        set_seed = TRUE,
+        seed_number = 1234) {
     ## select cluster method
     cluster_method <- match.arg(
         arg = cluster_method,
@@ -1816,113 +1872,250 @@ clusterCells <- function(gobject,
 
 
 
+# subclustering ####
 
 
-#' @title doLeidenSubCluster
-#' @name doLeidenSubCluster
-#' @description Further subcluster cells using a NN-network and the Leiden
-#' algorithm
-#' @param gobject giotto object
-#' @param feat_type feature type
+#' @title Cell subclustering
+#' @name subClusterCells
+#' @description Perform cell subclustering by taking an annotated group of
+#' cells and performing another round of clustering on just that subset.
+#' Several methods are implemented. `subClusterCells()` is the main wrapper
+#' function. `doLeidenSubCluster()` and `doLouvainSubCluster()` are more
+#' specific implementations.
+#' @param gobject `giotto` object
 #' @param name name for new clustering result
+#' @param cluster_method clustering method to use. Currently one of "leiden"
+#' (default), "louvain_community", "louvain_multinet"
 #' @param cluster_column cluster column to subcluster
 #' @param selected_clusters only do subclustering on these clusters
-#' @param hvf_param parameters for calculateHVf
-#' @param hvg_param deprecatd, use hvf_param
+#' @param hvf_param list of parameters for [calculateHVF()]
+#' @param hvg_param deprecated
 #' @param hvf_min_perc_cells threshold for detection in min percentage of cells
-#' @param hvg_min_perc_cells deprecated, use hvf_min_perc_cells
+#' @param hvg_min_perc_cells deprecated
 #' @param hvf_mean_expr_det threshold for mean expression level in cells with
 #' detection
-#' @param hvg_mean_expr_det deprecated, use hvf_mean_expr_det
+#' @param hvg_mean_expr_det deprecated
 #' @param use_all_feats_as_hvf forces all features to be HVF and to be used as
 #' input for PCA
-#' @param use_all_genes_as_hvg deprecated, use use_all_feats_as_hvf
+#' @param use_all_genes_as_hvg deprecated
 #' @param min_nr_of_hvf minimum number of HVF, or all features will be used as
 #' input for PCA
-#' @param min_nr_of_hvg deprecated, use min_nr_of_hvf
-#' @param pca_param parameters for runPCA
-#' @param nn_param parameters for parameters for createNearestNetwork
-#' @param k_neighbors number of k for createNearestNetwork
-#' @param resolution resolution of Leiden clustering
-#' @param n_iterations number of interations to run the Leiden algorithm.
+#' @param min_nr_of_hvg deprecated
+#' @param pca_param list of parameters for [runPCA()]
+#' @param nn_param list of parameters for [createNearestNetwork()]
+#' @param k_neighbors number of k for [createNearestNetwork()]
+#' @param resolution resolution for community algorithm
+#' @param n_iterations number of iterations to run the Leiden algorithm.
+#' @param gamma gamma
+#' @param omega omega
 #' @param python_path specify specific path to python if required
 #' @param nn_network_to_use type of NN network to use (kNN vs sNN)
 #' @param network_name name of NN network to use
-#' @param return_gobject boolean: return giotto object (default = TRUE)
+#' @param return_gobject logical. return `giotto` object (default = TRUE)
 #' @param verbose verbose
-#' @returns giotto object with new subclusters appended to cell metadata
-#' @details This function performs subclustering using the Leiden algorithm on
-#' selected clusters.
+#' @returns `giotto` object with new subclusters appended to cell metadata
+#' @details This function performs subclustering on selected clusters.
 #' The systematic steps are:
-#' \itemize{
-#'   \item{1. subset Giotto object}
-#'   \item{2. identify highly variable fetures}
-#'   \item{3. run PCA}
-#'   \item{4. create nearest neighbouring network}
-#'   \item{5. do Leiden clustering}
-#' }
-#' @seealso \code{\link{doLeidenCluster}}
+#'   1. subset Giotto object
+#'   2. identify highly variable genes
+#'   3. run PCA
+#'   4. create nearest neighbouring network
+#'   5. do clustering
+#'
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #'
+#' # Run some subclusterings based on "leiden_clus" annotations that already
+#' # exist in the visium mini object
+#'
+#' # default method is leiden subclustering
+#' subClusterCells(g, cluster_column = "leiden_clus")
+#'
+#' # use louvain instead
+#' subClusterCells(g,
+#'     cluster_column = "leiden_clus",
+#'     cluster_method = "louvain_community"
+#' )
+#'
+#' # directly call the more specific functions
 #' doLeidenSubCluster(g, cluster_column = "leiden_clus")
+#'
+#' doLouvainSubCluster(g, cluster_column = "leiden_clus")
+#' @md
+NULL
+
+
+
+
+#' @rdname subClusterCells
 #' @export
-doLeidenSubCluster <- function(gobject,
-    feat_type = NULL,
-    name = "sub_pleiden_clus",
+subClusterCells <- function(gobject,
+    name = "sub_clus",
+    cluster_method = c(
+        "leiden",
+        "louvain_community",
+        "louvain_multinet"
+    ),
     cluster_column = NULL,
     selected_clusters = NULL,
-    hvf_param = list(reverse_log_scale = TRUE, difference_in_cov = 1,
-                    expression_values = "normalized"),
-    hvg_param = NULL,
+    hvg_param = deprecated(),
+    hvf_param = list(
+        reverse_log_scale = TRUE, difference_in_cov = 1,
+        expression_values = "normalized"
+    ),
+    hvg_min_perc_cells = deprecated(),
     hvf_min_perc_cells = 5,
-    hvg_min_perc_cells = NULL,
+    hvg_mean_expr_det = deprecated(),
     hvf_mean_expr_det = 1,
-    hvg_mean_expr_det = NULL,
+    use_all_genes_as_hvg = deprecated(),
     use_all_feats_as_hvf = FALSE,
-    use_all_genes_as_hvg = NULL,
+    min_nr_of_hvg = deprecated(),
     min_nr_of_hvf = 5,
-    min_nr_of_hvg = NULL,
     pca_param = list(expression_values = "normalized", scale_unit = TRUE),
     nn_param = list(dimensions_to_use = 1:20),
     k_neighbors = 10,
-    resolution = 0.5,
-    n_iterations = 500,
+    resolution = 1,
+    n_iterations = 1000,
+    gamma = 1,
+    omega = 1,
     python_path = NULL,
     nn_network_to_use = "sNN",
     network_name = "sNN.pca",
     return_gobject = TRUE,
     verbose = TRUE) {
+    ## select cluster method
+    cluster_method <- match.arg(arg = cluster_method, choices = c(
+        "leiden",
+        "louvain_community",
+        "louvain_multinet"
+    ))
+
+    # deprecations
+    .dep_param <- function(...) {
+        GiottoUtils::deprecate_param(
+            ...,
+            fun = "subClusterCells", when = "4.0.9"
+        )
+    }
+
+    hvf_param <- .dep_param(hvg_param, hvf_param)
+    hvf_min_perc_cells <- .dep_param(hvg_min_perc_cells, hvf_min_perc_cells)
+    hvf_mean_expr_det <- .dep_param(hvg_mean_expr_det, hvf_mean_expr_det)
+    use_all_feats_as_hvf <- .dep_param(use_all_genes_as_hvg, use_all_feats_as_hvf)
+    min_nr_of_hvf <- .dep_param(min_nr_of_hvg, min_nr_of_hvf)
+
+    # gather common args
+    common_args <- get_args_list(keep = c(
+        "gobject",
+        "cluster_column",
+        "selected_clusters",
+        "hvf_param",
+        "hvf_min_perc_cells",
+        "hvf_mean_expr_det",
+        "use_all_feats_as_hvf",
+        "min_nr_of_hvf",
+        "pca_param",
+        "nn_param",
+        "k_neighbors",
+        "nn_network_to_use",
+        "network_name",
+        "name",
+        "return_gobject",
+        "verbose"
+    ))
+
+    result <- switch(cluster_method,
+        "leiden" = {
+            do.call(doLeidenSubCluster, args = c(
+                common_args,
+                list(
+                    resolution = resolution,
+                    n_iterations = n_iterations,
+                    python_path = python_path,
+                    toplevel = 4
+                )
+            ))
+        },
+        "louvain_community" = {
+            do.call(.doLouvainSubCluster_community, args = c(
+                common_args,
+                list(
+                    resolution = resolution,
+                    python_path = python_path
+                )
+            ))
+        },
+        "louvain_multinet" = {
+            do.call(.doLouvainSubCluster_multinet, args = c(
+                common_args,
+                list(
+                    gamma = gamma,
+                    omega = omega
+                )
+            ))
+        }
+    )
+
+    return(result)
+}
+
+
+
+
+
+#' @describeIn subClusterCells Further subcluster cells using a NN-network and
+#' the Leiden algorithm
+#' @param toplevel do not use
+#' @param feat_type feature type
+#' @export
+doLeidenSubCluster <- function(
+        gobject,
+        feat_type = NULL,
+        name = "sub_leiden_clus",
+        cluster_column = NULL,
+        selected_clusters = NULL,
+        hvf_param = list(
+            reverse_log_scale = TRUE, difference_in_cov = 1,
+            expression_values = "normalized"
+        ),
+        hvg_param = deprecated(),
+        hvf_min_perc_cells = 5,
+        hvg_min_perc_cells = deprecated(),
+        hvf_mean_expr_det = 1,
+        hvg_mean_expr_det = deprecated(),
+        use_all_feats_as_hvf = FALSE,
+        use_all_genes_as_hvg = deprecated(),
+        min_nr_of_hvf = 5,
+        min_nr_of_hvg = deprecated(),
+        pca_param = list(expression_values = "normalized", scale_unit = TRUE),
+        nn_param = list(dimensions_to_use = 1:20),
+        k_neighbors = 10,
+        resolution = 0.5,
+        n_iterations = 500,
+        python_path = NULL,
+        nn_network_to_use = "sNN",
+        network_name = "sNN.pca",
+        return_gobject = TRUE,
+        toplevel = 2,
+        verbose = TRUE) {
     # specify feat_type
     if (is.null(feat_type)) {
         feat_type <- gobject@expression_feat[[1]]
     }
 
-    ## deprecated arguments
-    if (!is.null(hvg_param)) {
-        hvf_param <- hvg_param
-        warning("hvg_param is deprecated, use hvf_param in the future")
-    }
-    if (!is.null(hvg_min_perc_cells)) {
-        hvf_min_perc_cells <- hvg_min_perc_cells
-        warning("hvg_min_perc_cells is deprecated, use hvf_min_perc_cells in
-                the future")
-    }
-    if (!is.null(hvg_mean_expr_det)) {
-        hvf_mean_expr_det <- hvg_mean_expr_det
-        warning("hvg_mean_expr_det is deprecated, use hvf_mean_expr_det in the
-                future")
-    }
-    if (!is.null(use_all_genes_as_hvg)) {
-        use_all_feats_as_hvf <- use_all_genes_as_hvg
-        warning("use_all_genes_as_hvg is deprecated, use use_all_feats_as_hvf
-                in the future")
-    }
-    if (!is.null(min_nr_of_hvg)) {
-        min_nr_of_hvf <- min_nr_of_hvg
-        warning("min_nr_of_hvg is deprecated, use min_nr_of_hvf in the future")
+    # deprecated arguments
+    .dep_param <- function(x, y) {
+        GiottoUtils::deprecate_param(
+            x, y,
+            fun = "doLeidenSubCluster", when = "4.0.9"
+        )
     }
 
+    hvf_param <- .dep_param(hvg_param, hvf_param)
+    hvf_min_perc_cells <- .dep_param(hvg_min_perc_cells, hvf_min_perc_cells)
+    hvf_mean_expr_det <- .dep_param(hvg_mean_expr_det, hvf_mean_expr_det)
+    use_all_feats_as_hvf <- .dep_param(use_all_genes_as_hvg, use_all_feats_as_hvf)
+    min_nr_of_hvf <- .dep_param(min_nr_of_hvg, min_nr_of_hvf)
 
 
     iter_list <- list()
@@ -1943,11 +2136,12 @@ doLeidenSubCluster <- function(gobject,
 
 
     for (cluster in unique_clusters) {
-        if (verbose == TRUE) cat("start with cluster: ", cluster)
+        vmsg(.v = verbose, "start with cluster: ", cluster, "\n")
 
         ## get subset
         subset_cell_IDs <- cell_metadata[
-            get(cluster_column) == cluster][["cell_ID"]]
+            get(cluster_column) == cluster
+        ][["cell_ID"]]
         temp_giotto <- subsetGiotto(
             gobject = gobject,
             feat_type = feat_type,
@@ -1959,7 +2153,8 @@ doLeidenSubCluster <- function(gobject,
             temp_cluster <- data.table(
                 "cell_ID" = subset_cell_IDs,
                 "tempclus" = 1,
-                "parent_cluster" = cluster)
+                "parent_cluster" = cluster
+            )
             iter_list[[cluster]] <- temp_cluster
         } else {
             # continue for selected clusters or all clusters if there is no
@@ -1968,45 +2163,58 @@ doLeidenSubCluster <- function(gobject,
             ## calculate stats
             temp_giotto <- addStatistics(
                 gobject = temp_giotto,
-                feat_type = feat_type
+                feat_type = feat_type,
+                verbose = FALSE
             )
 
             ## calculate variable feats
+            hvf_param$verbose <- FALSE
             temp_giotto <- do.call(
-                "calculateHVF", c(gobject = temp_giotto, hvf_param))
+                "calculateHVF", c(gobject = temp_giotto, hvf_param)
+            )
 
             ## get hvg
             feat_metadata <- fDataDT(temp_giotto,
                 feat_type = feat_type
             )
-            featfeats <- feat_metadata[
+            usefeats <- feat_metadata[
                 hvf == "yes" & perc_cells >= hvf_min_perc_cells &
-                    mean_expr_det >= hvf_mean_expr_det]$feat_ID
+                    mean_expr_det >= hvf_mean_expr_det
+            ]$feat_ID
 
             ## catch too low number of hvg
             if (use_all_feats_as_hvf == TRUE) {
-                featfeats == feat_metadata$feat_ID
+                usefeats == feat_metadata$feat_ID
             } else {
-                if (verbose == TRUE)
-                    cat(length(featfeats),
-                        "highly variable feats have been selected")
-                if (length(featfeats) <= min_nr_of_hvf) {
+                if (verbose == TRUE) {
+                    cat(
+                        length(usefeats),
+                        "highly variable feats have been selected\n"
+                    )
+                }
+                if (length(usefeats) <= min_nr_of_hvf) {
                     message("too few feats, will continue with all feats
                             instead")
-                    featfeats <- feat_metadata$feat_ID
+                    usefeats <- feat_metadata$feat_ID
                 }
             }
 
             ## run PCA
+            pca_param$verbose <- FALSE
             temp_giotto <- do.call(
                 "runPCA",
-                c(gobject = temp_giotto, feats_to_use = list(featfeats),
-                pca_param))
+                c(
+                    gobject = temp_giotto, feats_to_use = list(usefeats),
+                    pca_param
+                )
+            )
 
             ## nearest neighbor and clustering
+            nn_param$verbose <- FALSE
             temp_giotto <- do.call(
                 "createNearestNetwork",
-                c(gobject = temp_giotto, k = k_neighbors, nn_param))
+                c(gobject = temp_giotto, k = k_neighbors, nn_param)
+            )
 
             ## Leiden Cluster
             ## TO DO: expand to all clustering options
@@ -2049,7 +2257,10 @@ doLeidenSubCluster <- function(gobject,
         )
 
         ## update parameters used ##
-        gobject <- update_giotto_params(gobject, description = "_sub_cluster")
+        gobject <- update_giotto_params(
+            gobject,
+            description = "_sub_cluster", toplevel = toplevel
+        )
         return(gobject)
     } else {
         return(together)
@@ -2057,63 +2268,31 @@ doLeidenSubCluster <- function(gobject,
 }
 
 
-#' @title doLouvainSubCluster community
-#' @name .doLouvainSubCluster_community
-#' @description subcluster cells using a NN-network and the Louvain community
-#' detection algorithm
-#' @param gobject giotto object
-#' @param name name for new clustering result
-#' @param cluster_column cluster column to subcluster
-#' @param selected_clusters only do subclustering on these clusters
-#' @param hvg_param parameters for calculateHVG
-#' @param hvg_min_perc_cells threshold for detection in min percentage of cells
-#' @param hvg_mean_expr_det threshold for mean expression level in cells with
-#' detection
-#' @param use_all_genes_as_hvg forces all genes to be HVG and to be used as
-#' input for PCA
-#' @param min_nr_of_hvg minimum number of HVG, or all genes will be used as
-#' input for PCA
-#' @param pca_param parameters for runPCA
-#' @param nn_param parameters for parameters for createNearestNetwork
-#' @param k_neighbors number of k for createNearestNetwork
-#' @param resolution resolution
-#' @param python_path specify specific path to python if required
-#' @param nn_network_to_use type of NN network to use (kNN vs sNN)
-#' @param network_name name of NN network to use
-#' @param return_gobject Boolean: return giotto object (default = TRUE)
-#' @param verbose verbose
-#' @returns giotto object with new subclusters appended to cell metadata
-#' @details This function performs subclustering using the Louvain community
-#' algorithm on selected clusters.
-#' The systematic steps are:
-#' \itemize{
-#'   \item{1. subset Giotto object}
-#'   \item{2. identify highly variable genes}
-#'   \item{3. run PCA}
-#'   \item{4. create nearest neighbouring network}
-#'   \item{5. do Louvain community clustering}
-#' }
-#' @seealso \code{\link{.doLouvainCluster_community}}
-#' @keywords internal
-.doLouvainSubCluster_community <- function(gobject,
-    name = "sub_louvain_comm_clus",
-    cluster_column = NULL,
-    selected_clusters = NULL,
-    hvg_param = list(reverse_log_scale = TRUE, difference_in_cov = 1,
-                    expression_values = "normalized"),
-    hvg_min_perc_cells = 5,
-    hvg_mean_expr_det = 1,
-    use_all_genes_as_hvg = FALSE,
-    min_nr_of_hvg = 5,
-    pca_param = list(expression_values = "normalized", scale_unit = TRUE),
-    nn_param = list(dimensions_to_use = 1:20),
-    k_neighbors = 10,
-    resolution = 0.5,
-    python_path = NULL,
-    nn_network_to_use = "sNN",
-    network_name = "sNN.pca",
-    return_gobject = TRUE,
-    verbose = TRUE) {
+# subcluster cells using a NN-network and the Louvain community
+# detection algorithm
+.doLouvainSubCluster_community <- function(
+        gobject,
+        name = "sub_louvain_comm_clus",
+        cluster_column = NULL,
+        selected_clusters = NULL,
+        hvf_param = list(
+            reverse_log_scale = TRUE,
+            difference_in_cov = 1,
+            expression_values = "normalized"
+        ),
+        hvf_min_perc_cells = 5,
+        hvf_mean_expr_det = 1,
+        use_all_feats_as_hvf = FALSE,
+        min_nr_of_hvf = 5,
+        pca_param = list(expression_values = "normalized", scale_unit = TRUE),
+        nn_param = list(dimensions_to_use = 1:20),
+        k_neighbors = 10,
+        resolution = 0.5,
+        python_path = NULL,
+        nn_network_to_use = "sNN",
+        network_name = "sNN.pca",
+        return_gobject = TRUE,
+        verbose = TRUE) {
     iter_list <- list()
 
     cell_metadata <- pDataDT(gobject)
@@ -2127,68 +2306,86 @@ doLeidenSubCluster <- function(gobject,
     index_offset <- ifelse(0 %in% unique_clusters, 1, 0)
 
     for (cluster in unique_clusters) {
-        if (verbose == TRUE) cat("start with cluster: ", cluster)
+        if (verbose == TRUE) cat("start with cluster: ", cluster, "\n")
 
         ## get subset
         subset_cell_IDs <- cell_metadata[
-            get(cluster_column) == cluster][["cell_ID"]]
-        temp_giotto <- subsetGiotto(gobject = gobject,
-                                    cell_ids = subset_cell_IDs)
+            get(cluster_column) == cluster
+        ][["cell_ID"]]
+        temp_giotto <- subsetGiotto(
+            gobject = gobject,
+            cell_ids = subset_cell_IDs
+        )
 
         ## if cluster is not selected
         if (!is.null(selected_clusters) & !cluster %in% selected_clusters) {
             temp_cluster <- data.table(
                 "cell_ID" = subset_cell_IDs,
                 "tempclus" = 1,
-                "parent_cluster" = cluster)
+                "parent_cluster" = cluster
+            )
             iter_list[[cluster + index_offset]] <- temp_cluster
         } else {
             # continue for selected clusters or all clusters if there is no
             # selection
 
             ## calculate stats
-            temp_giotto <- addStatistics(gobject = temp_giotto)
+            temp_giotto <- addStatistics(
+                gobject = temp_giotto, verbose = FALSE
+            )
 
             ## calculate variable genes
+            hvf_param$verbose <- FALSE
             temp_giotto <- do.call(
-                "calculateHVG", c(gobject = temp_giotto, hvg_param))
+                "calculateHVF", c(gobject = temp_giotto, hvf_param)
+            )
 
-            ## get hvg
-            gene_metadata <- fDataDT(temp_giotto)
+            ## get hvf
+            feat_metadata <- fDataDT(temp_giotto)
 
-            # data.table variables
-            hvg <- perc_cells <- mean_expr_det <- NULL
+            # NSE variables
+            hvf <- perc_cells <- mean_expr_det <- NULL
 
-            featgenes <- gene_metadata[
-                hvg == "yes" & perc_cells >= hvg_min_perc_cells &
-                    mean_expr_det >= hvg_mean_expr_det]$gene_ID
+            usefeats <- feat_metadata[
+                hvf == "yes" &
+                    perc_cells >= hvf_min_perc_cells &
+                    mean_expr_det >= hvf_mean_expr_det
+            ]$feat_ID
 
-            ## catch too low number of hvg
-            if (use_all_genes_as_hvg == TRUE) {
-                featgenes == gene_metadata$gene_ID
+            ## catch too low number of hvf
+            if (isTRUE(use_all_feats_as_hvf)) {
+                usefeats == feat_metadata$feat_ID
             } else {
-                if (verbose == TRUE)
-                    cat(length(featgenes),
-                        "highly variable genes have been selected")
-                if (length(featgenes) <= min_nr_of_hvg) {
-                    message("too few genes, will continue with all genes
-                            instead")
-                    featgenes <- gene_metadata$gene_ID
+                if (isTRUE(verbose)) {
+                    cat(
+                        length(usefeats),
+                        "highly variable features have been selected\n"
+                    )
+                }
+                if (length(usefeats) <= min_nr_of_hvf) {
+                    wrap_msg("too few features
+                            will continue with all features instead")
+                    usefeats <- feat_metadata$feat_ID
                 }
             }
 
             ## run PCA
+            pca_param$verbose <- FALSE
             temp_giotto <- do.call(
                 "runPCA",
-                c(gobject = temp_giotto, genes_to_use = list(featgenes),
-                pca_param))
+                c(
+                    gobject = temp_giotto, feats_to_use = list(usefeats),
+                    pca_param
+                )
+            )
 
             ## nearest neighbor and clustering
+            nn_param$verbose <- FALSE
             temp_giotto <- do.call(
                 "createNearestNetwork",
-                c(gobject = temp_giotto, k = k_neighbors, nn_param))
+                c(gobject = temp_giotto, k = k_neighbors, nn_param)
+            )
 
-            ## Leiden Cluster
             ## TO DO: expand to all clustering options
             temp_cluster <- .doLouvainCluster_community(
                 gobject = temp_giotto,
@@ -2256,63 +2453,30 @@ doLeidenSubCluster <- function(gobject,
 
 
 
-#' @title doLouvainSubCluster multinet
-#' @name .doLouvainSubCluster_multinet
-#' @description subcluster cells using a NN-network and the Louvain multinet
-#' detection algorithm
-#' @param gobject giotto object
-#' @param name name for new clustering result
-#' @param cluster_column cluster column to subcluster
-#' @param selected_clusters only do subclustering on these clusters
-#' @param hvg_param parameters for calculateHVG
-#' @param hvg_min_perc_cells threshold for detection in min percentage of cells
-#' @param hvg_mean_expr_det threshold for mean expression level in cells with
-#' detection
-#' @param use_all_genes_as_hvg forces all genes to be HVG and to be used as
-#' input for PCA
-#' @param min_nr_of_hvg minimum number of HVG, or all genes will be used as
-#' input for PCA
-#' @param pca_param parameters for runPCA
-#' @param nn_param parameters for parameters for createNearestNetwork
-#' @param k_neighbors number of k for createNearestNetwork
-#' @param gamma gamma
-#' @param omega omega
-#' @param nn_network_to_use type of NN network to use (kNN vs sNN)
-#' @param network_name name of NN network to use
-#' @param return_gobject boolean: return giotto object (default = TRUE)
-#' @param verbose verbose
-#' @returns giotto object with new subclusters appended to cell metadata
-#' @details This function performs subclustering using the Louvain multinet
-#' algorithm on selected clusters.
-#' The systematic steps are:
-#' \itemize{
-#'   \item{1. subset Giotto object}
-#'   \item{2. identify highly variable genes}
-#'   \item{3. run PCA}
-#'   \item{4. create nearest neighbouring network}
-#'   \item{5. do Louvain multinet clustering}
-#' }
-#' @seealso \code{\link{.doLouvainCluster_multinet}}
-#' @keywords internal
-.doLouvainSubCluster_multinet <- function(gobject,
-    name = "sub_louvain_mult_clus",
-    cluster_column = NULL,
-    selected_clusters = NULL,
-    hvg_param = list(reverse_log_scale = TRUE, difference_in_cov = 1,
-                    expression_values = "normalized"),
-    hvg_min_perc_cells = 5,
-    hvg_mean_expr_det = 1,
-    use_all_genes_as_hvg = FALSE,
-    min_nr_of_hvg = 5,
-    pca_param = list(expression_values = "normalized", scale_unit = TRUE),
-    nn_param = list(dimensions_to_use = 1:20),
-    k_neighbors = 10,
-    gamma = 1,
-    omega = 1,
-    nn_network_to_use = "sNN",
-    network_name = "sNN.pca",
-    return_gobject = TRUE,
-    verbose = TRUE) {
+# subcluster cells using a NN-network and the Louvain multinet
+# detection algorithm
+.doLouvainSubCluster_multinet <- function(
+        gobject,
+        name = "sub_louvain_mult_clus",
+        cluster_column = NULL,
+        selected_clusters = NULL,
+        hvf_param = list(
+            reverse_log_scale = TRUE, difference_in_cov = 1,
+            expression_values = "normalized"
+        ),
+        hvf_min_perc_cells = 5,
+        hvf_mean_expr_det = 1,
+        use_all_feats_as_hvf = FALSE,
+        min_nr_of_hvf = 5,
+        pca_param = list(expression_values = "normalized", scale_unit = TRUE),
+        nn_param = list(dimensions_to_use = 1:20),
+        k_neighbors = 10,
+        gamma = 1,
+        omega = 1,
+        nn_network_to_use = "sNN",
+        network_name = "sNN.pca",
+        return_gobject = TRUE,
+        verbose = TRUE) {
     if ("multinet" %in% rownames(installed.packages()) == FALSE) {
         stop(
             "package 'multinet' is not yet installed \n",
@@ -2335,69 +2499,86 @@ doLeidenSubCluster <- function(gobject,
 
 
     # data.table variables
-    hvg <- perc_cells <- mean_expr_det <- parent_cluster <- cell_ID <-
+    hvf <- perc_cells <- mean_expr_det <- parent_cluster <- cell_ID <-
         comb <- tempclus <- NULL
 
 
     for (cluster in unique_clusters) {
-        if (verbose == TRUE) cat("start with cluster: ", cluster)
+        if (verbose == TRUE) cat("start with cluster: ", cluster, "\n")
 
         ## get subset
         subset_cell_IDs <- cell_metadata[
-            get(cluster_column) == cluster][["cell_ID"]]
-        temp_giotto <- subsetGiotto(gobject = gobject,
-                                    cell_ids = subset_cell_IDs)
+            get(cluster_column) == cluster
+        ][["cell_ID"]]
+        temp_giotto <- subsetGiotto(
+            gobject = gobject,
+            cell_ids = subset_cell_IDs
+        )
 
         ## if cluster is not selected
         if (!is.null(selected_clusters) & !cluster %in% selected_clusters) {
             temp_cluster <- data.table(
                 "cell_ID" = subset_cell_IDs,
                 "tempclus" = 1,
-                "parent_cluster" = cluster)
+                "parent_cluster" = cluster
+            )
             iter_list[[cluster + index_offset]] <- temp_cluster
         } else {
             # continue for selected clusters or all clusters if there is no
             # selection
 
             ## calculate stats
-            temp_giotto <- addStatistics(gobject = temp_giotto)
+            temp_giotto <- addStatistics(
+                gobject = temp_giotto, verbose = FALSE
+            )
 
             ## calculate variable genes
+            hvf_param$verbose <- FALSE
             temp_giotto <- do.call(
-                "calculateHVG", c(gobject = temp_giotto, hvg_param))
+                "calculateHVF", c(gobject = temp_giotto, hvf_param)
+            )
 
-            ## get hvg
-            gene_metadata <- fDataDT(temp_giotto)
-            featgenes <- gene_metadata[
-                hvg == "yes" & perc_cells >= hvg_min_perc_cells &
-                    mean_expr_det >= hvg_mean_expr_det]$gene_ID
+            ## get hvf
+            feat_metadata <- fDataDT(temp_giotto)
+            usefeats <- feat_metadata[
+                hvf == "yes" & perc_cells >= hvf_min_perc_cells &
+                    mean_expr_det >= hvf_mean_expr_det
+            ]$feat_ID
 
-            ## catch too low number of hvg
-            if (use_all_genes_as_hvg == TRUE) {
-                featgenes == gene_metadata$gene_ID
+            ## catch too low number of hvf
+            if (use_all_feats_as_hvf == TRUE) {
+                usefeats == feat_metadata$feat_ID
             } else {
-                if (verbose == TRUE)
-                    cat(length(featgenes),
-                        "highly variable genes have been selecteds")
-                if (length(featgenes) <= min_nr_of_hvg) {
-                    message("too few genes, will continue with all genes
+                if (verbose == TRUE) {
+                    cat(
+                        length(usefeats),
+                        "highly variable features have been selected\n"
+                    )
+                }
+                if (length(usefeats) <= min_nr_of_hvf) {
+                    message("too few features, will continue with all features
                             instead")
-                    featgenes <- gene_metadata$gene_ID
+                    usefeats <- feat_metadata$feat_ID
                 }
             }
 
             ## run PCA
+            pca_param$verbose <- FALSE
             temp_giotto <- do.call(
                 "runPCA",
-                c(gobject = temp_giotto, genes_to_use = list(featgenes),
-                pca_param))
+                c(
+                    gobject = temp_giotto, feats_to_use = list(usefeats),
+                    pca_param
+                )
+            )
 
             ## nearest neighbor and clustering
+            nn_param$verbose <- FALSE
             temp_giotto <- do.call(
                 "createNearestNetwork",
-                c(gobject = temp_giotto, k = k_neighbors, nn_param))
+                c(gobject = temp_giotto, k = k_neighbors, nn_param)
+            )
 
-            ## Leiden Cluster
             ## TO DO: expand to all clustering options
             temp_cluster <- .doLouvainCluster_multinet(
                 gobject = temp_giotto,
@@ -2460,269 +2641,99 @@ doLeidenSubCluster <- function(gobject,
 
 
 
-#' @title doLouvainSubCluster
-#' @name doLouvainSubCluster
-#' @description subcluster cells using a NN-network and the Louvain algorithm
-#' @param gobject giotto object
-#' @param name name for new clustering result
-#' @param version version of Louvain algorithm to use
-#' @param cluster_column cluster column to subcluster
-#' @param selected_clusters only do subclustering on these clusters
-#' @param hvg_param parameters for calculateHVG
-#' @param hvg_min_perc_cells threshold for detection in min percentage of cells
-#' @param hvg_mean_expr_det threshold for mean expression level in cells with
-#' detection
-#' @param use_all_genes_as_hvg forces all genes to be HVG and to be used as
-#' input for PCA
-#' @param min_nr_of_hvg minimum number of HVG, or all genes will be used as
-#' input for PCA
-#' @param pca_param parameters for runPCA
-#' @param nn_param parameters for parameters for createNearestNetwork
-#' @param k_neighbors number of k for createNearestNetwork
-#' @param resolution resolution for community algorithm
-#' @param gamma gamma
-#' @param omega omega
-#' @param python_path specify specific path to python if required
-#' @param nn_network_to_use type of NN network to use (kNN vs sNN)
-#' @param network_name name of NN network to use
-#' @param return_gobject boolean: return giotto object (default = TRUE)
-#' @param verbose verbose
-#' @returns giotto object with new subclusters appended to cell metadata
-#' @details This function performs subclustering using the Louvain algorithm on
-#' selected clusters.
-#' The systematic steps are:
-#' \itemize{
-#'   \item{1. subset Giotto object}
-#'   \item{2. identify highly variable genes}
-#'   \item{3. run PCA}
-#'   \item{4. create nearest neighbouring network}
-#'   \item{5. do Louvain clustering}
-#' }
-#' @seealso \code{\link{.doLouvainCluster_multinet}} and
-#' \code{\link{.doLouvainCluster_community}}
-#' @examples
-#' g <- GiottoData::loadGiottoMini("visium")
-#'
-#' doLouvainSubCluster(g, cluster_column = "leiden_clus")
+
+#' @describeIn subClusterCells subcluster cells using a NN-network and the
+#' Louvain algorithm
+#' @param version version of Louvain algorithm to use. One of "community" or
+#' "multinet", with the default being "community"
 #' @export
-doLouvainSubCluster <- function(gobject,
-    name = "sub_louvain_clus",
-    version = c("community", "multinet"),
-    cluster_column = NULL,
-    selected_clusters = NULL,
-    hvg_param = list(reverse_log_scale = TRUE, difference_in_cov = 1,
-                    expression_values = "normalized"),
-    hvg_min_perc_cells = 5,
-    hvg_mean_expr_det = 1,
-    use_all_genes_as_hvg = FALSE,
-    min_nr_of_hvg = 5,
-    pca_param = list(expression_values = "normalized", scale_unit = TRUE),
-    nn_param = list(dimensions_to_use = 1:20),
-    k_neighbors = 10,
-    resolution = 0.5,
-    gamma = 1,
-    omega = 1,
-    python_path = NULL,
-    nn_network_to_use = "sNN",
-    network_name = "sNN.pca",
-    return_gobject = TRUE,
-    verbose = TRUE) {
+doLouvainSubCluster <- function(
+        gobject,
+        name = "sub_louvain_clus",
+        version = c("community", "multinet"),
+        cluster_column = NULL,
+        selected_clusters = NULL,
+        hvg_param = deprecated(),
+        hvf_param = list(
+            reverse_log_scale = TRUE, difference_in_cov = 1,
+            expression_values = "normalized"
+        ),
+        hvg_min_perc_cells = deprecated(),
+        hvf_min_perc_cells = 5,
+        hvg_mean_expr_det = deprecated(),
+        hvf_mean_expr_det = 1,
+        use_all_genes_as_hvg = deprecated(),
+        use_all_feats_as_hvf = FALSE,
+        min_nr_of_hvg = deprecated(),
+        min_nr_of_hvf = 5,
+        pca_param = list(expression_values = "normalized", scale_unit = TRUE),
+        nn_param = list(dimensions_to_use = 1:20),
+        k_neighbors = 10,
+        resolution = 0.5,
+        gamma = 1,
+        omega = 1,
+        python_path = NULL,
+        nn_network_to_use = "sNN",
+        network_name = "sNN.pca",
+        return_gobject = TRUE,
+        verbose = TRUE) {
     ## louvain clustering version to use
     version <- match.arg(version, c("community", "multinet"))
 
-
-    # python community implementation
-    if (version == "community") {
-        result <- .doLouvainSubCluster_community(
-            gobject = gobject,
-            cluster_column = cluster_column,
-            selected_clusters = selected_clusters,
-            hvg_param = hvg_param,
-            hvg_mean_expr_det = hvg_mean_expr_det,
-            pca_param = pca_param,
-            nn_param = nn_param,
-            k_neighbors = k_neighbors,
-            resolution = resolution,
-            python_path = python_path,
-            nn_network_to_use = nn_network_to_use,
-            network_name = network_name,
-            name = name,
-            return_gobject = return_gobject,
-            verbose = verbose
-        )
-    } else if (version == "multinet") {
-        result <- .doLouvainSubCluster_multinet(
-            gobject = gobject,
-            cluster_column = cluster_column,
-            selected_clusters = selected_clusters,
-            hvg_param = hvg_param,
-            hvg_mean_expr_det = hvg_mean_expr_det,
-            pca_param = pca_param,
-            nn_param = nn_param,
-            k_neighbors = k_neighbors,
-            gamma = gamma,
-            omega = omega,
-            nn_network_to_use = nn_network_to_use,
-            network_name = network_name,
-            name = name,
-            return_gobject = return_gobject,
-            verbose = verbose
+    # deprecations
+    .dep_param <- function(x, y) {
+        GiottoUtils::deprecate_param(
+            x, y,
+            fun = "doLouvainSubCluster", when = "4.0.9"
         )
     }
 
-    return(result)
-}
-
-
-
-
-
-#' @title subClusterCells
-#' @name subClusterCells
-#' @description subcluster cells
-#' @param gobject giotto object
-#' @param name name for new clustering result
-#' @param cluster_method clustering method to use
-#' @param cluster_column cluster column to subcluster
-#' @param selected_clusters only do subclustering on these clusters
-#' @param hvg_param parameters for calculateHVG
-#' @param hvg_min_perc_cells threshold for detection in min percentage of cells
-#' @param hvg_mean_expr_det threshold for mean expression level in cells with
-#' detection
-#' @param use_all_genes_as_hvg forces all genes to be HVG and to be used as
-#' input for PCA
-#' @param min_nr_of_hvg minimum number of HVG, or all genes will be used as
-#' input for PCA
-#' @param pca_param parameters for runPCA
-#' @param nn_param parameters for parameters for createNearestNetwork
-#' @param k_neighbors number of k for createNearestNetwork
-#' @param resolution resolution
-#' @param n_iterations number of interations to run the Leiden algorithm.
-#' @param gamma gamma
-#' @param omega omega
-#' @param python_path specify specific path to python if required
-#' @param nn_network_to_use type of NN network to use (kNN vs sNN)
-#' @param network_name name of NN network to use
-#' @param return_gobject boolean: return giotto object (default = TRUE)
-#' @param verbose verbose
-#' @returns giotto object with new subclusters appended to cell metadata
-#' @details This function performs subclustering on selected clusters.
-#' The systematic steps are:
-#' \itemize{
-#'   \item{1. subset Giotto object}
-#'   \item{2. identify highly variable genes}
-#'   \item{3. run PCA}
-#'   \item{4. create nearest neighbouring network}
-#'   \item{5. do clustering}
-#' }
-#' @seealso \code{\link{.doLouvainCluster_multinet}},
-#' \code{\link{.doLouvainCluster_community}}
-#' and  @seealso \code{\link{doLeidenCluster}}
-#' @examples
-#' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' subClusterCells(g, cluster_column = "leiden_clus")
-#' @export
-subClusterCells <- function(gobject,
-    name = "sub_clus",
-    cluster_method = c(
-        "leiden",
-        "louvain_community",
-        "louvain_multinet"
-    ),
-    cluster_column = NULL,
-    selected_clusters = NULL,
-    hvg_param = list(reverse_log_scale = TRUE, difference_in_cov = 1,
-                    expression_values = "normalized"),
-    hvg_min_perc_cells = 5,
-    hvg_mean_expr_det = 1,
-    use_all_genes_as_hvg = FALSE,
-    min_nr_of_hvg = 5,
-    pca_param = list(expression_values = "normalized", scale_unit = TRUE),
-    nn_param = list(dimensions_to_use = 1:20),
-    k_neighbors = 10,
-    resolution = 1,
-    n_iterations = 1000,
-    gamma = 1,
-    omega = 1,
-    python_path = NULL,
-    nn_network_to_use = "sNN",
-    network_name = "sNN.pca",
-    return_gobject = TRUE,
-    verbose = TRUE) {
-    ## select cluster method
-    cluster_method <- match.arg(arg = cluster_method, choices = c(
-        "leiden",
-        "louvain_community",
-        "louvain_multinet"
+    hvf_param <- .dep_param(hvg_param, hvf_param)
+    hvf_min_perc_cells <- .dep_param(hvg_min_perc_cells, hvf_min_perc_cells)
+    hvf_mean_expr_det <- .dep_param(hvg_mean_expr_det, hvf_mean_expr_det)
+    use_all_feats_as_hvf <- .dep_param(use_all_genes_as_hvg, use_all_feats_as_hvf)
+    min_nr_of_hvf <- .dep_param(min_nr_of_hvg, min_nr_of_hvf)
+
+    # get common args
+    common_args <- get_args_list(keep = c(
+        "gobject",
+        "cluster_column",
+        "selected_clusters",
+        "hvf_param",
+        "hvf_min_perc_cells",
+        "hvf_mean_expr_det",
+        "use_all_feats_as_hvf",
+        "min_nr_of_hvf",
+        "pca_param",
+        "nn_param",
+        "k_neighbors",
+        "nn_network_to_use",
+        "network_name",
+        "name",
+        "return_gobject",
+        "verbose"
     ))
 
-
-    if (cluster_method == "leiden") {
-        result <- doLeidenSubCluster(
-            gobject = gobject,
-            cluster_column = cluster_column,
-            selected_clusters = selected_clusters,
-            hvg_param = hvg_param,
-            hvg_min_perc_cells = hvg_min_perc_cells,
-            hvg_mean_expr_det = hvg_mean_expr_det,
-            use_all_genes_as_hvg = use_all_genes_as_hvg,
-            min_nr_of_hvg = min_nr_of_hvg,
-            pca_param = pca_param,
-            nn_param = nn_param,
-            k_neighbors = k_neighbors,
-            resolution = resolution,
-            n_iterations = n_iterations,
-            python_path = python_path,
-            nn_network_to_use = nn_network_to_use,
-            network_name = network_name,
-            name = name,
-            return_gobject = return_gobject,
-            verbose = verbose
-        )
-    } else if (cluster_method == "louvain_community") {
-        result <- .doLouvainSubCluster_community(
-            gobject = gobject,
-            cluster_column = cluster_column,
-            selected_clusters = selected_clusters,
-            hvg_param = hvg_param,
-            hvg_min_perc_cells = hvg_min_perc_cells,
-            hvg_mean_expr_det = hvg_mean_expr_det,
-            use_all_genes_as_hvg = use_all_genes_as_hvg,
-            min_nr_of_hvg = min_nr_of_hvg,
-            pca_param = pca_param,
-            nn_param = nn_param,
-            k_neighbors = k_neighbors,
-            resolution = resolution,
-            python_path = python_path,
-            nn_network_to_use = nn_network_to_use,
-            network_name = network_name,
-            name = name,
-            return_gobject = return_gobject,
-            verbose = verbose
-        )
-    } else if (cluster_method == "louvain_multinet") {
-        result <- .doLouvainSubCluster_multinet(
-            gobject = gobject,
-            cluster_column = cluster_column,
-            selected_clusters = selected_clusters,
-            hvg_param = hvg_param,
-            hvg_min_perc_cells = hvg_min_perc_cells,
-            hvg_mean_expr_det = hvg_mean_expr_det,
-            use_all_genes_as_hvg = use_all_genes_as_hvg,
-            min_nr_of_hvg = min_nr_of_hvg,
-            pca_param = pca_param,
-            nn_param = nn_param,
-            k_neighbors = k_neighbors,
-            gamma = gamma,
-            omega = omega,
-            nn_network_to_use = nn_network_to_use,
-            network_name = network_name,
-            name = name,
-            return_gobject = return_gobject,
-            verbose = verbose
-        )
-    }
+    result <- switch(version,
+        "community" = {
+            do.call(.doLouvainSubCluster_community, args = c(
+                common_args,
+                list(
+                    resolution = resolution,
+                    python_path = python_path
+                )
+            ))
+        },
+        "multinet" = {
+            do.call(.doLouvainSubCluster_multinet, args = c(
+                common_args,
+                list(
+                    gamma = gamma,
+                    omega = omega
+                )
+            ))
+        }
+    )
 
     return(result)
 }
@@ -2734,6 +2745,9 @@ subClusterCells <- function(gobject,
 
 
 
+# cluster manipulation ####
+
+
 #' @title getClusterSimilarity
 #' @name getClusterSimilarity
 #' @description Creates data.table with pairwise correlation scores between
@@ -2754,12 +2768,13 @@ subClusterCells <- function(gobject,
 #'
 #' getClusterSimilarity(g, cluster_column = "leiden_clus")
 #' @export
-getClusterSimilarity <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    cluster_column,
-    cor = c("pearson", "spearman")) {
+getClusterSimilarity <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        cluster_column,
+        cor = c("pearson", "spearman")) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -2777,7 +2792,8 @@ getClusterSimilarity <- function(gobject,
     cor <- match.arg(cor, c("pearson", "spearman"))
     values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
 
     metadata <- pDataDT(gobject,
         feat_type = feat_type,
@@ -2802,25 +2818,38 @@ getClusterSimilarity <- function(gobject,
         metadata_cols = cluster_column
     )
     dcast_metatable <- data.table::dcast.data.table(
-        metatable, formula = variable ~ uniq_ID, value.var = "value")
+        metatable,
+        formula = variable ~ uniq_ID, value.var = "value"
+    )
     testmatrix <- dt_to_matrix(x = dcast_metatable)
 
     # correlation matrix
     cormatrix <- cor_flex(x = testmatrix, method = cor)
     cor_table <- data.table::as.data.table(reshape2::melt(cormatrix))
     data.table::setnames(
-        cor_table, old = c("Var1", "Var2"), c("group1", "group2"))
+        cor_table,
+        old = c("Var1", "Var2"), c("group1", "group2")
+    )
     cor_table[, c("group1", "group2") := list(
-        as.character(group1), as.character(group2))]
+        as.character(group1), as.character(group2)
+    )]
     cor_table[, unified_group := paste(
-        sort(c(group1, group2)), collapse = "--"), by = 1:nrow(cor_table)]
+        sort(c(group1, group2)),
+        collapse = "--"
+    ),
+    by = 1:nrow(cor_table)
+    ]
     cor_table <- cor_table[!duplicated(cor_table[, .(value, unified_group)])]
 
     cor_table <- merge(
-        cor_table, by.x = "group1", clustersize, by.y = "clusters")
+        cor_table,
+        by.x = "group1", clustersize, by.y = "clusters"
+    )
     setnames(cor_table, "size", "group1_size")
     cor_table <- merge(
-        cor_table, by.x = "group2", clustersize, by.y = "clusters")
+        cor_table,
+        by.x = "group2", clustersize, by.y = "clusters"
+    )
     setnames(cor_table, "size", "group2_size")
 
     return(cor_table)
@@ -2864,19 +2893,20 @@ getClusterSimilarity <- function(gobject,
 #'
 #' mergeClusters(g, cluster_column = "leiden_clus")
 #' @export
-mergeClusters <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    cluster_column,
-    cor = c("pearson", "spearman"),
-    new_cluster_name = "merged_cluster",
-    min_cor_score = 0.8,
-    max_group_size = 20,
-    force_min_group_size = 10,
-    max_sim_clusters = 10,
-    return_gobject = TRUE,
-    verbose = TRUE) {
+mergeClusters <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        cluster_column,
+        cor = c("pearson", "spearman"),
+        new_cluster_name = "merged_cluster",
+        min_cor_score = 0.8,
+        max_group_size = 20,
+        force_min_group_size = 10,
+        max_sim_clusters = 10,
+        return_gobject = TRUE,
+        verbose = TRUE) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -2891,7 +2921,8 @@ mergeClusters <- function(gobject,
     # expression values to be used
     values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
 
     # correlation score to be used
     cor <- match.arg(cor, c("pearson", "spearman"))
@@ -2914,12 +2945,16 @@ mergeClusters <- function(gobject,
         min_reached <- cumsum_reached <- NULL
 
     filter_set_first <- similarityDT[group1 != group2][
-        group1_size < max_group_size][value >= min_cor_score]
+        group1_size < max_group_size
+    ][value >= min_cor_score]
 
     # 2. small clusters
     minimum_set <- similarityDT[group1 != group2][
-        group1_size < force_min_group_size][order(-value)][
-            , head(.SD, max_sim_clusters), by = group1]
+        group1_size < force_min_group_size
+    ][order(-value)][
+        , head(.SD, max_sim_clusters),
+        by = group1
+    ]
 
     # 2.1 take all clusters necessary to reach force_min_group_size
     minimum_set[, cumsum_val := cumsum(group2_size) + group1_size, by = group1]
@@ -2933,7 +2968,7 @@ mergeClusters <- function(gobject,
     ## get list of correlated groups
     finallist <- list()
     start_i <- 1
-    for (row in 1:nrow(filter_set)) {
+    for (row in seq_len(nrow(filter_set))) {
         first_clus <- filter_set[row][["group1"]]
         second_clus <- filter_set[row][["group2"]]
 
@@ -2949,7 +2984,8 @@ mergeClusters <- function(gobject,
         } else {
             who <- which(res == TRUE)[[1]]
             finallist[[who]] <- unique(
-                c(finallist[[who]], first_clus, second_clus))
+                c(finallist[[who]], first_clus, second_clus)
+            )
         }
     }
 
@@ -2996,7 +3032,9 @@ mergeClusters <- function(gobject,
             spat_unit = spat_unit,
             feat_type = feat_type,
             new_metadata = metadata[
-                , c("cell_ID", new_cluster_name), with = FALSE],
+                , c("cell_ID", new_cluster_name),
+                with = FALSE
+            ],
             by_column = TRUE,
             column_cell_ID = "cell_ID"
         )
@@ -3034,10 +3072,12 @@ mergeClusters <- function(gobject,
 
     dend_1 <- dendextend::find_dendrogram(
         dend = dend,
-        selected_labels = names(numerical_leaves[selected_labels_ind_1]))
+        selected_labels = names(numerical_leaves[selected_labels_ind_1])
+    )
     dend_2 <- dendextend::find_dendrogram(
         dend = dend,
-        selected_labels = names(numerical_leaves[selected_labels_ind_2]))
+        selected_labels = names(numerical_leaves[selected_labels_ind_2])
+    )
 
     return(list(theight = top_height, dend1 = dend_1, dend2 = dend_2))
 }
@@ -3074,7 +3114,9 @@ mergeClusters <- function(gobject,
 
         # check which heights are available
         available_h <- as.numeric(unlist(lapply(
-            dend_list, FUN = function(x) attributes(x)$height)))
+            dend_list,
+            FUN = function(x) attributes(x)$height
+        )))
 
         # get dendrogram associated with height and split in two
         select_dend_ind <- which.min(abs(available_h - n_height))
@@ -3084,13 +3126,19 @@ mergeClusters <- function(gobject,
         # find leave labels
         toph <- tempres[[1]]
         first_group <- dendextend::get_leaves_attr(
-            tempres[[2]], attribute = "label")
+            tempres[[2]],
+            attribute = "label"
+        )
         second_group <- dendextend::get_leaves_attr(
-            tempres[[3]], attribute = "label")
+            tempres[[3]],
+            attribute = "label"
+        )
 
-        result_list[[j]] <- list("height" = toph,
-                                "first" = first_group,
-                                "sec" = second_group)
+        result_list[[j]] <- list(
+            "height" = toph,
+            "first" = first_group,
+            "sec" = second_group
+        )
         j <- j + 1
 
 
@@ -3136,17 +3184,18 @@ mergeClusters <- function(gobject,
 #'
 #' getDendrogramSplits(g, cluster_column = "leiden_clus")
 #' @export
-getDendrogramSplits <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    cluster_column,
-    cor = c("pearson", "spearman"),
-    distance = "ward.D",
-    h = NULL,
-    h_color = "red",
-    show_dend = TRUE,
-    verbose = TRUE) {
+getDendrogramSplits <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        cluster_column,
+        cor = c("pearson", "spearman"),
+        distance = "ward.D",
+        h = NULL,
+        h_color = "red",
+        show_dend = TRUE,
+        verbose = TRUE) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -3167,7 +3216,8 @@ getDendrogramSplits <- function(gobject,
     cor <- match.arg(cor, c("pearson", "spearman"))
     values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
 
     # create average expression matrix per cluster
     metatable <- calculateMetaTable(
@@ -3178,7 +3228,9 @@ getDendrogramSplits <- function(gobject,
         metadata_cols = cluster_column
     )
     dcast_metatable <- data.table::dcast.data.table(
-        metatable, formula = variable ~ uniq_ID, value.var = "value")
+        metatable,
+        formula = variable ~ uniq_ID, value.var = "value"
+    )
     testmatrix <- dt_to_matrix(x = dcast_metatable)
 
     # correlation
@@ -3203,9 +3255,10 @@ getDendrogramSplits <- function(gobject,
     splitList <- .node_clusters(hclus_obj = corclus, verbose = verbose)
 
     splitDT <- data.table::as.data.table(t_flex(
-        data.table::as.data.table(splitList[[2]])))
+        data.table::as.data.table(splitList[[2]])
+    ))
     colnames(splitDT) <- c("node_h", "tree_1", "tree_2")
-    splitDT[, nodeID := paste0("node_", 1:.N)]
+    splitDT[, nodeID := paste0("node_", seq_len(.N))]
 
     return(splitDT)
 }
@@ -3256,27 +3309,30 @@ getDendrogramSplits <- function(gobject,
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #' x <- pDataDT(g)
-#' g_small <- subsetGiotto(g, cell_ids = sample(x$cell_ID,300))
-#' doClusterProjection(target_gobject = g, source_gobject = g_small,
-#' source_cluster_labels = "leiden_clus")
+#' g_small <- subsetGiotto(g, cell_ids = sample(x$cell_ID, 300))
+#' doClusterProjection(
+#'     target_gobject = g, source_gobject = g_small,
+#'     source_cluster_labels = "leiden_clus"
+#' )
 #' @export
-doClusterProjection <- function(target_gobject,
-    target_cluster_label_name = "knn_labels",
-    spat_unit = NULL,
-    feat_type = NULL,
-    source_gobject,
-    source_cluster_labels = NULL,
-    reduction = "cells",
-    reduction_method = "pca",
-    reduction_name = "pca",
-    dimensions_to_use = 1:10,
-    knn_k = 10,
-    prob = FALSE,
-    algorithm = c(
-        "kd_tree",
-        "cover_tree", "brute"
-    ),
-    return_gobject = TRUE) {
+doClusterProjection <- function(
+        target_gobject,
+        target_cluster_label_name = "knn_labels",
+        spat_unit = NULL,
+        feat_type = NULL,
+        source_gobject,
+        source_cluster_labels = NULL,
+        reduction = "cells",
+        reduction_method = "pca",
+        reduction_name = "pca",
+        dimensions_to_use = 1:10,
+        knn_k = 10,
+        prob = FALSE,
+        algorithm = c(
+            "kd_tree",
+            "cover_tree", "brute"
+        ),
+        return_gobject = TRUE) {
     # NSE vars
     cell_ID <- temp_name_prob <- NULL
 
@@ -3317,7 +3373,8 @@ doClusterProjection <- function(target_gobject,
 
     dim_coord <- dim_obj[]
     dimensions_to_use <- dimensions_to_use[
-        dimensions_to_use %in% 1:ncol(dim_coord)]
+        dimensions_to_use %in% seq_len(ncol(dim_coord))
+    ]
     matrix_to_use <- dim_coord[, dimensions_to_use]
 
     ## create the training and testset from the matrix
@@ -3326,12 +3383,14 @@ doClusterProjection <- function(target_gobject,
     # (w/ labels)
     # and target giotto object
     train <- matrix_to_use[
-        rownames(matrix_to_use) %in% names(source_annot_vec), ]
+        rownames(matrix_to_use) %in% names(source_annot_vec),
+    ]
     train <- train[match(names(source_annot_vec), rownames(train)), ]
 
     # the test set are the remaining cell_IDs that need a label
     test <- matrix_to_use[
-        !rownames(matrix_to_use) %in% names(source_annot_vec), ]
+        !rownames(matrix_to_use) %in% names(source_annot_vec),
+    ]
     cl <- source_annot_vec
 
     # make prediction
@@ -3369,14 +3428,18 @@ doClusterProjection <- function(target_gobject,
     if (isTRUE(prob)) {
         cell_meta_target[, temp_name_prob := probs[cell_ID]]
         cell_meta_target <- cell_meta_target[
-            , .(cell_ID, temp_name, temp_name_prob)]
+            , .(cell_ID, temp_name, temp_name_prob)
+        ]
         cell_meta_target[, temp_name_prob := ifelse(
-            is.na(temp_name_prob), 1, temp_name_prob)]
+            is.na(temp_name_prob), 1, temp_name_prob
+        )]
 
         data.table::setnames(cell_meta_target,
             old = c("temp_name", "temp_name_prob"),
-            new = c(target_cluster_label_name,
-                    paste0(target_cluster_label_name, "_prob"))
+            new = c(
+                target_cluster_label_name,
+                paste0(target_cluster_label_name, "_prob")
+            )
         )
     } else {
         cell_meta_target <- cell_meta_target[, .(cell_ID, temp_name)]
@@ -3397,7 +3460,8 @@ doClusterProjection <- function(target_gobject,
                 feat_type = feat_type,
                 new_metadata = cell_meta_target[
                     , c("cell_ID", target_cluster_label_name, prob_label),
-                    with = FALSE],
+                    with = FALSE
+                ],
                 by_column = TRUE,
                 column_cell_ID = "cell_ID"
             )
@@ -3407,7 +3471,9 @@ doClusterProjection <- function(target_gobject,
                 spat_unit = spat_unit,
                 feat_type = feat_type,
                 new_metadata = cell_meta_target[
-                    , c("cell_ID", target_cluster_label_name), with = FALSE],
+                    , c("cell_ID", target_cluster_label_name),
+                    with = FALSE
+                ],
                 by_column = TRUE,
                 column_cell_ID = "cell_ID"
             )
diff --git a/R/convenience.R b/R/convenience.R
index e94f8e20c..69fe5feee 100644
--- a/R/convenience.R
+++ b/R/convenience.R
@@ -13,12 +13,12 @@
 #' reader functions should be built using it as a base.
 #' @param spat_method spatial method for which the data is being read
 #' @param data_dir exported data directory to read from
-#' @param dir_items named list of directory items to expect and keywords to 
+#' @param dir_items named list of directory items to expect and keywords to
 #' match
 #' @param data_to_use character. Which type(s) of expression data to build the
 #' gobject with. Values should match with a *workflow* item in require_data_DT
 #' (see details)
-#' @param require_data_DT data.table detailing if expected data items are 
+#' @param require_data_DT data.table detailing if expected data items are
 #' required or optional for each \code{data_to_use} *workflow*
 #' @param cores cores to use
 #' @param verbose be verbose
@@ -31,10 +31,10 @@
 #'   \item{1. detection of items within \code{data_dir} by looking for keywords
 #'   assigned through \code{dir_items}}
 #'   \item{2. check of detected items to see if everything needed has been found.
-#'   Dictionary of necessary vs optional items for each \code{data_to_use} 
+#'   Dictionary of necessary vs optional items for each \code{data_to_use}
 #'   *workflow* is provided through \code{require_data_DT}}
-#'   \item{3. if multiple filepaths are found to be matching then select the 
-#'   first one. This function is only intended to find the first level 
+#'   \item{3. if multiple filepaths are found to be matching then select the
+#'   first one. This function is only intended to find the first level
 #'   subdirectories and files.}
 #' }
 #'
@@ -82,27 +82,32 @@ NULL
 
 #' @describeIn read_data_folder Should not be used directly
 #' @keywords internal
-.read_data_folder <- function(spat_method = NULL,
-    data_dir = NULL,
-    dir_items,
-    data_to_use,
-    load_format = NULL,
-    require_data_DT,
-    cores = NA,
-    verbose = NULL,
-    toplevel = 2L) {
+.read_data_folder <- function(
+        spat_method = NULL,
+        data_dir = NULL,
+        dir_items,
+        data_to_use,
+        load_format = NULL,
+        require_data_DT,
+        cores = NA,
+        verbose = NULL,
+        toplevel = 2L) {
     ch <- box_chars()
 
     # 0. check params
     if (is.null(data_dir) ||
         !dir.exists(data_dir)) {
-        .gstop(.n = toplevel, "The full path to a", spat_method, 
-                "directory must be given.")
+        .gstop(
+            .n = toplevel, "The full path to a", spat_method,
+            "directory must be given."
+        )
     }
     vmsg(.v = verbose, "A structured", spat_method, "directory will be used")
     if (!data_to_use %in% require_data_DT$workflow) {
-        .gstop(.n = toplevel, 
-            "Data requirements for data_to_use not found in require_data_DT")
+        .gstop(
+            .n = toplevel,
+            "Data requirements for data_to_use not found in require_data_DT"
+        )
     }
 
     # 1. detect items
@@ -126,10 +131,12 @@ NULL
                     .initial = paste0(ch$s, "> "),
                     item, " found"
                 )
-                for (item_i in seq_along(dir_items[[item]])) { 
+                for (item_i in seq_along(dir_items[[item]])) {
                     # print found item names
-                    subItem <- gsub(pattern = ".*/", replacement = "", 
-                                    x = dir_items[[item]][[item_i]])
+                    subItem <- gsub(
+                        pattern = ".*/", replacement = "",
+                        x = dir_items[[item]][[item_i]]
+                    )
                     vmsg(
                         .v = verbose, .is_debug = TRUE,
                         .initial = paste0(ch$s, ch$s, ch$l, ch$h, ch$h),
@@ -147,13 +154,16 @@ NULL
 
 
             require_data_DT <- require_data_DT[workflow == data_to_use, ]
-            if (!is.null(load_format)) 
+            if (!is.null(load_format)) {
                 require_data_DT <- require_data_DT[filetype == load_format, ]
+            }
 
-            if (item %in% require_data_DT[needed == TRUE, item]) 
+            if (item %in% require_data_DT[needed == TRUE, item]) {
                 stop(item, " is missing")
-            if (item %in% require_data_DT[needed == FALSE, item]) 
+            }
+            if (item %in% require_data_DT[needed == FALSE, item]) {
                 warning(item, "is missing (optional)")
+            }
         }
     }
 
@@ -193,7 +203,7 @@ NULL
 
 #' @title Create a giotto object from 10x visium data
 #' @name createGiottoVisiumObject
-#' @description Create Giotto object directly from a 10X visium folder. Also 
+#' @description Create Giotto object directly from a 10X visium folder. Also
 #' accepts visium H5 outputs.
 #'
 #' @param visium_dir path to the 10X visium directory [required]
@@ -202,7 +212,7 @@ NULL
 #' @param h5_visium_path path to visium 10X .h5 file
 #' @param h5_gene_ids gene names as symbols (default) or ensemble gene ids
 #' @param h5_tissue_positions_path path to tissue locations (.csv file)
-#' @param h5_image_png_path path to tissue .png file (optional). Image 
+#' @param h5_image_png_path path to tissue .png file (optional). Image
 #' autoscaling looks for matches in the filename for either 'hires' or 'lowres'
 #' @param h5_json_scalefactors_path path to .json scalefactors (optional)
 #' @param png_name select name of png to use (see details)
@@ -211,11 +221,11 @@ NULL
 #' @param xmin_adj deprecated
 #' @param ymax_adj deprecated
 #' @param ymin_adj deprecated
-#' @param instructions list of instructions or output result from 
+#' @param instructions list of instructions or output result from
 #' \code{\link[GiottoClass]{createGiottoInstructions}}
-#' @param cores how many cores or threads to use to read data if paths are 
+#' @param cores how many cores or threads to use to read data if paths are
 #' provided
-#' @param expression_matrix_class class of expression matrix to use 
+#' @param expression_matrix_class class of expression matrix to use
 #' (e.g. 'dgCMatrix', 'DelayedArray')
 #' @param h5_file optional path to create an on-disk h5 file
 #' @param verbose be verbose
@@ -239,31 +249,32 @@ NULL
 #' }
 #'
 #' @export
-createGiottoVisiumObject <- function(visium_dir = NULL,
-    expr_data = c("raw", "filter"),
-    gene_column_index = 1,
-    h5_visium_path = NULL,
-    h5_gene_ids = c("symbols", "ensembl"),
-    h5_tissue_positions_path = NULL,
-    h5_image_png_path = NULL,
-    h5_json_scalefactors_path = NULL,
-    png_name = NULL,
-    do_manual_adj = FALSE, # deprecated
-    xmax_adj = 0, # deprecated
-    xmin_adj = 0, # deprecated
-    ymax_adj = 0, # deprecated
-    ymin_adj = 0, # deprecated
-    instructions = NULL,
-    expression_matrix_class = c("dgCMatrix", "DelayedArray"),
-    h5_file = NULL,
-    cores = NA,
-    verbose = NULL) {
+createGiottoVisiumObject <- function(
+        visium_dir = NULL,
+        expr_data = c("raw", "filter"),
+        gene_column_index = 1,
+        h5_visium_path = NULL,
+        h5_gene_ids = c("symbols", "ensembl"),
+        h5_tissue_positions_path = NULL,
+        h5_image_png_path = NULL,
+        h5_json_scalefactors_path = NULL,
+        png_name = NULL,
+        do_manual_adj = FALSE, # deprecated
+        xmax_adj = 0, # deprecated
+        xmin_adj = 0, # deprecated
+        ymax_adj = 0, # deprecated
+        ymin_adj = 0, # deprecated
+        instructions = NULL,
+        expression_matrix_class = c("dgCMatrix", "DelayedArray"),
+        h5_file = NULL,
+        cores = NA,
+        verbose = NULL) {
     # NSE vars
     barcode <- row_pxl <- col_pxl <- in_tissue <- array_row <- array_col <- NULL
 
     # handle deprecations
-    img_dep_msg <- "The params 'do_manual_adj', 'xmax_adj', 'xmin_adj', 
-    'ymax_adj', 'ymin_adj' are no longer used. 
+    img_dep_msg <- "The params 'do_manual_adj', 'xmax_adj', 'xmin_adj',
+    'ymax_adj', 'ymin_adj' are no longer used.
     Please use the automated workflow."
     if (!isFALSE(do_manual_adj) ||
         xmax_adj != 0 ||
@@ -316,18 +327,17 @@ createGiottoVisiumObject <- function(visium_dir = NULL,
 
 
 
-.visium_create <- function(
-        expr_counts_path,
-        h5_gene_ids = NULL, # h5
-        gene_column_index = NULL, # folder
-        tissue_positions_path,
-        image_path = NULL,
-        scale_json_path = NULL,
-        png_name = NULL,
-        instructions = NULL,
-        expression_matrix_class = c("dgCMatrix", "DelayedArray"),
-        h5_file = NULL,
-        verbose = NULL) {
+.visium_create <- function(expr_counts_path,
+    h5_gene_ids = NULL, # h5
+    gene_column_index = NULL, # folder
+    tissue_positions_path,
+    image_path = NULL,
+    scale_json_path = NULL,
+    png_name = NULL,
+    instructions = NULL,
+    expression_matrix_class = c("dgCMatrix", "DelayedArray"),
+    h5_file = NULL,
+    verbose = NULL) {
     # NSE vars
     barcode <- cell_ID <- row_pxl <- col_pxl <- in_tissue <- array_row <-
         array_col <- NULL
@@ -348,12 +358,16 @@ createGiottoVisiumObject <- function(visium_dir = NULL,
     }
 
     # if expr_results is not a list, make it a list compatible with downstream
-    if (!is.list(expr_results)) expr_results <- list(
-        "Gene Expression" = expr_results)
+    if (!is.list(expr_results)) {
+        expr_results <- list(
+            "Gene Expression" = expr_results
+        )
+    }
 
     # format expected data into list to be used with readExprData()
     raw_matrix_list <- list("cell" = list("rna" = list(
-        "raw" = expr_results[["Gene Expression"]])))
+        "raw" = expr_results[["Gene Expression"]]
+    )))
 
     # add protein expression data to list if it exists
     if ("Antibody Capture" %in% names(expr_results)) {
@@ -363,12 +377,15 @@ createGiottoVisiumObject <- function(visium_dir = NULL,
 
     # 2. spatial locations
     spatial_results <- data.table::fread(tissue_positions_path)
-    colnames(spatial_results) <- c("barcode", "in_tissue", "array_row", 
-                                  "array_col", "col_pxl", "row_pxl")
+    colnames(spatial_results) <- c(
+        "barcode", "in_tissue", "array_row",
+        "array_col", "col_pxl", "row_pxl"
+    )
     spatial_results <- spatial_results[match(colnames(
-        raw_matrix_list$cell[[1]]$raw), barcode)]
+        raw_matrix_list$cell[[1]]$raw
+    ), barcode)]
     data.table::setnames(spatial_results, old = "barcode", new = "cell_ID")
-    spatial_locs <- spatial_results[, .(cell_ID, row_pxl, -col_pxl)] 
+    spatial_locs <- spatial_results[, .(cell_ID, row_pxl, -col_pxl)]
     # flip x and y
     colnames(spatial_locs) <- c("cell_ID", "sdimx", "sdimy")
 
@@ -388,7 +405,8 @@ createGiottoVisiumObject <- function(visium_dir = NULL,
 
     # 5. metadata
     meta_results <- spatial_results[
-        , .(cell_ID, in_tissue, array_row, array_col)]
+        , .(cell_ID, in_tissue, array_row, array_col)
+    ]
     expr_types <- names(raw_matrix_list$cell)
     meta_list <- list()
     for (etype in expr_types) {
@@ -427,17 +445,17 @@ createGiottoVisiumObject <- function(visium_dir = NULL,
 
 
 # Find and check the filepaths within a structured visium directory
-.visium_read_folder <- function(
-        visium_dir = NULL,
-        expr_data = c("raw", "filter"),
-        gene_column_index = 1,
-        png_name = NULL,
-        verbose = NULL) {
+.visium_read_folder <- function(visium_dir = NULL,
+    expr_data = c("raw", "filter"),
+    gene_column_index = 1,
+    png_name = NULL,
+    verbose = NULL) {
     vmsg(.v = verbose, "A structured visium directory will be used")
 
     ## check arguments
-    if (is.null(visium_dir)) 
+    if (is.null(visium_dir)) {
         .gstop("visium_dir needs to be a path to a visium directory")
+    }
     visium_dir <- path.expand(visium_dir)
     if (!dir.exists(visium_dir)) .gstop(visium_dir, " does not exist!")
     expr_data <- match.arg(expr_data, choices = c("raw", "filter"))
@@ -448,14 +466,16 @@ createGiottoVisiumObject <- function(visium_dir = NULL,
         "raw" = paste0(visium_dir, "/", "raw_feature_bc_matrix/"),
         "filter" = paste0(visium_dir, "/", "filtered_feature_bc_matrix/")
     )
-    if (!file.exists(expr_counts_path)) 
+    if (!file.exists(expr_counts_path)) {
         .gstop(expr_counts_path, "does not exist!")
+    }
 
 
     ## 2. check spatial locations
     spatial_dir <- paste0(visium_dir, "/", "spatial/")
     tissue_positions_path <- Sys.glob(
-        paths = file.path(spatial_dir, "tissue_positions*"))
+        paths = file.path(spatial_dir, "tissue_positions*")
+    )
 
 
     ## 3. check spatial image
@@ -469,8 +489,9 @@ createGiottoVisiumObject <- function(visium_dir = NULL,
 
     ## 4. check scalefactors
     scalefactors_path <- paste0(spatial_dir, "/", "scalefactors_json.json")
-    if (!file.exists(scalefactors_path)) 
+    if (!file.exists(scalefactors_path)) {
         .gstop(scalefactors_path, "does not exist!")
+    }
 
 
     list(
@@ -484,36 +505,44 @@ createGiottoVisiumObject <- function(visium_dir = NULL,
 
 
 
-.visium_read_h5 <- function(
-        h5_visium_path = h5_visium_path, # expression matrix
-        h5_gene_ids = h5_gene_ids,
-        h5_tissue_positions_path = h5_tissue_positions_path,
-        h5_image_png_path = h5_image_png_path,
-        h5_json_scalefactors_path = h5_json_scalefactors_path,
-        verbose = NULL) {
+.visium_read_h5 <- function(h5_visium_path = h5_visium_path, # expression matrix
+    h5_gene_ids = h5_gene_ids,
+    h5_tissue_positions_path = h5_tissue_positions_path,
+    h5_image_png_path = h5_image_png_path,
+    h5_json_scalefactors_path = h5_json_scalefactors_path,
+    verbose = NULL) {
     # 1. filepaths
-    vmsg(.v = verbose, 
-        "A path to an .h5 10X file was provided and will be used")
-    if (!file.exists(h5_visium_path)) 
+    vmsg(
+        .v = verbose,
+        "A path to an .h5 10X file was provided and will be used"
+    )
+    if (!file.exists(h5_visium_path)) {
         .gstop("The provided path ", h5_visium_path, " does not exist")
-    if (is.null(h5_tissue_positions_path)) 
-        .gstop("A path to the tissue positions (.csv) needs to be provided to 
+    }
+    if (is.null(h5_tissue_positions_path)) {
+        .gstop("A path to the tissue positions (.csv) needs to be provided to
                 h5_tissue_positions_path")
-    if (!file.exists(h5_tissue_positions_path)) 
-        .gstop("The provided path ", h5_tissue_positions_path, 
-                " does not exist")
+    }
+    if (!file.exists(h5_tissue_positions_path)) {
+        .gstop(
+            "The provided path ", h5_tissue_positions_path,
+            " does not exist"
+        )
+    }
     if (!is.null(h5_image_png_path)) {
         if (!file.exists(h5_image_png_path)) {
-            .gstop("The provided h5 image path ", h5_image_png_path, 
-            "does not exist.
-            Set to NULL to exclude or provide the correct path.")
+            .gstop(
+                "The provided h5 image path ", h5_image_png_path,
+                "does not exist.
+            Set to NULL to exclude or provide the correct path."
+            )
         }
     }
     if (!is.null(h5_json_scalefactors_path)) {
         if (!file.exists(h5_json_scalefactors_path)) {
             warning(wrap_txt(
                 "No file found at h5_json_scalefactors_path.
-                Scalefactors are needed for proper image alignment and 
+                Scalefactors are needed for proper image alignment and
                 polygon generation"
             ))
         }
@@ -549,8 +578,9 @@ createGiottoVisiumObject <- function(visium_dir = NULL,
 #' Adds circular giottoPolygons to the spatial_info slot of a Giotto Object
 #' for the "cell" spatial unit.
 #' @export
-addVisiumPolygons <- function(gobject,
-    scalefactor_path = NULL) {
+addVisiumPolygons <- function(
+        gobject,
+        scalefactor_path = NULL) {
     assert_giotto(gobject)
 
     visium_spat_locs <- getSpatialLocations(
@@ -591,8 +621,10 @@ addVisiumPolygons <- function(gobject,
 .visium_read_scalefactors <- function(json_path = NULL) {
     if (!checkmate::test_file_exists(json_path)) {
         if (!is.null(json_path)) {
-            warning("scalefactors not discovered at: \n", 
-                    json_path, call. = FALSE)
+            warning("scalefactors not discovered at: \n",
+                json_path,
+                call. = FALSE
+            )
         }
         return(NULL)
     }
@@ -640,7 +672,7 @@ addVisiumPolygons <- function(gobject,
 
 #' @title Calculate Pixel to Micron Scalefactor
 #' @name visium_micron_scalefactor
-#' @param json_scalefactors list of scalefactors from 
+#' @param json_scalefactors list of scalefactors from
 #' .visium_read_scalefactors()
 #' @returns scale factor for converting pixel to micron
 #' @details
@@ -662,7 +694,7 @@ addVisiumPolygons <- function(gobject,
 #' @name .visium_spot_poly
 #' @param spatlocs spatial locations data.table or `spatLocsObj` containing
 #' centroid locations of visium spots
-#' @param json_scalefactors list of scalefactors from 
+#' @param json_scalefactors list of scalefactors from
 #' .visium_read_scalefactors()
 #' @returns giottoPolygon object
 #' @details
@@ -670,8 +702,9 @@ addVisiumPolygons <- function(gobject,
 #' Visium spots.
 #' @keywords internal
 #' @md
-.visium_spot_poly <- function(spatlocs = NULL,
-    json_scalefactors) {
+.visium_spot_poly <- function(
+        spatlocs = NULL,
+        json_scalefactors) {
     if (inherits(spatlocs, "spatLocsObj")) {
         spatlocs <- spatlocs[]
     }
@@ -699,11 +732,10 @@ addVisiumPolygons <- function(gobject,
 # json_info expects the list read output from .visium_read_scalefactors
 # image_path should be expected to be full filepath
 # should only be used when do_manual_adj (deprecated) is FALSE
-.visium_image <- function(
-        image_path,
-        json_info = NULL,
-        micron_scale = FALSE,
-        verbose = NULL) {
+.visium_image <- function(image_path,
+    json_info = NULL,
+    micron_scale = FALSE,
+    verbose = NULL) {
     # assume image already checked
     vmsg(.v = verbose, .initial = " - ", "found image")
 
@@ -730,8 +762,8 @@ addVisiumPolygons <- function(gobject,
 
         if (is.null(visium_img_type)) { # if not recognized visium image type
             .gstop(
-                "\'image_path\' filename did not partial match either 
-                \'lowres\' or \'hires\'. Ensure specified image is either the 
+                "\'image_path\' filename did not partial match either
+                \'lowres\' or \'hires\'. Ensure specified image is either the
                 Visium lowres or hires image and rename it accordingly"
             )
         }
@@ -793,9 +825,10 @@ addVisiumPolygons <- function(gobject,
 #' if image_file is a list.
 #' @returns giottoLargeImage
 #' @export
-createMerscopeLargeImage <- function(image_file,
-    transforms_file,
-    name = "image") {
+createMerscopeLargeImage <- function(
+        image_file,
+        transforms_file,
+        name = "image") {
     checkmate::assert_character(transforms_file)
     tfsDT <- data.table::fread(transforms_file)
     if (inherits(image_file, "character")) {
@@ -836,12 +869,12 @@ createMerscopeLargeImage <- function(image_file,
 
 #' @title Create Vizgen MERSCOPE Giotto Object
 #' @name createGiottoMerscopeObject
-#' @description Given the path to a MERSCOPE experiment directory, creates a 
+#' @description Given the path to a MERSCOPE experiment directory, creates a
 #' Giotto object.
 #' @param merscope_dir full path to the exported merscope directory
-#' @param data_to_use which of either the 'subcellular' or 'aggregate' 
+#' @param data_to_use which of either the 'subcellular' or 'aggregate'
 #' information to use for object creation
-#' @param FOVs which FOVs to use when building the subcellular object. 
+#' @param FOVs which FOVs to use when building the subcellular object.
 #' (default is NULL)
 #' NULL loads all FOVs (very slow)
 #' @param calculate_overlap whether to run \code{\link{calculateOverlapRaster}}
@@ -851,9 +884,9 @@ createMerscopeLargeImage <- function(image_file,
 #' @inheritParams GiottoClass::createGiottoObjectSubcellular
 #' @returns a giotto object
 #' @details
-#' [\strong{Expected Directory}] This function generates a giotto object when 
-#' given a link to a MERSCOPE output directory. It expects the following items 
-#' within the directory where the \strong{bolded} portions are what this 
+#' [\strong{Expected Directory}] This function generates a giotto object when
+#' given a link to a MERSCOPE output directory. It expects the following items
+#' within the directory where the \strong{bolded} portions are what this
 #' function matches against:
 #' \itemize{
 #'   \item{\strong{cell_boundaries} (folder .hdf5 files)}
@@ -863,21 +896,22 @@ createMerscopeLargeImage <- function(image_file,
 #'   \item{detected_transcripts\strong{metadata_file}.csv (file)}
 #' }
 #' @export
-createGiottoMerscopeObject <- function(merscope_dir,
-    data_to_use = c("subcellular", "aggregate"),
-    FOVs = NULL,
-    poly_z_indices = 1:7,
-    calculate_overlap = TRUE,
-    overlap_to_matrix = TRUE,
-    aggregate_stack = TRUE,
-    aggregate_stack_param = list(
-        summarize_expression = "sum",
-        summarize_locations = "mean",
-        new_spat_unit = "cell"
-    ),
-    instructions = NULL,
-    cores = NA,
-    verbose = TRUE) {
+createGiottoMerscopeObject <- function(
+        merscope_dir,
+        data_to_use = c("subcellular", "aggregate"),
+        FOVs = NULL,
+        poly_z_indices = 1:7,
+        calculate_overlap = TRUE,
+        overlap_to_matrix = TRUE,
+        aggregate_stack = TRUE,
+        aggregate_stack_param = list(
+            summarize_expression = "sum",
+            summarize_locations = "mean",
+            new_spat_unit = "cell"
+        ),
+        instructions = NULL,
+        cores = NA,
+        verbose = TRUE) {
     fovs <- NULL
 
     # 0. setup
@@ -893,7 +927,8 @@ createGiottoMerscopeObject <- function(merscope_dir,
 
     # determine data to use
     data_to_use <- match.arg(
-        arg = data_to_use, choices = c("subcellular", "aggregate"))
+        arg = data_to_use, choices = c("subcellular", "aggregate")
+    )
 
     # 1. test if folder structure exists and is as expected
     dir_items <- .read_merscope_folder(
@@ -931,8 +966,10 @@ createGiottoMerscopeObject <- function(merscope_dir,
             verbose = verbose
         )
     } else {
-        stop(wrap_txt('data_to_use "', data_to_use, 
-                      '" not implemented', sep = ""))
+        stop(wrap_txt('data_to_use "', data_to_use,
+            '" not implemented',
+            sep = ""
+        ))
     }
 
     return(merscope_gobject)
@@ -941,21 +978,22 @@ createGiottoMerscopeObject <- function(merscope_dir,
 
 
 
-#' @describeIn createGiottoMerscopeObject Create giotto object with 
+#' @describeIn createGiottoMerscopeObject Create giotto object with
 #' 'subcellular' workflow
 #' @param data_list list of loaded data from \code{\link{load_merscope_folder}}
 #' @keywords internal
-.createGiottoMerscopeObject_subcellular <- function(data_list,
-    calculate_overlap = TRUE,
-    overlap_to_matrix = TRUE,
-    aggregate_stack = TRUE,
-    aggregate_stack_param = list(
-        summarize_expression = "sum",
-        summarize_locations = "mean",
-        new_spat_unit = "cell"
-    ),
-    cores = NA,
-    verbose = TRUE) {
+.createGiottoMerscopeObject_subcellular <- function(
+        data_list,
+        calculate_overlap = TRUE,
+        overlap_to_matrix = TRUE,
+        aggregate_stack = TRUE,
+        aggregate_stack_param = list(
+            summarize_expression = "sum",
+            summarize_locations = "mean",
+            new_spat_unit = "cell"
+        ),
+        cores = NA,
+        verbose = TRUE) {
     feat_coord <- neg_coord <- cellLabel_dir <- instructions <- NULL
 
     # unpack data_list
@@ -977,10 +1015,12 @@ createGiottoMerscopeObject <- function(merscope_dir,
     blank_dt <- tx_dt[gene %in% blank_id, ]
 
     # extract transcript_id col and store as feature meta
-    feat_meta <- unique(feat_dt[, c("gene", "transcript_id", "barcode_id"), 
-                                with = FALSE])
-    blank_meta <- unique(blank_dt[, c("gene", "transcript_id", "barcode_id"), 
-                                  with = FALSE])
+    feat_meta <- unique(feat_dt[, c("gene", "transcript_id", "barcode_id"),
+        with = FALSE
+    ])
+    blank_meta <- unique(blank_dt[, c("gene", "transcript_id", "barcode_id"),
+        with = FALSE
+    ])
     feat_dt[, c("transcript_id", "barcode_id") := NULL]
     blank_dt[, c("transcript_id", "barcode_id") := NULL]
 
@@ -1011,13 +1051,14 @@ createGiottoMerscopeObject <- function(merscope_dir,
 
 
 
-#' @describeIn createGiottoMerscopeObject Create giotto object with 'aggregate' 
+#' @describeIn createGiottoMerscopeObject Create giotto object with 'aggregate'
 #' workflow
 #' @param data_list list of loaded data from \code{\link{load_merscope_folder}}
 #' @keywords internal
-.createGiottoMerscopeObject_aggregate <- function(data_list,
-    cores = NA,
-    verbose = TRUE) {
+.createGiottoMerscopeObject_aggregate <- function(
+        data_list,
+        cores = NA,
+        verbose = TRUE) {
     # unpack data_list
     micronToPixelScale <- data_list$micronToPixelScale
     expr_dt <- data_list$expr_dt
@@ -1037,14 +1078,15 @@ createGiottoMerscopeObject <- function(merscope_dir,
 #' @title Create Spatial Genomics Giotto Object
 #' @name createSpatialGenomicsObject
 #' @param sg_dir full path to the exported Spatial Genomics directory
-#' @param instructions new instructions 
+#' @param instructions new instructions
 #' (e.g. result from createGiottoInstructions)
 #' @returns giotto object
 #' @description Given the path to a Spatial Genomics data directory, creates a
 #' Giotto object.
 #' @export
-createSpatialGenomicsObject <- function(sg_dir = NULL,
-    instructions = NULL) {
+createSpatialGenomicsObject <- function(
+        sg_dir = NULL,
+        instructions = NULL) {
     # Find files in Spatial Genomics directory
     dapi <- list.files(sg_dir, full.names = TRUE, pattern = "DAPI")
     mask <- list.files(sg_dir, full.names = TRUE, pattern = "mask")
@@ -1085,20 +1127,20 @@ createSpatialGenomicsObject <- function(sg_dir = NULL,
 #' object.
 #' @param cosmx_dir full path to the exported cosmx directory
 #' @param data_to_use which type(s) of expression data to build the gobject with
-#' Default is \code{'all'} information available. \code{'subcellular'} loads 
-#' the transcript coordinates only. \code{'aggregate'} loads the provided 
+#' Default is \code{'all'} information available. \code{'subcellular'} loads
+#' the transcript coordinates only. \code{'aggregate'} loads the provided
 #' aggregated expression matrix.
 #' @param FOVs field of views to load (only affects subcellular data and images)
-#' @param remove_background_polygon try to remove background polygon 
+#' @param remove_background_polygon try to remove background polygon
 #' (default: FALSE)
 #' @param background_algo algorithm to remove background polygon
 #' @param remove_unvalid_polygons remove unvalid polygons (default: TRUE)
 #' @inheritParams GiottoClass::createGiottoObjectSubcellular
 #' @returns a giotto object
 #' @details
-#' [\strong{Expected Directory}] This function generates a giotto object when 
-#' given a link to a cosmx output directory. It expects the following items 
-#' within the directory where the \strong{bolded} portions are what this 
+#' [\strong{Expected Directory}] This function generates a giotto object when
+#' given a link to a cosmx output directory. It expects the following items
+#' within the directory where the \strong{bolded} portions are what this
 #' function matches against:
 #' \itemize{
 #'   \item{\strong{CellComposite} (folder of images)}
@@ -1113,49 +1155,51 @@ createSpatialGenomicsObject <- function(sg_dir = NULL,
 #'
 #' [\strong{Workflows}] Workflow to use is accessed through the data_to_use param
 #' \itemize{
-#'   \item{'all' - loads and requires subcellular information from tx_file and 
+#'   \item{'all' - loads and requires subcellular information from tx_file and
 #'   fov_positions_file
-#'   and also the existing aggregated information 
+#'   and also the existing aggregated information
 #'   (expression, spatial locations, and metadata)
 #'   from exprMat_file and metadata_file.}
-#'   \item{'subcellular' - loads and requires subcellular information from 
+#'   \item{'subcellular' - loads and requires subcellular information from
 #'   tx_file and
 #'   fov_positions_file only.}
-#'   \item{'aggregate' - loads and requires the existing aggregate information 
-#'   (expression, spatial locations, and metadata) from exprMat_file and 
+#'   \item{'aggregate' - loads and requires the existing aggregate information
+#'   (expression, spatial locations, and metadata) from exprMat_file and
 #'   metadata_file.}
 #' }
 #'
-#' [\strong{Images}] Images in the default CellComposite, CellLabels, 
+#' [\strong{Images}] Images in the default CellComposite, CellLabels,
 #' CompartmentLabels, and CellOverlay
-#' folders will be loaded as giotto largeImage objects in all workflows as 
-#' long as they are available. Additionally, CellComposite images will be 
+#' folders will be loaded as giotto largeImage objects in all workflows as
+#' long as they are available. Additionally, CellComposite images will be
 #' converted to giotto image objects, making plotting with
 #' these image objects more responsive when accessing them from a server.
 #' \code{\link{showGiottoImageNames}} can be used to see the available images.
 #' @export
-createGiottoCosMxObject <- function(cosmx_dir = NULL,
-    data_to_use = c("all", "subcellular", "aggregate"),
-    remove_background_polygon = TRUE,
-    background_algo = c("range"),
-    remove_unvalid_polygons = TRUE,
-    FOVs = NULL,
-    instructions = NULL,
-    cores = determine_cores(),
-    verbose = TRUE) {
+createGiottoCosMxObject <- function(
+        cosmx_dir = NULL,
+        data_to_use = c("all", "subcellular", "aggregate"),
+        remove_background_polygon = TRUE,
+        background_algo = c("range"),
+        remove_unvalid_polygons = TRUE,
+        FOVs = NULL,
+        instructions = NULL,
+        cores = determine_cores(),
+        verbose = TRUE) {
     # 0. setup
     cosmx_dir <- path.expand(cosmx_dir)
 
     # determine data to use
     data_to_use <- match.arg(
-        arg = data_to_use, choices = c("all", "subcellular", "aggregate"))
+        arg = data_to_use, choices = c("all", "subcellular", "aggregate")
+    )
     if (data_to_use %in% c("all", "aggregate")) {
-        stop(wrap_txt('Convenience workflows "all" and "aggregate" are not 
+        stop(wrap_txt('Convenience workflows "all" and "aggregate" are not
                       available yet'))
     }
 
     # Define for data.table
-    fov <- target <- x_local_px <- y_local_px <- z <- cell_ID <- 
+    fov <- target <- x_local_px <- y_local_px <- z <- cell_ID <-
         CenterX_global_px <- CenterY_global_px <-
         CenterX_local_px <- CenterY_local_px <- NULL
 
@@ -1219,15 +1263,14 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
 #' @inheritParams createGiottoCosMxObject
 #' @returns giotto object
 #' @keywords internal
-.createGiottoCosMxObject_subcellular <- function(
-        dir_items,
-        FOVs = NULL,
-        remove_background_polygon = TRUE,
-        background_algo = c("range"),
-        remove_unvalid_polygons = TRUE,
-        cores,
-        verbose = TRUE,
-        instructions = NULL) {
+.createGiottoCosMxObject_subcellular <- function(dir_items,
+    FOVs = NULL,
+    remove_background_polygon = TRUE,
+    background_algo = c("range"),
+    remove_unvalid_polygons = TRUE,
+    cores,
+    verbose = TRUE,
+    instructions = NULL) {
     target <- fov <- NULL
 
     # load tx detections and FOV offsets ------------------------------------- #
@@ -1247,7 +1290,8 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
     tx_coord_all[, c("x_global_px", "y_global_px", "cell_ID") := NULL]
 
     data.table::setcolorder(
-        tx_coord_all, c("target", "x_local_px", "y_local_px", "z", "fov"))
+        tx_coord_all, c("target", "x_local_px", "y_local_px", "z", "fov")
+    )
 
     # feature detection type splitting --------------------------------------- #
 
@@ -1273,13 +1317,17 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
         if (isTRUE(verbose)) message("Loading image information...")
 
         composite_dir <- Sys.glob(paths = file.path(
-            dir_items$`CellComposite folder`, paste0("*", x, "*")))
+            dir_items$`CellComposite folder`, paste0("*", x, "*")
+        ))
         cellLabel_dir <- Sys.glob(paths = file.path(
-            dir_items$`CellLabels folder`, paste0("*", x, "*")))
+            dir_items$`CellLabels folder`, paste0("*", x, "*")
+        ))
         compartmentLabel_dir <- Sys.glob(paths = file.path(
-            dir_items$`CompartmentLabels folder`, paste0("*", x, "*")))
+            dir_items$`CompartmentLabels folder`, paste0("*", x, "*")
+        ))
         cellOverlay_dir <- Sys.glob(paths = file.path(
-            dir_items$`CellOverlay folder`, paste0("*", x, "*")))
+            dir_items$`CellOverlay folder`, paste0("*", x, "*")
+        ))
 
         # Missing warnings
         if (length(composite_dir) == 0) {
@@ -1314,11 +1362,15 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
 
         feat_coord <- feat_coords_all[fov == as.numeric(x)]
         data.table::setnames(
-            feat_coord, old = coord_oldnames, new = coord_newnames)
+            feat_coord,
+            old = coord_oldnames, new = coord_newnames
+        )
         # neg probe info
         neg_coord <- neg_coords_all[fov == as.numeric(x)]
         data.table::setnames(
-            neg_coord, old = coord_oldnames, new = coord_newnames)
+            neg_coord,
+            old = coord_oldnames, new = coord_newnames
+        )
 
 
         # build giotto object -------------------------------------- #
@@ -1344,8 +1396,9 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
 
 
         # find centroids as spatial locations ---------------------- #
-        if (isTRUE(verbose)) 
+        if (isTRUE(verbose)) {
             message("Finding polygon centroids as cell spatial locations...")
+        }
         fov_subset <- addSpatialCentroidLocations(
             fov_subset,
             poly_info = "cell",
@@ -1394,7 +1447,7 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
                 images = gImage_list
             )
 
-            # convert to MG for faster loading (particularly relevant for 
+            # convert to MG for faster loading (particularly relevant for
             # pulling from server)
             # TODO remove this
             fov_subset <- convertGiottoLargeImageToMG(
@@ -1439,10 +1492,11 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
 #' @inheritParams createGiottoCosMxObject
 #' @returns giotto object
 #' @keywords internal
-.createGiottoCosMxObject_aggregate <- function(dir_items,
-    cores,
-    verbose = TRUE,
-    instructions = NULL) {
+.createGiottoCosMxObject_aggregate <- function(
+        dir_items,
+        cores,
+        verbose = TRUE,
+        instructions = NULL) {
     data_to_use <- fov <- NULL
 
     data_list <- .load_cosmx_folder_aggregate(
@@ -1479,19 +1533,25 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
         # load in images
         img_ID <- data.table::data.table(
             fov = fov_shifts[, fov],
-            img_name = paste0("fov", 
-                              sprintf("%03d", fov_shifts[, fov]), "-image")
+            img_name = paste0(
+                "fov",
+                sprintf("%03d", fov_shifts[, fov]), "-image"
+            )
         )
 
         if (isTRUE(verbose)) message("Attaching image files...")
         composite_dir <- Sys.glob(paths = file.path(
-            dir_items$`CellComposite folder`, paste0("/*")))
+            dir_items$`CellComposite folder`, paste0("/*")
+        ))
         cellLabel_dir <- Sys.glob(paths = file.path(
-            dir_items$`CellLabels folder`, paste0("/*")))
+            dir_items$`CellLabels folder`, paste0("/*")
+        ))
         compartmentLabel_dir <- Sys.glob(paths = file.path(
-            dir_items$`CompartmentLabels folder`, paste0("/*")))
+            dir_items$`CompartmentLabels folder`, paste0("/*")
+        ))
         overlay_dir <- Sys.glob(paths = file.path(
-            dir_items$`CellOverlay folder`, paste0("/*")))
+            dir_items$`CellOverlay folder`, paste0("/*")
+        ))
 
         if (length(cellLabel_imgList) > 0) {
             cellLabel_imgList <- lapply(cellLabel_dir, function(x) {
@@ -1506,8 +1566,9 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
         if (length(compartmentLabel_dir) > 0) {
             compartmentLabel_imgList <- lapply(
                 compartmentLabel_dir, function(x) {
-                createGiottoLargeImage(x, name = "composite", negative_y = TRUE)
-            })
+                    createGiottoLargeImage(x, name = "composite", negative_y = TRUE)
+                }
+            )
         }
         if (length(overlay_dir) > 0) {
             overlay_imgList <- lapply(overlay_dir, function(x) {
@@ -1520,30 +1581,31 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
 
 
 
-#' @title Load and create a CosMx Giotto object from subcellular and aggregate 
+#' @title Load and create a CosMx Giotto object from subcellular and aggregate
 #' info
 #' @name .createGiottoCosMxObject_all
 #' @param dir_items list of full directory paths from \code{.read_cosmx_folder}
 #' @inheritParams createGiottoCosMxObject
 #' @returns giotto object
-#' @details Both \emph{subcellular} 
+#' @details Both \emph{subcellular}
 #' (subellular transcript detection information) and
-#' \emph{aggregate} (aggregated detection count matrices by cell polygon from 
+#' \emph{aggregate} (aggregated detection count matrices by cell polygon from
 #' NanoString)
 #' data will be loaded in. The two will be separated into 'cell' and 'cell_agg'
 #' spatial units in order to denote the difference in origin of the two.
 #' @seealso createGiottoCosMxObject .createGiottoCosMxObject_aggregate
 #' .createGiottoCosMxObject_subcellular
 #' @keywords internal
-.createGiottoCosMxObject_all <- function(dir_items,
-    FOVs,
-    remove_background_polygon = TRUE,
-    background_algo = c("range"),
-    remove_unvalid_polygons = TRUE,
-    cores,
-    verbose = TRUE,
-    instructions = NULL,
-    ...) {
+.createGiottoCosMxObject_all <- function(
+        dir_items,
+        FOVs,
+        remove_background_polygon = TRUE,
+        background_algo = c("range"),
+        remove_unvalid_polygons = TRUE,
+        cores,
+        verbose = TRUE,
+        instructions = NULL,
+        ...) {
     # 1. create subcellular giotto as spat_unit 'cell'
     cosmx_gobject <- .createGiottoCosMxObject_subcellular(
         dir_items = dir_items,
@@ -1570,15 +1632,18 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
     protM <- agg_data$protM
     spM <- agg_data$spM
 
-    # add in pre-generated aggregated expression matrix information for 'all' 
+    # add in pre-generated aggregated expression matrix information for 'all'
     # workflow
 
     # Add aggregate expression information
-    if (isTRUE(verbose)) wrap_msg(
-    'Appending provided aggregate expression data as...
+    if (isTRUE(verbose)) {
+        wrap_msg(
+            'Appending provided aggregate expression data as...
                                spat_unit: "cell_agg"
                                feat_type: "rna"
-                               name: "raw"')
+                               name: "raw"'
+        )
+    }
     # add expression data to expression slot
     s4_expr <- createExprObj(
         name = "raw",
@@ -1591,13 +1656,19 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
     cosmx_gobject <- set_expression_values(cosmx_gobject, values = s4_expr)
 
     # Add spatial locations
-    if (isTRUE(verbose)) wrap_msg(
-    'Appending metadata provided spatial locations data as...
+    if (isTRUE(verbose)) {
+        wrap_msg(
+            'Appending metadata provided spatial locations data as...
                                --> spat_unit: "cell_agg" name: "raw"
-                               --> spat_unit: "cell" name: "raw_fov"')
-    if (isTRUE(verbose)) wrap_msg(
-    'Polygon centroid derived spatial locations assigned as...
-                               --> spat_unit: "cell" name: "raw" (default)')
+                               --> spat_unit: "cell" name: "raw_fov"'
+        )
+    }
+    if (isTRUE(verbose)) {
+        wrap_msg(
+            'Polygon centroid derived spatial locations assigned as...
+                               --> spat_unit: "cell" name: "raw" (default)'
+        )
+    }
 
     locsObj <- create_spat_locs_obj(
         name = "raw",
@@ -1613,8 +1684,9 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
     )
 
     cosmx_gobject <- set_spatial_locations(cosmx_gobject, spatlocs = locsObj)
-    cosmx_gobject <- set_spatial_locations(cosmx_gobject, 
-                                          spatlocs = locsObj_fov)
+    cosmx_gobject <- set_spatial_locations(cosmx_gobject,
+        spatlocs = locsObj_fov
+    )
 
     # initialize cell and feat IDs and metadata slots for 'cell_agg' spat_unit
     agg_cell_ID <- colnames(s4_expr[])
@@ -1658,24 +1730,24 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
 
 #' @title Create 10x Xenium Giotto Object
 #' @name createGiottoXeniumObject
-#' @description Given the path to a Xenium experiment output folder, creates a 
+#' @description Given the path to a Xenium experiment output folder, creates a
 #' Giotto object
 #' @param xenium_dir full path to the exported xenium directory
 #' @param data_to_use which type(s) of expression data to build the gobject with
 #' (e.g. default: \strong{'subcellular'}, 'aggregate', or 'all')
 #' @param load_format files formats from which to load the data. Either `csv` or
 #' `parquet` currently supported.
-#' @param h5_expression (boolean) whether to load cell_feature_matrix from .h5 
+#' @param h5_expression (boolean) whether to load cell_feature_matrix from .h5
 #' file. Default is \code{TRUE}
 #' @param h5_gene_ids use gene symbols (default) or ensembl ids for the .h5 gene
 #' expression matrix
-#' @param bounds_to_load vector of boundary information to load 
+#' @param bounds_to_load vector of boundary information to load
 #' (e.g. \code{'cell'}
 #' or \code{'nucleus'} by themselves or \code{c('cell', 'nucleus')} to load both
 #' at the same time.)
-#' @param qv_threshold Minimum Phred-scaled quality score cutoff to be included 
+#' @param qv_threshold Minimum Phred-scaled quality score cutoff to be included
 #' as a subcellular transcript detection (default = 20)
-#' @param key_list (advanced) list of grep-based keywords to split the 
+#' @param key_list (advanced) list of grep-based keywords to split the
 #' subcellular feature detections by feature type. See details
 #' @inheritParams get10Xmatrix
 #' @inheritParams GiottoClass::createGiottoObjectSubcellular
@@ -1686,20 +1758,20 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
 #' Xenium provides info on feature detections that include more than only the
 #' Gene Expression specific probes. Additional probes for QC are included:
 #' \emph{blank codeword}, \emph{negative control codeword}, and
-#' \emph{negative control probe}. These additional QC probes each occupy and 
-#' are treated as their own feature types so that they can largely remain 
+#' \emph{negative control probe}. These additional QC probes each occupy and
+#' are treated as their own feature types so that they can largely remain
 #' independent of the gene expression information.
 #'
 #' [\strong{key_list}]
 #' Related to \code{data_to_use = 'subcellular'} workflow only:
-#' Additional QC probe information is in the subcellular feature detections 
-#' information and must be separated from the gene expression information 
+#' Additional QC probe information is in the subcellular feature detections
+#' information and must be separated from the gene expression information
 #' during processing.
-#' The QC probes have prefixes that allow them to be selected from the rest of 
+#' The QC probes have prefixes that allow them to be selected from the rest of
 #' the feature IDs.
-#' Giotto uses a named list of keywords (\code{key_list}) to select these QC 
-#' probes, with the list names being the names that will be assigned as the 
-#' feature type of these feature detections. The default list is used when 
+#' Giotto uses a named list of keywords (\code{key_list}) to select these QC
+#' probes, with the list names being the names that will be assigned as the
+#' feature type of these feature detections. The default list is used when
 #' \code{key_list} = NULL.
 #'
 #' Default list:
@@ -1713,30 +1785,33 @@ createGiottoCosMxObject <- function(cosmx_dir = NULL,
 #' map to any of the keys.
 #'
 #' @export
-createGiottoXeniumObject <- function(xenium_dir,
-    data_to_use = c("subcellular", "aggregate"),
-    load_format = "csv",
-    h5_expression = TRUE,
-    h5_gene_ids = c("symbols", "ensembl"),
-    gene_column_index = 1,
-    bounds_to_load = c("cell"),
-    qv_threshold = 20,
-    key_list = NULL,
-    instructions = NULL,
-    cores = NA,
-    verbose = TRUE) {
+createGiottoXeniumObject <- function(
+        xenium_dir,
+        data_to_use = c("subcellular", "aggregate"),
+        load_format = "csv",
+        h5_expression = TRUE,
+        h5_gene_ids = c("symbols", "ensembl"),
+        gene_column_index = 1,
+        bounds_to_load = c("cell"),
+        qv_threshold = 20,
+        key_list = NULL,
+        instructions = NULL,
+        cores = NA,
+        verbose = TRUE) {
     # 0. setup
     xenium_dir <- path.expand(xenium_dir)
 
     # Determine data to load
     data_to_use <- match.arg(
-        arg = data_to_use, choices = c("subcellular", "aggregate"))
+        arg = data_to_use, choices = c("subcellular", "aggregate")
+    )
 
     # Determine load formats
-    load_format <- "csv" # TODO Remove this and add as param once other options 
+    load_format <- "csv" # TODO Remove this and add as param once other options
     # are available
     load_format <- match.arg(
-        arg = load_format, choices = c("csv", "parquet", "zarr"))
+        arg = load_format, choices = c("csv", "parquet", "zarr")
+    )
 
     # set number of cores automatically, but with limit of 10
     cores <- determine_cores(cores)
@@ -1837,19 +1912,20 @@ createGiottoXeniumObject <- function(xenium_dir,
 #' @param data_list list of data loaded by \code{\link{.load_xenium_folder}}
 #' @param key_list regex-based search keys for feature IDs to allow separation
 #' into separate giottoPoints objects by feat_type
-#' @param qv_threshold Minimum Phred-scaled quality score cutoff to be included 
+#' @param qv_threshold Minimum Phred-scaled quality score cutoff to be included
 #' as a subcellular transcript detection (default = 20)
 #' @inheritParams get10Xmatrix
 #' @inheritParams GiottoClass::createGiottoObjectSubcellular
 #' @returns giotto object
 #' @seealso createGiottoXeniumObject .createGiottoXeniumObject_aggregate
 #' @keywords internal
-.createGiottoXeniumObject_subcellular <- function(data_list,
-    key_list = NULL,
-    qv_threshold = 20,
-    instructions = NULL,
-    cores = NA,
-    verbose = TRUE) {
+.createGiottoXeniumObject_subcellular <- function(
+        data_list,
+        key_list = NULL,
+        qv_threshold = 20,
+        instructions = NULL,
+        cores = NA,
+        verbose = TRUE) {
     # data.table vars
     qv <- NULL
 
@@ -1866,8 +1942,10 @@ createGiottoXeniumObject <- function(xenium_dir,
     vmsg("> points data prep...", .v = verbose)
 
     # filter by qv_threshold
-    vmsg("> filtering feature detections for Phred score >= ", 
-        qv_threshold, .v = verbose)
+    vmsg("> filtering feature detections for Phred score >= ",
+        qv_threshold,
+        .v = verbose
+    )
     n_before <- tx_dt[, .N]
     tx_dt_filtered <- tx_dt[qv >= qv_threshold]
     n_after <- tx_dt_filtered[, .N]
@@ -1884,7 +1962,8 @@ createGiottoXeniumObject <- function(xenium_dir,
     # discover feat_IDs for each feat_type
     all_IDs <- tx_dt_filtered[, unique(feat_ID)]
     feat_types_IDs <- lapply(
-        key_list, function(x) all_IDs[grepl(pattern = x, all_IDs)])
+        key_list, function(x) all_IDs[grepl(pattern = x, all_IDs)]
+    )
     rna <- list("rna" = all_IDs[!all_IDs %in% unlist(feat_types_IDs)])
     feat_types_IDs <- append(rna, feat_types_IDs)
 
@@ -1937,11 +2016,12 @@ createGiottoXeniumObject <- function(xenium_dir,
 #' @returns giotto object
 #' @seealso createGiottoXeniumObject .createGiottoXeniumObject_subcellular
 #' @keywords internal
-.createGiottoXeniumObject_aggregate <- function(data_list,
-    # include_analysis = FALSE,
-    instructions = NULL,
-    cores = NA,
-    verbose = TRUE) {
+.createGiottoXeniumObject_aggregate <- function(
+        data_list,
+        # include_analysis = FALSE,
+        instructions = NULL,
+        cores = NA,
+        verbose = TRUE) {
     # Unpack data_list info
     feat_meta <- data_list$feat_meta
     cell_meta <- data_list$cell_meta
@@ -2002,10 +2082,11 @@ createGiottoXeniumObject <- function(xenium_dir,
 
 #' @describeIn read_data_folder Read a structured MERSCOPE folder
 #' @keywords internal
-.read_merscope_folder <- function(merscope_dir,
-    data_to_use,
-    cores = NA,
-    verbose = NULL) {
+.read_merscope_folder <- function(
+        merscope_dir,
+        data_to_use,
+        cores = NA,
+        verbose = NULL) {
     # prepare dir_items list
     dir_items <- list(
         `boundary info` = "*cell_boundaries*",
@@ -2064,12 +2145,14 @@ createGiottoXeniumObject <- function(xenium_dir,
 #' @returns path_list a list of cosmx files discovered and their filepaths. NULL
 #' values denote missing items
 #' @keywords internal
-.read_cosmx_folder <- function(cosmx_dir,
-    verbose = TRUE) {
+.read_cosmx_folder <- function(
+        cosmx_dir,
+        verbose = TRUE) {
     ch <- box_chars()
 
-    if (is.null(cosmx_dir) | !dir.exists(cosmx_dir)) 
+    if (is.null(cosmx_dir) | !dir.exists(cosmx_dir)) {
         stop("The full path to a cosmx directory must be given.")
+    }
     vmsg("A structured CosMx directory will be used\n", .v = verbose)
 
     # find directories (length = 1 if present, length = 0 if missing)
@@ -2084,7 +2167,8 @@ createGiottoXeniumObject <- function(xenium_dir,
         `metadata file` = "*metadata_file*"
     )
     dir_items <- lapply(
-        dir_items, function(x) Sys.glob(paths = file.path(cosmx_dir, x)))
+        dir_items, function(x) Sys.glob(paths = file.path(cosmx_dir, x))
+    )
     dir_items_lengths <- lengths(dir_items)
 
     if (isTRUE(verbose)) {
@@ -2100,7 +2184,7 @@ createGiottoXeniumObject <- function(xenium_dir,
 
     # select first directory in list if multiple are detected
     if (any(dir_items_lengths > 1)) {
-        warning("Multiple matches for expected subdirectory item(s).\n 
+        warning("Multiple matches for expected subdirectory item(s).\n
                 First matching item selected")
 
         multiples <- which(dir_items_lengths > 1)
@@ -2124,12 +2208,13 @@ createGiottoXeniumObject <- function(xenium_dir,
 #' @keywords internal
 #' @returns path_list a list of xenium files discovered and their filepaths. NULL
 #' values denote missing items
-.read_xenium_folder <- function(xenium_dir,
-    data_to_use = "subcellular",
-    bounds_to_load = c("cell"),
-    load_format = "csv",
-    h5_expression = FALSE,
-    verbose = TRUE) {
+.read_xenium_folder <- function(
+        xenium_dir,
+        data_to_use = "subcellular",
+        bounds_to_load = c("cell"),
+        load_format = "csv",
+        h5_expression = FALSE,
+        verbose = TRUE) {
     # Check needed packages
     if (load_format == "parquet") {
         package_check(pkg_name = "arrow", repository = "CRAN")
@@ -2145,8 +2230,9 @@ createGiottoXeniumObject <- function(xenium_dir,
     # 0. test if folder structure exists and is as expected
 
 
-    if (is.null(xenium_dir) | !dir.exists(xenium_dir)) 
+    if (is.null(xenium_dir) | !dir.exists(xenium_dir)) {
         stop("The full path to a xenium directory must be given.")
+    }
     vmsg("A structured Xenium directory will be used\n", .v = verbose)
 
     # find items (length = 1 if present, length = 0 if missing)
@@ -2162,7 +2248,8 @@ createGiottoXeniumObject <- function(xenium_dir,
     )
 
     dir_items <- lapply(
-        dir_items, function(x) Sys.glob(paths = file.path(xenium_dir, x)))
+        dir_items, function(x) Sys.glob(paths = file.path(xenium_dir, x))
+    )
     dir_items_lengths <- lengths(dir_items)
 
     if (isTRUE(verbose)) {
@@ -2172,10 +2259,12 @@ createGiottoXeniumObject <- function(xenium_dir,
 
             if (dir_items_lengths[[item]] > 0) {
                 message(ch$s, "> ", item, " found")
-                for (item_i in seq_along(dir_items[[item]])) { 
+                for (item_i in seq_along(dir_items[[item]])) {
                     # print found item names
-                    subItem <- gsub(pattern = ".*/", replacement = "", 
-                                    x = dir_items[[item]][[item_i]])
+                    subItem <- gsub(
+                        pattern = ".*/", replacement = "",
+                        x = dir_items[[item]][[item_i]]
+                    )
                     message(ch$s, ch$s, ch$l, ch$h, ch$h, subItem)
                 }
             } else {
@@ -2186,24 +2275,30 @@ createGiottoXeniumObject <- function(xenium_dir,
 
                 if (data_to_use == "subcellular") {
                     # necessary items
-                    if (item %in% c("boundary info", "raw transcript info")) 
+                    if (item %in% c("boundary info", "raw transcript info")) {
                         stop(item, " is missing")
+                    }
                     # optional items
                     if (item %in% c(
-                        "image info", "experiment info (.xenium)", 
-                        "panel metadata")) 
+                        "image info", "experiment info (.xenium)",
+                        "panel metadata"
+                    )) {
                         warning(item, " is missing (optional)")
-                    # items to ignore: analysis info, cell feature matrix, 
+                    }
+                    # items to ignore: analysis info, cell feature matrix,
                     # cell metadata
                 } else if (data_to_use == "aggregate") {
                     # necessary items
-                    if (item %in% c("cell feature matrix", "cell metadata")) 
+                    if (item %in% c("cell feature matrix", "cell metadata")) {
                         stop(item, " is missing")
+                    }
                     # optional items
                     if (item %in% c(
-                        "image info", "experiment info (.xenium)", 
-                        "panel metadata", "analysis info")) 
+                        "image info", "experiment info (.xenium)",
+                        "panel metadata", "analysis info"
+                    )) {
                         warning(item, " is missing (optional)")
+                    }
                     # items to ignore: boundary info, raw transcript info
                 }
             }
@@ -2217,45 +2312,55 @@ createGiottoXeniumObject <- function(xenium_dir,
     # **** transcript info ****
     tx_path <- NULL
     tx_path <- dir_items$`raw transcript info`[grepl(
-        pattern = load_format, dir_items$`raw transcript info`)]
+        pattern = load_format, dir_items$`raw transcript info`
+    )]
     # **** cell metadata ****
     cell_meta_path <- NULL
     cell_meta_path <- dir_items$`cell metadata`[grepl(
-        pattern = load_format, dir_items$`cell metadata`)]
+        pattern = load_format, dir_items$`cell metadata`
+    )]
 
     # **** boundary info ****
     # Select bound load format
     if (load_format != "zarr") { # No zarr available for boundary info
         dir_items$`boundary info` <- dir_items$`boundary info`[grepl(
-            pattern = load_format, dir_items$`boundary info`)]
+            pattern = load_format, dir_items$`boundary info`
+        )]
     } else {
         dir_items$`boundary info` <- dir_items$`boundary info`[grepl(
-            pattern = "csv", dir_items$`boundary info`)]
+            pattern = "csv", dir_items$`boundary info`
+        )]
     }
 
     # Organize bound paths by type of bound (bounds_to_load param)
     bound_paths <- NULL
     bound_names <- bounds_to_load
     bounds_to_load <- as.list(bounds_to_load)
-    bound_paths <- lapply(bounds_to_load, function(x) dir_items$`boundary info`[
-        grepl(pattern = x, dir_items$`boundary info`)])
+    bound_paths <- lapply(bounds_to_load, function(x) {
+        dir_items$`boundary info`[
+            grepl(pattern = x, dir_items$`boundary info`)
+        ]
+    })
     names(bound_paths) <- bound_names
 
     # **** aggregated expression info ****
     agg_expr_path <- NULL
     if (isTRUE(h5_expression)) { # h5 expression matrix loading is default
         agg_expr_path <- dir_items$`cell feature matrix`[grepl(
-            pattern = "h5", dir_items$`cell feature matrix`)]
+            pattern = "h5", dir_items$`cell feature matrix`
+        )]
     } else if (load_format == "zarr") {
         agg_expr_path <- dir_items$`cell feature matrix`[grepl(
-            pattern = "zarr", dir_items$`cell feature matrix`)]
+            pattern = "zarr", dir_items$`cell feature matrix`
+        )]
     } else { # No parquet for aggregated expression - default to normal 10x loading
         agg_expr_path <- dir_items$`cell feature matrix`[sapply(
-            dir_items$`cell feature matrix`, function(x) file_test(op = "-d", x))]
+            dir_items$`cell feature matrix`, function(x) file_test(op = "-d", x)
+        )]
         if (length(agg_expr_path) == 0) {
             stop(wrap_txt(
                 "Expression matrix cannot be loaded.\n
-                Has cell_feature_matrix(.tar.gz) been unpacked into a 
+                Has cell_feature_matrix(.tar.gz) been unpacked into a
                 directory?"
             ))
         }
@@ -2300,7 +2405,7 @@ createGiottoXeniumObject <- function(xenium_dir,
 
 #' @title Load MERSCOPE data from folder
 #' @name load_merscope_folder
-#' @param dir_items list of full filepaths from 
+#' @param dir_items list of full filepaths from
 #' \code{\link{.read_merscope_folder}}
 #' @inheritParams createGiottoMerscopeObject
 #' @returns list of loaded-in MERSCOPE data
@@ -2308,12 +2413,13 @@ NULL
 
 #' @rdname load_merscope_folder
 #' @keywords internal
-.load_merscope_folder <- function(dir_items,
-    data_to_use,
-    fovs = NULL,
-    poly_z_indices = 1L:7L,
-    cores = NA,
-    verbose = TRUE) {
+.load_merscope_folder <- function(
+        dir_items,
+        data_to_use,
+        fovs = NULL,
+        poly_z_indices = 1L:7L,
+        cores = NA,
+        verbose = TRUE) {
     # 1. load data_to_use-specific
     if (data_to_use == "subcellular") {
         data_list <- .load_merscope_folder_subcellular(
@@ -2332,17 +2438,22 @@ NULL
             verbose = verbose
         )
     } else {
-        stop(wrap_txt('data_to_use "', data_to_use, 
-                      '" not implemented', sep = ""))
+        stop(wrap_txt('data_to_use "', data_to_use,
+            '" not implemented',
+            sep = ""
+        ))
     }
 
     # 2. Load images if available
     if (!is.null(dir_items$`image info`)) {
         ## micron to px scaling factor
         micronToPixelScale <- Sys.glob(paths = file.path(
-            dir_items$`image info`, "*micron_to_mosaic_pixel_transform*"))[[1]]
+            dir_items$`image info`, "*micron_to_mosaic_pixel_transform*"
+        ))[[1]]
         micronToPixelScale <- data.table::fread(
-            micronToPixelScale, nThread = cores)
+            micronToPixelScale,
+            nThread = cores
+        )
         # add to data_list
         data_list$micronToPixelScale <- micronToPixelScale
 
@@ -2350,14 +2461,17 @@ NULL
         ## determine types of stains
         images_filenames <- list.files(dir_items$`image info`)
         bound_stains_filenames <- images_filenames[
-            grep(pattern = ".tif", images_filenames)]
+            grep(pattern = ".tif", images_filenames)
+        ]
         bound_stains_types <- sapply(strsplit(
-            bound_stains_filenames, "_"), `[`, 2)
+            bound_stains_filenames, "_"
+        ), `[`, 2)
         bound_stains_types <- unique(bound_stains_types)
 
         img_list <- lapply_flex(bound_stains_types, function(stype) {
             img_paths <- Sys.glob(paths = file.path(
-                dir_items$`image info`, paste0("*", stype, "*")))
+                dir_items$`image info`, paste0("*", stype, "*")
+            ))
 
             lapply_flex(img_paths, function(img) {
                 createGiottoLargeImage(raster_object = img)
@@ -2376,16 +2490,19 @@ NULL
 
 #' @describeIn load_merscope_folder Load items for 'subcellular' workflow
 #' @keywords internal
-.load_merscope_folder_subcellular <- function(dir_items,
-    data_to_use,
-    cores = NA,
-    poly_z_indices = 1L:7L,
-    verbose = TRUE,
-    fovs = NULL) {
+.load_merscope_folder_subcellular <- function(
+        dir_items,
+        data_to_use,
+        cores = NA,
+        poly_z_indices = 1L:7L,
+        verbose = TRUE,
+        fovs = NULL) {
     if (isTRUE(verbose)) message("Loading transcript level info...")
     if (is.null(fovs)) {
         tx_dt <- data.table::fread(
-            dir_items$`raw transcript info`, nThread = cores)
+            dir_items$`raw transcript info`,
+            nThread = cores
+        )
     } else {
         message("Selecting FOV subset transcripts")
         tx_dt <- fread_colmatch(
@@ -2398,7 +2515,8 @@ NULL
     }
     tx_dt[, c("x", "y") := NULL] # remove unneeded cols
     data.table::setcolorder(
-        tx_dt, c("gene", "global_x", "global_y", "global_z"))
+        tx_dt, c("gene", "global_x", "global_y", "global_z")
+    )
 
     if (isTRUE(verbose)) message("Loading polygon info...")
     poly_info <- readPolygonFilesVizgenHDF5(
@@ -2422,18 +2540,23 @@ NULL
 
 #' @describeIn load_merscope_folder Load items for 'aggregate' workflow
 #' @keywords internal
-.load_merscope_folder_aggregate <- function(dir_items,
-    data_to_use,
-    cores = NA,
-    verbose = TRUE) {
+.load_merscope_folder_aggregate <- function(
+        dir_items,
+        data_to_use,
+        cores = NA,
+        verbose = TRUE) {
     # metadata is polygon-related measurements
     vmsg("Loading cell metadata...", .v = verbose)
     cell_metadata_file <- data.table::fread(
-        dir_items$`cell metadata`, nThread = cores)
+        dir_items$`cell metadata`,
+        nThread = cores
+    )
 
     vmsg("Loading expression matrix", .v = verbose)
     expr_dt <- data.table::fread(
-        dir_items$`cell feature matrix`, nThread = cores)
+        dir_items$`cell feature matrix`,
+        nThread = cores
+    )
 
 
     data_list <- list(
@@ -2457,15 +2580,16 @@ NULL
 #' @title Load CosMx folder subcellular info
 #' @name .load_cosmx_folder_subcellular
 #' @description loads in the feature detections information. Note that the mask
-#' images are still required for a working subcellular object, and those are 
+#' images are still required for a working subcellular object, and those are
 #' loaded in \code{\link{.createGiottoCosMxObject_subcellular}}
 #' @inheritParams createGiottoCosMxObject
 #' @returns list
 #' @keywords internal
-.load_cosmx_folder_subcellular <- function(dir_items,
-    FOVs = NULL,
-    cores,
-    verbose = TRUE) {
+.load_cosmx_folder_subcellular <- function(
+        dir_items,
+        FOVs = NULL,
+        cores,
+        verbose = TRUE) {
     vmsg(.v = verbose, "Loading subcellular information...")
 
     # subcellular checks
@@ -2479,7 +2603,8 @@ NULL
     # FOVs to load
     vmsg(.v = verbose, "Loading FOV offsets...")
     fov_offset_file <- fread(
-        input = dir_items$`fov positions file`, nThread = cores)
+        input = dir_items$`fov positions file`, nThread = cores
+    )
     if (is.null(FOVs)) FOVs <- fov_offset_file$fov # default to ALL FOVs
     FOV_ID <- as.list(sprintf("%03d", FOVs))
 
@@ -2487,7 +2612,8 @@ NULL
 
     vmsg(.v = verbose, "Loading transcript level info...")
     tx_coord_all <- fread(
-        input = dir_items$`transcript locations file`, nThread = cores)
+        input = dir_items$`transcript locations file`, nThread = cores
+    )
     vmsg(.v = verbose, "Subcellular load done")
 
     data_list <- list(
@@ -2506,11 +2632,12 @@ NULL
 #' @inheritParams createGiottoCosMxObject
 #' @returns list
 #' @keywords internal
-.load_cosmx_folder_aggregate <- function(dir_items,
-    cores,
-    verbose = TRUE) {
+.load_cosmx_folder_aggregate <- function(
+        dir_items,
+        cores,
+        verbose = TRUE) {
     # data.table vars
-    fov <- cell_ID <- fov_cell_ID <- CenterX_global_px <- 
+    fov <- cell_ID <- fov_cell_ID <- CenterX_global_px <-
         CenterY_global_px <- CenterX_local_px <-
         CenterY_local_px <- x_shift <- y_shift <- NULL
 
@@ -2518,15 +2645,18 @@ NULL
     vmsg(.v = verbose, "Loading provided aggregated information...")
 
     # aggregate checks
-    if (!file.exists(dir_items$`expression matrix file`)) 
+    if (!file.exists(dir_items$`expression matrix file`)) {
         stop(wrap_txt("No expression matrix file (.csv) detected"))
-    if (!file.exists(dir_items$`metadata file`)) 
-        stop(wrap_txt("No metadata file (.csv) detected. Needed for cell 
+    }
+    if (!file.exists(dir_items$`metadata file`)) {
+        stop(wrap_txt("No metadata file (.csv) detected. Needed for cell
                       spatial locations."))
+    }
 
     # read in aggregate data
     expr_mat <- fread(
-        input = dir_items$`expression matrix file`, nThread = cores)
+        input = dir_items$`expression matrix file`, nThread = cores
+    )
     metadata <- fread(input = dir_items$`metadata file`, nThread = cores)
 
     # setorder expression and spatlocs
@@ -2536,12 +2666,14 @@ NULL
 
     # generate unique cell IDs
     expr_mat[, cell_ID := paste0(
-        "fov", sprintf("%03d", fov), "-", "cell_", cell_ID)]
+        "fov", sprintf("%03d", fov), "-", "cell_", cell_ID
+    )]
     expr_mat <- expr_mat[, fov := NULL]
 
     metadata[, fov_cell_ID := cell_ID]
     metadata[, cell_ID := paste0(
-        "fov", sprintf("%03d", fov), "-", "cell_", cell_ID)]
+        "fov", sprintf("%03d", fov), "-", "cell_", cell_ID
+    )]
     # reorder
     data.table::setcolorder(x = metadata, c("cell_ID", "fov", "fov_cell_ID"))
 
@@ -2562,11 +2694,15 @@ NULL
     spatloc_newnames <- c("sdimx", "sdimy", "cell_ID")
     data.table::setnames(spatlocs, old = spatloc_oldnames, new = spatloc_newnames)
     data.table::setnames(
-        spatlocs_fov, old = spatloc_oldnames_fov, new = spatloc_newnames)
+        spatlocs_fov,
+        old = spatloc_oldnames_fov, new = spatloc_newnames
+    )
 
     # cleanup metadata and spatlocs
-    metadata <- metadata[, c("CenterX_global_px", "CenterY_global_px", 
-                            "CenterX_local_px", "CenterY_local_px") := NULL]
+    metadata <- metadata[, c(
+        "CenterX_global_px", "CenterY_global_px",
+        "CenterX_local_px", "CenterY_local_px"
+    ) := NULL]
     # find unique cell_IDs present in both expression and metadata
     giotto_cell_ID <- unique(intersect(expr_mat$cell_ID, metadata$cell_ID))
 
@@ -2579,27 +2715,35 @@ NULL
     # take all mean intensity protein information except for MembraneStain and DAPI
     protein_meta_cols <- colnames(metadata)
     protein_meta_cols <- protein_meta_cols[
-        grepl(pattern = "Mean.*", x = protein_meta_cols)]
+        grepl(pattern = "Mean.*", x = protein_meta_cols)
+    ]
     protein_meta_cols <- protein_meta_cols[
-        !protein_meta_cols %in% c("Mean.MembraneStain", "Mean.DAPI")]
+        !protein_meta_cols %in% c("Mean.MembraneStain", "Mean.DAPI")
+    ]
     protein_meta_cols <- c("cell_ID", protein_meta_cols)
 
     prot_expr <- metadata[, protein_meta_cols, with = FALSE]
     prot_cell_ID <- metadata[, cell_ID]
-    protM <- Matrix::Matrix(as.matrix(prot_expr[, -1]), 
-                            dimnames = list(prot_expr[[1]], 
-                                            colnames(prot_expr[, -1])),
-                            sparse = FALSE)
+    protM <- Matrix::Matrix(as.matrix(prot_expr[, -1]),
+        dimnames = list(
+            prot_expr[[1]],
+            colnames(prot_expr[, -1])
+        ),
+        sparse = FALSE
+    )
     protM <- t_flex(protM)
 
     # convert expression to sparse matrix
-    spM <- Matrix::Matrix(as.matrix(expr_mat[, -1]), 
-                          dimnames = list(expr_mat[[1]], 
-                                          colnames(expr_mat[, -1])), 
-                          sparse = TRUE)
+    spM <- Matrix::Matrix(as.matrix(expr_mat[, -1]),
+        dimnames = list(
+            expr_mat[[1]],
+            colnames(expr_mat[, -1])
+        ),
+        sparse = TRUE
+    )
     spM <- t_flex(spM)
 
-    ## Ready for downstream aggregate gobject creation or appending into 
+    ## Ready for downstream aggregate gobject creation or appending into
     # existing subcellular Giotto object ##
 
     data_list <- list(
@@ -2631,14 +2775,15 @@ NULL
 
 #' @rdname load_xenium_folder
 #' @keywords internal
-.load_xenium_folder <- function(path_list,
-    load_format = "csv",
-    data_to_use = "subcellular",
-    h5_expression = "FALSE",
-    h5_gene_ids = "symbols",
-    gene_column_index = 1,
-    cores,
-    verbose = TRUE) {
+.load_xenium_folder <- function(
+        path_list,
+        load_format = "csv",
+        data_to_use = "subcellular",
+        h5_expression = "FALSE",
+        h5_gene_ids = "symbols",
+        gene_column_index = 1,
+        cores,
+        verbose = TRUE) {
     data_list <- switch(load_format,
         "csv" = .load_xenium_folder_csv(
             path_list = path_list,
@@ -2667,13 +2812,14 @@ NULL
 
 #' @describeIn load_xenium_folder Load from csv files
 #' @keywords internal
-.load_xenium_folder_csv <- function(path_list,
-    cores,
-    data_to_use = "subcellular",
-    h5_expression = FALSE,
-    h5_gene_ids = "symbols",
-    gene_column_index = 1,
-    verbose = TRUE) {
+.load_xenium_folder_csv <- function(
+        path_list,
+        cores,
+        data_to_use = "subcellular",
+        h5_expression = FALSE,
+        h5_gene_ids = "symbols",
+        gene_column_index = 1,
+        verbose = TRUE) {
     # initialize return vars
     feat_meta <- tx_dt <- bound_dt_list <- cell_meta <- agg_expr <- NULL
 
@@ -2682,8 +2828,10 @@ NULL
     fdata_path <- path_list$panel_meta_path[[1]]
     fdata_ext <- GiottoUtils::file_extension(fdata_path)
     if ("json" %in% fdata_ext) {
-        feat_meta <- .load_xenium_panel_json(path = fdata_path, 
-                                            gene_ids = h5_gene_ids)
+        feat_meta <- .load_xenium_panel_json(
+            path = fdata_path,
+            gene_ids = h5_gene_ids
+        )
     } else {
         feat_meta <- data.table::fread(fdata_path, nThread = cores)
         colnames(feat_meta)[[1]] <- "feat_ID"
@@ -2715,7 +2863,9 @@ NULL
         }
         colnames(features_dt) <- c("id", "feat_ID", "feat_class")
         feat_meta <- merge(
-            features_dt[, c(2, 3)], feat_meta, all.x = TRUE, by = "feat_ID")
+            features_dt[, c(2, 3)], feat_meta,
+            all.x = TRUE, by = "feat_ID"
+        )
 
         GiottoUtils::vmsg("Loading transcript level info...", .v = verbose)
         tx_dt <- data.table::fread(path_list$tx_path[[1]], nThread = cores)
@@ -2735,7 +2885,9 @@ NULL
     # **** aggregate info ****
     GiottoUtils::vmsg("loading cell metadata...", .v = verbose)
     cell_meta <- data.table::fread(
-        path_list$cell_meta_path[[1]], nThread = cores)
+        path_list$cell_meta_path[[1]],
+        nThread = cores
+    )
 
     if (data_to_use == "aggregate") {
         GiottoUtils::vmsg("Loading aggregated expression...", .v = verbose)
@@ -2772,13 +2924,14 @@ NULL
 
 #' @describeIn load_xenium_folder Load from parquet files
 #' @keywords internal
-.load_xenium_folder_parquet <- function(path_list,
-    cores,
-    data_to_use = "subcellular",
-    h5_expression = FALSE,
-    h5_gene_ids = "symbols",
-    gene_column_index = 1,
-    verbose = TRUE) {
+.load_xenium_folder_parquet <- function(
+        path_list,
+        cores,
+        data_to_use = "subcellular",
+        h5_expression = FALSE,
+        h5_gene_ids = "symbols",
+        gene_column_index = 1,
+        verbose = TRUE) {
     # initialize return vars
     feat_meta <- tx_dt <- bound_dt_list <- cell_meta <- agg_expr <- NULL
     # dplyr variable
@@ -2790,7 +2943,8 @@ NULL
     fdata_ext <- GiottoUtils::file_extension(fdata_path)
     if ("json" %in% fdata_ext) {
         feat_meta <- .load_xenium_panel_json(
-            path = fdata_path, gene_ids = h5_gene_ids)
+            path = fdata_path, gene_ids = h5_gene_ids
+        )
     } else {
         feat_meta <- data.table::fread(fdata_path, nThread = cores)
         colnames(feat_meta)[[1]] <- "feat_ID"
@@ -2818,15 +2972,18 @@ NULL
                 h5$close_all()
             })
         } else {
-            features_dt <- arrow::read_tsv_arrow(paste0(
-                path_list$agg_expr_path, "/features.tsv.gz"),
+            features_dt <- arrow::read_tsv_arrow(
+                paste0(
+                    path_list$agg_expr_path, "/features.tsv.gz"
+                ),
                 col_names = FALSE
             ) %>%
                 data.table::setDT()
         }
         colnames(features_dt) <- c("id", "feat_ID", "feat_class")
         feat_meta <- merge(features_dt[
-            , c(2, 3)], feat_meta, all.x = TRUE, by = "feat_ID")
+            , c(2, 3)
+        ], feat_meta, all.x = TRUE, by = "feat_ID")
 
         vmsg("Loading transcript level info...", .v = verbose)
         tx_dt <- arrow::read_parquet(
@@ -2834,10 +2991,12 @@ NULL
             as_data_frame = FALSE
         ) %>%
             dplyr::mutate(
-                transcript_id = cast(transcript_id, arrow::string())) %>%
+                transcript_id = cast(transcript_id, arrow::string())
+            ) %>%
             dplyr::mutate(cell_id = cast(cell_id, arrow::string())) %>%
             dplyr::mutate(
-                feature_name = cast(feature_name, arrow::string())) %>%
+                feature_name = cast(feature_name, arrow::string())
+            ) %>%
             as.data.frame() %>%
             data.table::setDT()
         data.table::setnames(
@@ -2939,47 +3098,48 @@ NULL
 #' (ii) fragment files, or (iii) bam files.
 #' @param genome A string indicating the default genome to be used for all ArchR
 #' functions. Currently supported values include "hg19","hg38","mm9", and "mm10".
-#' This value is stored as a global environment variable, not part of the 
+#' This value is stored as a global environment variable, not part of the
 #' ArchRProject.
 #' This can be overwritten on a per-function basis using the given function's
 #' geneAnnotationand genomeAnnotation parameter. For something other than one of
-#' the currently supported, see createGeneAnnnotation() and 
+#' the currently supported, see createGeneAnnnotation() and
 #' createGenomeAnnnotation()
-#' @param createArrowFiles_params list of parameters passed to 
+#' @param createArrowFiles_params list of parameters passed to
 #' `ArchR::createArrowFiles`
 #' @param ArchRProject_params list of parameters passed to `ArchR::ArchRProject`
-#' @param addIterativeLSI_params list of parameters passed to 
+#' @param addIterativeLSI_params list of parameters passed to
 #' `ArchR::addIterativeLSI`
 #' @param threads number of threads to use. Default = `ArchR::getArchRThreads()`
 #' @param force Default = FALSE
 #' @param verbose Default = TRUE
 #'
-#' @returns An ArchR project with GeneScoreMatrix, TileMatrix, and 
+#' @returns An ArchR project with GeneScoreMatrix, TileMatrix, and
 #' TileMatrix-based LSI
 #' @export
-createArchRProj <- function(fragmentsPath,
-    genome = c("hg19", "hg38", "mm9", "mm10"),
-    createArrowFiles_params = list(
-        sampleNames = "sample1",
-        minTSS = 0,
-        minFrags = 0,
-        maxFrags = 1e+07,
-        minFragSize = 10,
-        maxFragSize = 2000,
-        offsetPlus = 0,
-        offsetMinus = 0,
-        TileMatParams = list(tileSize = 5000)
-    ),
-    ArchRProject_params = list(
-        outputDirectory = getwd(),
-        copyArrows = FALSE
-    ),
-    addIterativeLSI_params = list(),
-    threads = ArchR::getArchRThreads(),
-    force = FALSE,
-    verbose = TRUE) {
+createArchRProj <- function(
+        fragmentsPath,
+        genome = c("hg19", "hg38", "mm9", "mm10"),
+        createArrowFiles_params = list(
+            sampleNames = "sample1",
+            minTSS = 0,
+            minFrags = 0,
+            maxFrags = 1e+07,
+            minFragSize = 10,
+            maxFragSize = 2000,
+            offsetPlus = 0,
+            offsetMinus = 0,
+            TileMatParams = list(tileSize = 5000)
+        ),
+        ArchRProject_params = list(
+            outputDirectory = getwd(),
+            copyArrows = FALSE
+        ),
+        addIterativeLSI_params = list(),
+        threads = ArchR::getArchRThreads(),
+        force = FALSE,
+        verbose = TRUE) {
     if (!requireNamespace("ArchR")) {
-        message('ArchR is needed. Install the package using 
+        message('ArchR is needed. Install the package using
                 remotes::install_github("GreenleafLab/ArchR")')
     }
 
@@ -3029,27 +3189,32 @@ createArchRProj <- function(fragmentsPath,
 #' @param archRproj ArchR project
 #' @param expression expression information
 #' @param expression_feat Giotto object available features (e.g. atac, rna, ...)
-#' @param spatial_locs data.table or data.frame with coordinates for cell 
+#' @param spatial_locs data.table or data.frame with coordinates for cell
 #' centroids
-#' @param sampleNames A character vector containing the ArchR project sample 
+#' @param sampleNames A character vector containing the ArchR project sample
 #' name
 #' @param ... additional arguments passed to `createGiottoObject`
 #'
 #' @returns A Giotto object with at least an atac or epigenetic modality
 #'
 #' @export
-createGiottoObjectfromArchR <- function(archRproj,
-    expression = NULL,
-    expression_feat = "atac",
-    spatial_locs = NULL,
-    sampleNames = "sample1",
-    ...) {
+createGiottoObjectfromArchR <- function(
+        archRproj,
+        expression = NULL,
+        expression_feat = "atac",
+        spatial_locs = NULL,
+        sampleNames = "sample1",
+        ...) {
     # extract GeneScoreMatrix
     GeneScoreMatrix_summarizedExperiment <- ArchR::getMatrixFromProject(
-        archRproj)
-    GeneScoreMatrix <- slot(slot(
-        GeneScoreMatrix_summarizedExperiment, "assays"), 
-        "data")[["GeneScoreMatrix"]]
+        archRproj
+    )
+    GeneScoreMatrix <- slot(
+        slot(
+            GeneScoreMatrix_summarizedExperiment, "assays"
+        ),
+        "data"
+    )[["GeneScoreMatrix"]]
 
     ## get cell names
     cell_names <- colnames(GeneScoreMatrix)
@@ -3057,8 +3222,10 @@ createGiottoObjectfromArchR <- function(archRproj,
     cell_names <- gsub("-1", "", cell_names)
 
     ## get gene names
-    gene_names <- slot(GeneScoreMatrix_summarizedExperiment, 
-                      "elementMetadata")[["name"]]
+    gene_names <- slot(
+        GeneScoreMatrix_summarizedExperiment,
+        "elementMetadata"
+    )[["name"]]
 
     ## replace colnames with cell names
     colnames(GeneScoreMatrix) <- cell_names
diff --git a/R/cross_section.R b/R/cross_section.R
index f1fac6960..ff93ccaf2 100644
--- a/R/cross_section.R
+++ b/R/cross_section.R
@@ -8,43 +8,44 @@
 #' @description create a crossSection object
 #' @param name name of cross section object. (default = cross_section)
 #' @param method method to define the cross section plane.
-#' @param thickness_unit unit of the virtual section thickness. If "cell", 
-#' average size of the observed cells is used as length unit. If "natural", 
+#' @param thickness_unit unit of the virtual section thickness. If "cell",
+#' average size of the observed cells is used as length unit. If "natural",
 #' the unit of cell location coordinates is used.(default = cell)
 #' @param slice_thickness thickness of slice
-#' @param cell_distance_estimate_method method to estimate average distance 
+#' @param cell_distance_estimate_method method to estimate average distance
 #' between neighboring cells. (default = mean)
-#' @param extend_ratio deciding the span of the cross section meshgrid, as a 
-#' ratio of extension compared to the borders of the virtual tissue section. 
+#' @param extend_ratio deciding the span of the cross section meshgrid, as a
+#' ratio of extension compared to the borders of the virtual tissue section.
 #' (default = 0.2)
-#' @param plane_equation a numerical vector of length 4, in the form of 
+#' @param plane_equation a numerical vector of length 4, in the form of
 #' c(A,B,C,D), which defines plane Ax+By+Cz=D.
-#' @param mesh_grid_n number of meshgrid lines to generate along both 
+#' @param mesh_grid_n number of meshgrid lines to generate along both
 #' directions for the cross section plane.
 #' @param mesh_obj object that stores the cross section meshgrid information.
 #' @param cell_subset cells selected by the cross section
-#' @param cell_subset_spatial_locations locations of cells selected by the 
+#' @param cell_subset_spatial_locations locations of cells selected by the
 #' cross section
-#' @param cell_subset_projection_locations 3D projection coordinates of 
+#' @param cell_subset_projection_locations 3D projection coordinates of
 #' selected cells onto the cross section plane
 #' @param cell_subset_projection_PCA pca of projection coordinates
-#' @param cell_subset_projection_coords 2D PCA coordinates of selected cells 
+#' @param cell_subset_projection_coords 2D PCA coordinates of selected cells
 #' in the cross section plane
 #' @returns crossSection object
-create_crossSection_object <- function(name = NULL,
-    method = NULL,
-    thickness_unit = NULL,
-    slice_thickness = NULL,
-    cell_distance_estimate_method = NULL,
-    extend_ratio = NULL,
-    plane_equation = NULL,
-    mesh_grid_n = NULL,
-    mesh_obj = NULL,
-    cell_subset = NULL,
-    cell_subset_spatial_locations = NULL,
-    cell_subset_projection_locations = NULL,
-    cell_subset_projection_PCA = NULL,
-    cell_subset_projection_coords = NULL) {
+create_crossSection_object <- function(
+        name = NULL,
+        method = NULL,
+        thickness_unit = NULL,
+        slice_thickness = NULL,
+        cell_distance_estimate_method = NULL,
+        extend_ratio = NULL,
+        plane_equation = NULL,
+        mesh_grid_n = NULL,
+        mesh_obj = NULL,
+        cell_subset = NULL,
+        cell_subset_spatial_locations = NULL,
+        cell_subset_projection_locations = NULL,
+        cell_subset_projection_PCA = NULL,
+        cell_subset_projection_coords = NULL) {
     crossSection_obj <- list(
         "method" = method,
         "thickness_unit" = thickness_unit,
@@ -64,42 +65,58 @@ create_crossSection_object <- function(name = NULL,
 #' @name read_crossSection
 #' @description read a cross section object from a giotto object
 #' @param gobject gobject
+#' @param spat_unit spatial unit
 #' @param name name
 #' @param spatial_network_name spatial_network_name
 #' @returns crossSectionObjects
 #' @keywords internal
-read_crossSection <- function(gobject,
-    name = NULL,
-    spatial_network_name = NULL) {
+read_crossSection <- function(
+        gobject,
+        spat_unit = NULL,
+        name = NULL,
+        spatial_network_name = NULL) {
+    spat_unit <- set_default_spat_unit(
+        gobject = gobject, spat_unit = spat_unit
+    )
+
     if (is.null(spatial_network_name)) {
         stop("spatial_network_name is not specified.")
-    } else if (!is.element(
-        spatial_network_name, names(slot(gobject, "spatial_network")))) {
-        stop(paste0(spatial_network_name, " has not been created."))
-    } else {
-        sp_network_obj <- get_spatialNetwork(gobject,
-            name = spatial_network_name,
-            output = "spatialNetworkObj"
+    }
+
+    sn <- getSpatialNetwork(
+        gobject = gobject,
+        spat_unit = spat_unit,
+        name = spatial_network_name,
+        set_defaults = FALSE,
+        copy_obj = FALSE,
+        verbose = FALSE,
+        output = "spatialNetworkObj"
+    )
+
+    cs_list <- slot(sn, "crossSectionObjects")
+
+    if (length(cs_list) == 0L) {
+        stop("No cross section object has been created.")
+    }
+
+    if (is.null(name)) {
+        name <- names(cs_list)[length(cs_list)]
+
+        default_name_msg <- sprintf(
+            "cross section object is not specified, \n%s \n'%s'",
+            "reading the last one from the existing list:", name
         )
-        if (length(slot(sp_network_obj, "crossSectionObjects")) == 0) {
-            stop("No cross section object has been created.")
-        } else if (is.null(name)) {
-            sprintf(
-                "cross section object is not specified, reading the last one %s 
-                from the existing list",
-                names(slot(sp_network_obj, "crossSectionObjects"))[
-                    length(slot(sp_network_obj, "crossSectionObjects"))]
-            )
-            crossSection_obj <- slot(sp_network_obj, "crossSectionObjects")[[
-                length(slot(sp_network_obj, "crossSectionObjects"))]]
-        } else if (!is.element(name, names(slot(
-            sp_network_obj, "crossSectionObjects")))) {
-            stop(paste0(name, " has not been created."))
-        } else {
-            crossSection_obj <- slot(
-                sp_network_obj, "crossSectionObjects")[[name]]
-        }
     }
+
+    if (!name %in% names(cs_list)) {
+        stop(sprintf(
+            "crossSectionObject '%s' has not been created.",
+            name
+        ))
+    }
+
+    crossSection_obj <- cs_list[[name]]
+
     return(crossSection_obj)
 }
 
@@ -109,49 +126,69 @@ read_crossSection <- function(gobject,
 #' @name estimateCellCellDistance
 #' @description estimate average distance between neighboring cells
 #' @param gobject gobject
+#' @param spat_unit spatial unit
 #' @param spatial_network_name spatial_network_name
 #' @param method method
 #' @returns matrix
 #' @keywords internal
-estimateCellCellDistance <- function(gobject,
-    spatial_network_name = "Delaunay_network",
-    method = c("mean", "median")) {
-    delaunay_network_DT <- gobject@spatial_network[["thickness_unit"]][[
-        spatial_network_name]]@networkDT
+estimateCellCellDistance <- function(
+        gobject,
+        spat_unit = NULL,
+        spatial_network_name = "Delaunay_network",
+        method = c("mean", "median")) {
+    spat_unit <- set_default_spat_unit(
+        gobject = gobject, spat_unit = spat_unit
+    )
+
+    net <- getSpatialNetwork(
+        gobject = gobject,
+        spat_unit = spat_unit,
+        name = spatial_network_name,
+        output = "networkDT"
+    )
 
     CellCellDistance <- get_distance(
-        networkDT = delaunay_network_DT,
+        networkDT = net,
         method = method
     )
+
     return(CellCellDistance)
 }
 #' @title get_sectionThickness
 #' @name get_sectionThickness
 #' @description get section thickness
 #' @param gobject gobject
+#' @param spat_unit spatial unit
 #' @param thickness_unit thickness_unit
 #' @param spatial_network_name spatial_network_name
 #' @param cell_distance_estimate_method cell_distance_estimate_method
 #' @param plane_equation plane_equation
 #' @returns numeric
 #' @keywords internal
-get_sectionThickness <- function(gobject, thickness_unit = c("cell", "natural"),
-    slice_thickness = 2,
-    spatial_network_name = "Delaunay_network",
-    cell_distance_estimate_method = c("mean", "median"),
-    plane_equation = NULL) {
+get_sectionThickness <- function(
+        gobject,
+        spat_unit = NULL,
+        thickness_unit = c("cell", "natural"),
+        slice_thickness = 2,
+        spatial_network_name = "Delaunay_network",
+        cell_distance_estimate_method = c("mean", "median"),
+        plane_equation = NULL) {
     thickness_unit <- match.arg(thickness_unit, c("cell", "natural"))
 
-    if (thickness_unit == "cell") {
-        CellCellDistance <- estimateCellCellDistance(gobject,
-            method = cell_distance_estimate_method,
-            spatial_network_name = spatial_network_name
-        )
-        sectionThickness <- CellCellDistance * slice_thickness
-    } else if (thickness_unit == "natural") {
-        sectionThickness <- slice_thickness
-    }
-    return(sectionThickness)
+    section_thickness <- switch(thickness_unit,
+        "cell" = {
+            CellCellDistance <- estimateCellCellDistance(
+                gobject = gobject,
+                spat_unit = spat_unit,
+                method = cell_distance_estimate_method,
+                spatial_network_name = spatial_network_name
+            )
+            CellCellDistance * slice_thickness
+        },
+        "natural" = slice_thickness
+    )
+
+    return(section_thickness)
 }
 
 #' @title projection_fun
@@ -182,7 +219,7 @@ projection_fun <- function(point_to_project, plane_point, plane_norm) {
 
 #' @title adapt_aspect_ratio
 #' @name adapt_aspect_ratio
-#' @description adapt the aspact ratio after inserting cross section mesh grid 
+#' @description adapt the aspact ratio after inserting cross section mesh grid
 #' lines
 #' @param current_ratio current_ratio
 #' @param cell_locations cell_locations
@@ -192,19 +229,23 @@ projection_fun <- function(point_to_project, plane_point, plane_norm) {
 #' @param mesh_obj mesh_obj
 #' @returns numeric
 #' @keywords internal
-adapt_aspect_ratio <- function(current_ratio, cell_locations,
-    sdimx = NULL, sdimy = NULL, sdimz = NULL,
-    mesh_obj = NULL) {
+adapt_aspect_ratio <- function(
+        current_ratio, cell_locations,
+        sdimx = NULL, sdimy = NULL, sdimz = NULL,
+        mesh_obj = NULL) {
     x_range <- max(cell_locations[[sdimx]]) - min(cell_locations[[sdimx]])
     y_range <- max(cell_locations[[sdimy]]) - min(cell_locations[[sdimy]])
     z_range <- max(cell_locations[[sdimz]]) - min(cell_locations[[sdimz]])
 
     x_mesh_range <- max(mesh_obj$mesh_grid_lines$mesh_grid_lines_X) - min(
-        mesh_obj$mesh_grid_lines$mesh_grid_lines_X)
+        mesh_obj$mesh_grid_lines$mesh_grid_lines_X
+    )
     y_mesh_range <- max(mesh_obj$mesh_grid_lines$mesh_grid_lines_Y) - min(
-        mesh_obj$mesh_grid_lines$mesh_grid_lines_Y)
+        mesh_obj$mesh_grid_lines$mesh_grid_lines_Y
+    )
     z_mesh_range <- max(mesh_obj$mesh_grid_lines$mesh_grid_lines_Z) - min(
-        mesh_obj$mesh_grid_lines$mesh_grid_lines_Z)
+        mesh_obj$mesh_grid_lines$mesh_grid_lines_Z
+    )
 
     if (x_mesh_range > x_range) {
         x_adapt <- x_mesh_range / x_range
@@ -223,7 +264,8 @@ adapt_aspect_ratio <- function(current_ratio, cell_locations,
     }
 
     new_ratio <- as.numeric(current_ratio) * c(
-        as.numeric(x_adapt), as.numeric(y_adapt), as.numeric(z_adapt))
+        as.numeric(x_adapt), as.numeric(y_adapt), as.numeric(z_adapt)
+    )
     new_ratio <- new_ratio / min(new_ratio)
     return(new_ratio)
 }
@@ -278,8 +320,7 @@ find_x_y_ranges <- function(data, extend_ratio) {
 #' @param mesh_grid_n mesh_grid_n
 #' @returns 2d mesh grid line object
 #' @keywords internal
-create_2d_mesh_grid_line_obj <- function(
-        x_min, x_max, y_min, y_max, mesh_grid_n) {
+create_2d_mesh_grid_line_obj <- function(x_min, x_max, y_min, y_max, mesh_grid_n) {
     x_grid <- seq(x_min, x_max, length.out = mesh_grid_n)
     y_grid <- seq(y_min, y_max, length.out = mesh_grid_n)
 
@@ -333,9 +374,13 @@ reshape_to_data_point <- function(mesh_grid_obj) {
 reshape_to_mesh_grid_obj <- function(data_points, mesh_grid_n) {
     if (dim(data_points)[2] == 2) {
         mesh_grid_lines_X <- matrix(
-            data_points[, 1], nrow = mesh_grid_n, byrow = FALSE)
+            data_points[, 1],
+            nrow = mesh_grid_n, byrow = FALSE
+        )
         mesh_grid_lines_Y <- matrix(
-            data_points[, 2], nrow = mesh_grid_n, byrow = FALSE)
+            data_points[, 2],
+            nrow = mesh_grid_n, byrow = FALSE
+        )
 
         mesh_grid_obj <- list(
             "mesh_grid_lines_X" = mesh_grid_lines_X,
@@ -343,11 +388,17 @@ reshape_to_mesh_grid_obj <- function(data_points, mesh_grid_n) {
         )
     } else if (dim(data_points)[2] == 3) {
         mesh_grid_lines_X <- matrix(
-            data_points[, 1], nrow = mesh_grid_n, byrow = FALSE)
+            data_points[, 1],
+            nrow = mesh_grid_n, byrow = FALSE
+        )
         mesh_grid_lines_Y <- matrix(
-            data_points[, 2], nrow = mesh_grid_n, byrow = FALSE)
+            data_points[, 2],
+            nrow = mesh_grid_n, byrow = FALSE
+        )
         mesh_grid_lines_Z <- matrix(
-            data_points[, 3], nrow = mesh_grid_n, byrow = FALSE)
+            data_points[, 3],
+            nrow = mesh_grid_n, byrow = FALSE
+        )
         mesh_grid_obj <- list(
             "mesh_grid_lines_X" = mesh_grid_lines_X,
             "mesh_grid_lines_Y" = mesh_grid_lines_Y,
@@ -367,17 +418,19 @@ reshape_to_mesh_grid_obj <- function(data_points, mesh_grid_n) {
 #' @param mesh_grid_n mesh_grid_n
 #' @returns 3d mesh
 #' @keywords internal
-transform_2d_mesh_to_3d_mesh <- function(
-        mesh_line_obj_2d, pca_out, center_vec, mesh_grid_n) {
+transform_2d_mesh_to_3d_mesh <- function(mesh_line_obj_2d, pca_out, center_vec, mesh_grid_n) {
     data_point_2d <- reshape_to_data_point(mesh_line_obj_2d)
     center_mat <- matrix(
-        rep(center_vec, dim(data_point_2d)[1]), 
-        nrow = dim(data_point_2d)[1], byrow = TRUE)
+        rep(center_vec, dim(data_point_2d)[1]),
+        nrow = dim(data_point_2d)[1], byrow = TRUE
+    )
     data_point_3d <- cbind(
-        data_point_2d, 
-        rep(0, dim(data_point_2d)[1])) %*% t((pca_out$rotation)) + center_mat
+        data_point_2d,
+        rep(0, dim(data_point_2d)[1])
+    ) %*% t((pca_out$rotation)) + center_mat
     mesh_grid_line_obj_3d <- reshape_to_mesh_grid_obj(
-        data_point_3d, mesh_grid_n)
+        data_point_3d, mesh_grid_n
+    )
 
     return(mesh_grid_line_obj_3d)
 }
@@ -390,10 +443,12 @@ transform_2d_mesh_to_3d_mesh <- function(
 #' @keywords internal
 get_cross_section_coordinates <- function(cell_subset_projection_locations) {
     cell_subset_projection_PCA <- stats::prcomp(
-        cell_subset_projection_locations)
+        cell_subset_projection_locations
+    )
 
     cell_subset_projection_coords <- cell_subset_projection_PCA$x[
-        , c("PC1", "PC2")]
+        , c("PC1", "PC2")
+    ]
 
     return(cell_subset_projection_coords)
 }
@@ -406,13 +461,14 @@ get_cross_section_coordinates <- function(cell_subset_projection_locations) {
 #' @param mesh_grid_n mesh_grid_n
 #' @returns mesh grid lines
 #' @keywords internal
-create_mesh_grid_lines <- function(
-        cell_subset_projection_locations, extend_ratio, mesh_grid_n) {
+create_mesh_grid_lines <- function(cell_subset_projection_locations, extend_ratio, mesh_grid_n) {
     cell_subset_projection_PCA <- stats::prcomp(
-        cell_subset_projection_locations)
+        cell_subset_projection_locations
+    )
 
     cell_subset_projection_coords <- cell_subset_projection_PCA$x[
-        , c("PC1", "PC2")]
+        , c("PC1", "PC2")
+    ]
 
     x_y_ranges <- find_x_y_ranges(cell_subset_projection_coords, extend_ratio)
 
@@ -424,7 +480,8 @@ create_mesh_grid_lines <- function(
         mesh_grid_n
     )
     center_vec <- apply(
-        cell_subset_projection_locations, 2, function(x) mean(x))
+        cell_subset_projection_locations, 2, function(x) mean(x)
+    )
     mesh_grid_line_obj_3d <- transform_2d_mesh_to_3d_mesh(
         mesh_line_obj_2d,
         cell_subset_projection_PCA,
@@ -440,136 +497,162 @@ create_mesh_grid_lines <- function(
 #' @title createCrossSection
 #' @description Create a virtual 2D cross section.
 #' @param gobject giotto object
+#' @param spat_unit spatial unit
 #' @param spat_loc_name name of spatial locations
 #' @param name name of cress section object. (default = cross_sectino)
-#' @param spatial_network_name name of spatial network object. 
+#' @param spatial_network_name name of spatial network object.
 #' (default = Delaunay_network)
-#' @param thickness_unit unit of the virtual section thickness. If "cell", 
-#' average size of the observed cells is used as length unit. If "natural", 
-#' the unit of cell location coordinates is used.(default = cell)
+#' @param thickness_unit unit of the virtual section thickness. If "cell",
+#' average size of the observed cells is used as length unit. If "natural",
+#' the unit of cell location coordinates is used. (default = cell)
 #' @param slice_thickness thickness of slice. default = 2
-#' @param cell_distance_estimate_method method to estimate average distance 
+#' @param cell_distance_estimate_method method to estimate average distance
 #' between neighobring cells. (default = mean)
-#' @param extend_ratio deciding the span of the cross section meshgrid, as a 
-#' ratio of extension compared to the borders of the vitural tissue section. 
+#' @param extend_ratio deciding the span of the cross section meshgrid, as a
+#' ratio of extension compared to the borders of the vitural tissue section.
 #' (default = 0.2)
 #' @param method method to define the cross section plane.
-#' If equation, the plane is defined by a four element numerical vector 
-#' (equation) in the form of c(A,B,C,D), corresponding to a plane with 
+#' If equation, the plane is defined by a four element numerical vector
+#' (equation) in the form of c(A,B,C,D), corresponding to a plane with
 #' equation Ax+By+Cz=D.
-#' If 3 points, the plane is define by the coordinates of 3 points, as given by 
+#' If 3 points, the plane is define by the coordinates of 3 points, as given by
 #' point1, point2, and point3.
-#' If point and norm vector, the plane is defined by the coordinates of one 
-#' point (point1) in the plane and the coordinates of one norm vector 
+#' If point and norm vector, the plane is defined by the coordinates of one
+#' point (point1) in the plane and the coordinates of one norm vector
 #' (normVector) to the plane.
-#' If point and two plane vector, the plane is defined by the coordinates of 
-#' one point (point1) in the plane and the coordinates of two vectors 
+#' If point and two plane vector, the plane is defined by the coordinates of
+#' one point (point1) in the plane and the coordinates of two vectors
 #' (planeVector1, planeVector2) in the plane.
 #' (default = equation)
-#' @param equation equation required by method "equation".equations needs to be 
-#' a numerical vector of length 4, in the form of c(A,B,C,D), which defines 
+#' @param equation equation required by method "equation".equations needs to be
+#' a numerical vector of length 4, in the form of c(A,B,C,D), which defines
 #' plane Ax+By+Cz=D.
-#' @param point1 coordinates of the first point required by method 
+#' @param point1 coordinates of the first point required by method
 #' "3 points","point and norm vector", and "point and two plane vectors".
 #' @param point2 coordinates of the second point required by method "3 points"
 #' @param point3 coordinates of the third point required by method "3 points"
-#' @param normVector coordinates of the norm vector required by method 
+#' @param normVector coordinates of the norm vector required by method
 #' "point and norm vector"
-#' @param planeVector1 coordinates of the first plane vector required by 
+#' @param planeVector1 coordinates of the first plane vector required by
 #' method "point and two plane vectors"
-#' @param planeVector2 coordinates of the second plane vector required by 
+#' @param planeVector2 coordinates of the second plane vector required by
 #' method "point and two plane vectors"
-#' @param mesh_grid_n numer of meshgrid lines to generate along both directions 
+#' @param mesh_grid_n numer of meshgrid lines to generate along both directions
 #' for the cross section plane.
 #' @param return_gobject boolean: return giotto object (default = TRUE)
+#' @param verbose be verbose
 #' @returns giotto object with updated spatial network slot
-#' @details Creates a virtual 2D cross section object for a given spatial 
-#' network object. The users need to provide the definition of the cross 
+#' @details Creates a virtual 2D cross section object for a given spatial
+#' network object. The users need to provide the definition of the cross
 #' section plane (see method).
 #' @examples
-#' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' createCrossSection(gobject = g, spatial_network_name = "spatial_network")
+#' g <- GiottoData::loadGiottoMini("starmap")
+#'
+#' g <- createCrossSection(
+#'     gobject = g,
+#'     method = "equation",
+#'     equation = c(0, 1, 0, 600),
+#'     extend_ratio = 0.6,
+#'     name = "new_cs",
+#'     return_gobject = TRUE
+#' )
+#'
+#' crossSectionPlot(g, name = "new_cs")
 #' @export
-createCrossSection <- function(gobject,
-    spat_loc_name = "raw",
-    name = "cross_section",
-    spatial_network_name = "Delaunay_network",
-    thickness_unit = c("cell", "natural"),
-    slice_thickness = 2,
-    cell_distance_estimate_method = "mean",
-    extend_ratio = 0.2,
-    method = c("equation", "3 points", "point and norm vector", 
-                "point and two plane vectors"),
-    equation = NULL,
-    point1 = NULL, point2 = NULL, point3 = NULL,
-    normVector = NULL,
-    planeVector1 = NULL, planeVector2 = NULL,
-    mesh_grid_n = 20,
-    return_gobject = TRUE) {
-    # read spatial locations
-    spatial_locations <- getSpatialLocations(gobject, 
-                                            name = spat_loc_name)
-    cell_IDs <- spatial_locations[, "cell_ID"]
-    cell_IDs <- cell_IDs$cell_ID
-
-    colnames_to_extract <- c("sdimx", "sdimy", "sdimz")
-    spatial_locations <- spatial_locations[, colnames_to_extract]
+createCrossSection <- function(
+        gobject,
+        spat_unit = NULL,
+        spat_loc_name = "raw",
+        name = "cross_section",
+        spatial_network_name = "Delaunay_network",
+        thickness_unit = c("cell", "natural"),
+        slice_thickness = 2,
+        cell_distance_estimate_method = "mean",
+        extend_ratio = 0.2,
+        method = c(
+            "equation", "3 points", "point and norm vector",
+            "point and two plane vectors"
+        ),
+        equation = NULL,
+        point1 = NULL, point2 = NULL, point3 = NULL,
+        normVector = NULL,
+        planeVector1 = NULL, planeVector2 = NULL,
+        mesh_grid_n = 20,
+        return_gobject = TRUE,
+        verbose = NULL) {
+    spat_unit <- set_default_spat_unit(
+        gobject = gobject, spat_unit = spat_unit
+    )
 
-    spatial_locations <- spatial_locations@coordinates
+    # read spatial locations
+    spatial_locations <- getSpatialLocations(
+        gobject = gobject, spat_unit = spat_unit, name = spat_loc_name,
+        set_defaults = FALSE, verbose = FALSE, output = "spatLocsObj"
+    )
 
-    spatial_locations <- as.matrix(spatial_locations)
-    rownames(spatial_locations) <- cell_IDs
-    cell_ID_vec <- c(1:nrow(spatial_locations))
+    spatial_locations <- as.matrix(spatial_locations, id_rownames = TRUE)
+    cell_ID_vec <- seq_len(nrow(spatial_locations))
     names(cell_ID_vec) <- rownames(spatial_locations)
 
     # generate section plane equation
 
     method <- match.arg(
-        method, 
-        c("equation", "3 points", "point and norm vector", 
-        "point and two plane vectors"))
-
-    if (method == "equation") {
-        if (is.null(equation)) {
-            message("equation was not provided.")
-        } else {
-            plane_equation <- equation
-            plane_equation[4] <- -equation[4]
-        }
-    } else if (method == "point and norm vector") {
-        if (is.null(point1) | is.null(normVector)) {
-            message("either point or norm vector was not provided.")
-        } else {
-            plane_equation <- c()
-            plane_equation[1:3] <- normVector
-            plane_equation[4] <- -point1 %*% normVector
-        }
-    } else if (method == "point and two plane vectors") {
-        if (is.null(point1) | is.null(planeVector1) | is.null(planeVector2)) {
-            message("either point or any of the two plane vectors was not 
+        method,
+        c(
+            "equation", "3 points", "point and norm vector",
+            "point and two plane vectors"
+        )
+    )
+
+    switch(method,
+        "equation" = {
+            if (is.null(equation)) {
+                message("equation was not provided.")
+            } else {
+                plane_equation <- equation
+                plane_equation[4] <- -equation[4]
+            }
+        },
+        "point and norm vector" = {
+            if (is.null(point1) || is.null(normVector)) {
+                message("either point or norm vector was not provided.")
+            } else {
+                plane_equation <- c()
+                plane_equation[seq_len(3)] <- normVector
+                plane_equation[4] <- -point1 %*% normVector
+            }
+        },
+        "point and two plane vectors" = {
+            if (is.null(point1) ||
+                is.null(planeVector1) ||
+                is.null(planeVector2)) {
+                message("either point or any of the two plane vectors was not
                     provided.")
-        } else {
-            normVector <- crossprod(planeVector1, planeVector2)
-            plane_equation[1:3] <- normVector
-            plane_equation[4] <- -point1 %*% normVector
-        }
-    } else if (method == "3 points") {
-        if (is.null(point1) | is.null(point2) | is.null(point3)) {
-            message("not all three points were provided.")
-        } else {
-            planeVector1 <- point2 - point1
-            planeVector2 <- point3 - point1
-            normVector <- crossprod(planeVector1, planeVector2)
-            plane_equation[1:3] <- normVector
-            plane_equation[4] <- -point1 %*% normVector
+            } else {
+                normVector <- crossprod(planeVector1, planeVector2)
+                plane_equation[seq_len(3)] <- normVector
+                plane_equation[4] <- -point1 %*% normVector
+            }
+        },
+        "3 points" = {
+            if (is.null(point1) || is.null(point2) || is.null(point3)) {
+                message("not all three points were provided.")
+            } else {
+                planeVector1 <- point2 - point1
+                planeVector2 <- point3 - point1
+                normVector <- crossprod(planeVector1, planeVector2)
+                plane_equation[seq_len(3)] <- normVector
+                plane_equation[4] <- -point1 %*% normVector
+            }
         }
-    }
+    )
+
     names(plane_equation) <- c("A", "B", "C", "D")
 
     # determine section thickness
     thickness_unit <- match.arg(thickness_unit, c("cell", "natural"))
     sectionThickness <- get_sectionThickness(gobject,
+        spat_unit = spat_unit,
         thickness_unit = thickness_unit,
         slice_thickness = slice_thickness,
         spatial_network_name = spatial_network_name,
@@ -581,10 +664,12 @@ createCrossSection <- function(gobject,
 
     # calculate distances to cross section
     spatial_locations_mat <- cbind(
-        spatial_locations, as.matrix(rep(1, dim(spatial_locations)[1])))
+        spatial_locations, as.matrix(rep(1, dim(spatial_locations)[1]))
+    )
     norm_vec <- function(x) sqrt(sum(x^2))
     distance_to_plane_vector <- abs(spatial_locations_mat %*% as.matrix(
-        plane_equation) / norm_vec(plane_equation[1:3]))
+        plane_equation
+    ) / norm_vec(plane_equation[1:3]))
 
     # select cells within section ###
     cell_subset <- distance_to_plane_vector <= max_distance_to_section_plane
@@ -602,19 +687,27 @@ createCrossSection <- function(gobject,
     }
     ## find the projection Xp,Yp,Zp coordinates ##
     cell_subset_projection_locations <- t(apply(
-        cell_subset_spatial_locations, 1, 
-        function(x) projection_fun(x, plane_point = plane_point, 
-                                plane_norm = plane_equation[1:3])))
+        cell_subset_spatial_locations, 1,
+        function(x) {
+            projection_fun(x,
+                plane_point = plane_point,
+                plane_norm = plane_equation[1:3]
+            )
+        }
+    ))
 
     # get the local coordinates of selected cells on the section plane
     cell_subset_projection_PCA <- stats::prcomp(
-        cell_subset_projection_locations)
+        cell_subset_projection_locations
+    )
     cell_subset_projection_coords <- get_cross_section_coordinates(
-        cell_subset_projection_locations)
+        cell_subset_projection_locations
+    )
 
     # create mesh grid lines for the cross section ###
     mesh_grid_lines <- create_mesh_grid_lines(
-        cell_subset_projection_locations, extend_ratio, mesh_grid_n)
+        cell_subset_projection_locations, extend_ratio, mesh_grid_n
+    )
     mesh_obj <- list("mesh_grid_lines" = mesh_grid_lines)
 
     ### save and update the spatial object ###
@@ -634,14 +727,30 @@ createCrossSection <- function(gobject,
     )
 
 
-    if (return_gobject == TRUE) {
-        cs_names <- names(gobject@spatial_network[[
-            spatial_network_name]]$crossSectionObjects)
+    if (return_gobject) {
+        sn <- getSpatialNetwork(
+            gobject = gobject,
+            spat_unit = spat_unit,
+            name = spatial_network_name,
+            copy_obj = FALSE,
+            set_defaults = FALSE,
+            verbose = FALSE,
+            output = "spatialNetworkObj"
+        )
+
+        cs_names <- names(sn@crossSectionObjects)
         if (name %in% cs_names) {
-            cat(name, " has already been used, will be overwritten")
+            vmsg(.v = verbose, sprintf(
+                "name '%s' has already been used, will be overwritten",
+                name
+            ))
         }
-        gobject@spatial_network[[spatial_network_name]]$crossSectionObjects[[
-            name]] <- crossSection_obj
+
+        sn@crossSectionObjects[[name]] <- crossSection_obj
+
+        ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
+        gobject <- setGiotto(gobject, sn, verbose = FALSE)
+        ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
 
         return(gobject)
     } else {
@@ -653,16 +762,18 @@ createCrossSection <- function(gobject,
 # cross section visual functions ####
 
 ####
-#' @title crossSectionGenePlot
-#' @name crossSectionGenePlot
-#' @description Visualize cells and gene expression in a virtual cross section 
-#' according to spatial coordinates
+#' @title crossSectionFeatPlot
+#' @name crossSectionFeatPlot
+#' @description Visualize cells and feature expression in a virtual cross
+#' section according to spatial coordinates
 #' @param gobject giotto object
+#' @param spat_unit spatial unit
+#' @param feat_type feature type
 #' @param spat_loc_name name of spatial locations
 #' @param crossSection_obj crossSection object
 #' @param name name of virtual cross section to use
 #' @param spatial_network_name name of spatial network to use
-#' @param default_save_name default save name for saving, don't change, 
+#' @param default_save_name default save name for saving, don't change,
 #' change save_name in save_param
 #' @param ... parameters for spatFeatPlot2D
 #' @returns ggplot
@@ -670,36 +781,66 @@ createCrossSection <- function(gobject,
 #' @md
 #' @seealso [GiottoVisuals::spatGenePlot3D] and [GiottoVisuals::spatFeatPlot2D]
 #' @export
-crossSectionGenePlot <- function(
-        gobject = NULL,
-        spat_loc_name = "raw",
-        crossSection_obj = NULL,
-        name = NULL,
-        spatial_network_name = "Delaunay_network",
-        default_save_name = "crossSectionGenePlot",
-        ...) {
+crossSectionFeatPlot <- function(gobject = NULL,
+    spat_unit = NULL,
+    feat_type = NULL,
+    spat_loc_name = "raw",
+    crossSection_obj = NULL,
+    name = NULL,
+    spatial_network_name = "Delaunay_network",
+    default_save_name = "crossSectionGenePlot",
+    ...) {
+    spat_unit <- set_default_spat_unit(
+        gobject = gobject, spat_unit = spat_unit
+    )
+    feat_type <- set_default_feat_type(
+        gobject = gobject, spat_unit = spat_unit, feat_type = feat_type
+    )
+
     # load cross section object
-    if (!is.null(crossSection_obj)) {
-        crossSection_obj <- crossSection_obj
-    } else {
+    if (is.null(crossSection_obj)) {
         crossSection_obj <- read_crossSection(
             gobject,
+            spat_unit = spat_unit,
             name = name,
             spatial_network_name = spatial_network_name
         )
     }
 
     cell_subset <- crossSection_obj$cell_subset
-    cell_subset_projection_coords <- crossSection_obj$cell_subset_projection_coords
+    cell_subset <- rownames(cell_subset)[which(cell_subset)]
+    cell_subset_projection_coords <-
+        crossSection_obj$cell_subset_projection_coords
+
     # modify gobject based on crossSection object
-    subset_cell_IDs <- gobject@cell_metadata$cell_ID[cell_subset]
-    temp_gobject <- subsetGiotto(gobject = gobject, cell_ids = subset_cell_IDs)
-    temp_gobject@spatial_locs[[spat_loc_name]]$sdimx <- cell_subset_projection_coords[, 1]
-    temp_gobject@spatial_locs[[spat_loc_name]]$sdimy <- cell_subset_projection_coords[, 2]
-    temp_gobject@spatial_locs[[spat_loc_name]]$sdimz <- rep(0, dim(cell_subset_projection_coords)[1])
+    gobj_sids <- spatIDs(gobject, spat_unit = spat_unit)
+    subset_cell_ids <- gobj_sids[gobj_sids %in% cell_subset]
+    temp_gobject <- subsetGiotto(gobject,
+        spat_unit = spat_unit,
+        feat_type = feat_type,
+        cell_ids = subset_cell_ids
+    )
+
+    sl <- getSpatialLocations(
+        gobject = temp_gobject,
+        spat_unit = spat_unit,
+        name = spat_loc_name,
+        output = "spatLocsObj",
+        copy_obj = TRUE,
+        verbose = FALSE
+    )
+
+    sl[]$sdimx <- cell_subset_projection_coords[, 1]
+    sl[]$sdimy <- cell_subset_projection_coords[, 2]
+    sl[]$sdimz <- rep(0, dim(cell_subset_projection_coords)[1])
+
+    temp_gobject <- setGiotto(temp_gobject, sl, verbose = FALSE)
+
     # call spatFeatPlot2D to generate the plots
     GiottoVisuals::spatFeatPlot2D(
         gobject = temp_gobject,
+        spat_unit = spat_unit,
+        feat_type = feat_type,
         spatial_network_name = spatial_network_name,
         default_save_name = default_save_name,
         ...
@@ -709,62 +850,83 @@ crossSectionGenePlot <- function(
 
 #' @title crossSectionPlot
 #' @name crossSectionPlot
-#' @description Visualize cells in a virtual cross section according to 
+#' @description Visualize cells in a virtual cross section according to
 #' spatial coordinates
 #' @param gobject giotto object
-#' @param spat_loc_name name of spatial locations
+#' @param spat_unit spatial unit
 #' @param feat_type feature type
-#' @param crossSection_obj cross section object as alternative input. 
+#' @param spat_loc_name name of spatial locations
+#' @param crossSection_obj cross section object as alternative input.
 #' default = NULL.
 #' @param name name of virtual cross section to use
 #' @param spatial_network_name name of spatial network to use
-#' @param default_save_name default save name for saving, don't change, 
+#' @param default_save_name default save name for saving, don't change,
 #' change save_name in save_param
 #' @param ... parameters for spatPlot2D
 #' @returns ggplot
 #' @details Description of parameters.
 #' @export
 #' @seealso \code{\link{crossSectionPlot}}
-crossSectionPlot <- function(gobject,
-    spat_loc_name = "raw",
-    feat_type = NULL,
-    crossSection_obj = NULL,
-    name = NULL,
-    spatial_network_name = "Delaunay_network",
-    default_save_name = "crossSectionPlot",
-    ...) {
-    # specify feat_type
-    if (is.null(feat_type)) {
-        feat_type <- gobject@expression_feat[[1]]
-    }
+crossSectionPlot <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        spat_loc_name = "raw",
+        crossSection_obj = NULL,
+        name = NULL,
+        spatial_network_name = "Delaunay_network",
+        default_save_name = "crossSectionPlot",
+        ...) {
+    spat_unit <- set_default_spat_unit(
+        gobject = gobject, spat_unit = spat_unit
+    )
+    feat_type <- set_default_feat_type(
+        gobject = gobject, spat_unit = spat_unit, feat_type = feat_type
+    )
 
     # load cross section object
-    if (!is.null(crossSection_obj)) {
-        crossSection_obj <- crossSection_obj
-    } else {
-        crossSection_obj <- read_crossSection(gobject,
+    if (is.null(crossSection_obj)) {
+        crossSection_obj <- read_crossSection(
+            gobject = gobject,
+            spat_unit = spat_unit,
             name = name,
             spatial_network_name = spatial_network_name
         )
     }
 
-
     cell_subset <- crossSection_obj$cell_subset
-    cell_subset_projection_coords <- crossSection_obj$cell_subset_projection_coords
+    cell_subset <- rownames(cell_subset)[which(cell_subset)]
+    cell_subset_projection_coords <-
+        crossSection_obj$cell_subset_projection_coords
 
     # modify gobject based on crossSection object
-    subset_cell_IDs <- gobject@cell_metadata[[feat_type]]$cell_ID[cell_subset]
+    gobj_sids <- spatIDs(gobject, spat_unit = spat_unit)
+    subset_cell_ids <- gobj_sids[gobj_sids %in% cell_subset]
     temp_gobject <- subsetGiotto(gobject,
+        spat_unit = spat_unit,
         feat_type = feat_type,
-        cell_ids = subset_cell_IDs
+        cell_ids = subset_cell_ids
     )
-    temp_gobject@spatial_locs[[spat_loc_name]]$sdimx <- cell_subset_projection_coords[, 1]
-    temp_gobject@spatial_locs[[spat_loc_name]]$sdimy <- cell_subset_projection_coords[, 2]
-    temp_gobject@spatial_locs[[spat_loc_name]]$sdimz <- rep(0, dim(cell_subset_projection_coords)[1])
+
+    sl <- getSpatialLocations(
+        gobject = temp_gobject,
+        spat_unit = spat_unit,
+        name = spat_loc_name,
+        output = "spatLocsObj",
+        copy_obj = TRUE,
+        verbose = FALSE
+    )
+
+    sl[]$sdimx <- cell_subset_projection_coords[, 1]
+    sl[]$sdimy <- cell_subset_projection_coords[, 2]
+    sl[]$sdimz <- rep(0, dim(cell_subset_projection_coords)[1])
+
+    temp_gobject <- setGiotto(temp_gobject, sl, verbose = FALSE)
 
     # call spatFeatPlot2D to generate the plots
     spatPlot2D(
         gobject = temp_gobject,
+        spat_unit = spat_unit,
         feat_type = feat_type,
         spatial_network_name = spatial_network_name,
         default_save_name = default_save_name,
@@ -773,94 +935,141 @@ crossSectionPlot <- function(gobject,
 }
 
 ####
-#' @title crossSectionGenePlot3D
-#' @name crossSectionGenePlot3D
-#' @description Visualize cells and gene expression in a virtual cross section 
-#' according to spatial coordinates
+#' @title crossSectionFeatPlot3D
+#' @name crossSectionFeatPlot3D
+#' @description Visualize cells and feature expression in a virtual cross
+#' section according to spatial coordinates
 #' @param gobject giotto object
+#' @param spat_unit spatial unit
+#' @param feat_type feature type
 #' @param crossSection_obj cross section object as alternative input. default = NULL.
 #' @param name name of virtual cross section to use
 #' @param spatial_network_name name of spatial network to use
-#' @param other_cell_color color of cells outside the cross section. 
+#' @param show_other_cells logical. Default = TRUE
+#' @param other_cell_color color of cells outside the cross section.
 #' default = transparent.
-#' @param default_save_name default save name for saving, don't change, change 
+#' @param default_save_name default save name for saving, don't change, change
 #' save_name in save_param
 #' @param ... parameters for spatGenePlot3D
 #' @return ggplot
 #' @details Description of parameters.
 #' @export
-crossSectionGenePlot3D <- function(gobject,
-    crossSection_obj = NULL,
-    name = NULL,
-    spatial_network_name = "Delaunay_network",
-    other_cell_color = alpha("lightgrey", 0),
-    default_save_name = "crossSectionGenePlot3D", ...) {
+crossSectionFeatPlot3D <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        crossSection_obj = NULL,
+        name = NULL,
+        spatial_network_name = "Delaunay_network",
+        show_other_cells = TRUE,
+        other_cell_color = alpha("lightgrey", 0),
+        default_save_name = "crossSectionGenePlot3D",
+        ...) {
+    spat_unit <- set_default_spat_unit(
+        gobject = gobject, spat_unit = spat_unit
+    )
+    feat_type <- set_default_feat_type(
+        gobject = gobject, spat_unit = spat_unit, feat_type = feat_type
+    )
+
     # load cross section object
-    if (!is.null(crossSection_obj)) {
-        crossSection_obj <- crossSection_obj
-    } else {
+    if (is.null(crossSection_obj)) {
         crossSection_obj <- read_crossSection(
-            gobject, name = name, spatial_network_name = spatial_network_name)
+            gobject = gobject,
+            spat_unit = spat_unit,
+            name = name,
+            spatial_network_name = spatial_network_name
+        )
     }
 
-
     cell_subset <- crossSection_obj$cell_subset
-    cell_subset_projection_coords <- crossSection_obj$cell_subset_projection_coords
+    cell_subset <- rownames(cell_subset)[which(cell_subset)]
+    cell_subset_projection_coords <-
+        crossSection_obj$cell_subset_projection_coords
+
     # modify gobject based on crossSection object
-    subset_cell_IDs <- gobject@cell_metadata$cell_ID[cell_subset]
+    gobj_sids <- spatIDs(gobject, spat_unit = spat_unit)
+    subset_cell_ids <- gobj_sids[gobj_sids %in% cell_subset]
+
     # call spatGenePlot3D to generate the plots
-    spatGenePlot3D(gobject,
-        select_cells = subset_cell_IDs,
+    spatFeatPlot3D(gobject,
+        spat_unit = spat_unit,
+        feat_type = feat_type,
+        select_cells = subset_cell_ids,
+        show_other_cells = show_other_cells,
         other_cell_color = other_cell_color,
-        default_save_name = default_save_name, ...
+        default_save_name = default_save_name,
+        ...
     )
 }
 ####
 #' @title crossSectionPlot3D
 #' @name crossSectionPlot3D
-#' @description Visualize cells in a virtual cross section according to spatial 
+#' @description Visualize cells in a virtual cross section according to spatial
 #' coordinates
 #' @param gobject giotto object
-#' @param crossSection_obj cross section object as alternative input. 
+#' @param spat_unit spatial unit
+#' @param feat_type feature type
+#' @param crossSection_obj cross section object as alternative input.
 #' default = NULL.
 #' @param name name of virtual cross section to use
 #' @param spatial_network_name name of spatial network to use
 #' @param show_other_cells display not selected cells
-#' @param other_cell_color color of cells outside the cross section. 
+#' @param other_cell_color color of cells outside the cross section.
 #' default = transparent.
-#' @param default_save_name default save name for saving, don't change, 
+#' @param default_save_name default save name for saving, don't change,
 #' change save_name in save_param
 #' @param ... parameters for spatPlot3D
 #' @returns ggplot
 #' @details Description of parameters.
 #' @export
-crossSectionPlot3D <- function(gobject,
-    crossSection_obj = NULL,
-    name = NULL,
-    spatial_network_name = "Delaunay_network",
-    show_other_cells = TRUE,
-    other_cell_color = alpha("lightgrey", 0),
-    default_save_name = "crossSection3D", ...) {
+crossSectionPlot3D <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        crossSection_obj = NULL,
+        name = NULL,
+        spatial_network_name = "Delaunay_network",
+        show_other_cells = TRUE,
+        other_cell_color = alpha("lightgrey", 0),
+        default_save_name = "crossSection3D",
+        ...) {
+    spat_unit <- set_default_spat_unit(
+        gobject = gobject, spat_unit = spat_unit
+    )
+    feat_type <- set_default_feat_type(
+        gobject = gobject, spat_unit = spat_unit, feat_type = feat_type
+    )
+
     # load cross section object
-    if (!is.null(crossSection_obj)) {
-        crossSection_obj <- crossSection_obj
-    } else {
+    if (is.null(crossSection_obj)) {
         crossSection_obj <- read_crossSection(
-            gobject, name = name, spatial_network_name = spatial_network_name)
+            gobject,
+            spat_unit = spat_unit,
+            name = name,
+            spatial_network_name = spatial_network_name
+        )
     }
 
     cell_subset <- crossSection_obj$cell_subset
-    cell_subset_projection_coords <- crossSection_obj$cell_subset_projection_coords
+    cell_subset <- rownames(cell_subset)[which(cell_subset)]
+    cell_subset_projection_coords <-
+        crossSection_obj$cell_subset_projection_coords
+
     # modify gobject based on crossSection object
-    subset_cell_IDs <- gobject@cell_metadata$cell_ID[cell_subset]
-    
+    gobj_sids <- spatIDs(gobject, spat_unit = spat_unit)
+    subset_cell_ids <- gobj_sids[gobj_sids %in% cell_subset]
+
     # call spatPlot3D to generate the plots
     spatPlot3D(
         gobject = gobject,
-        select_cells = subset_cell_IDs,
+        spat_unit = spat_unit,
+        feat_type = feat_type,
+        select_cells = subset_cell_ids,
         show_other_cells = show_other_cells,
         other_cell_color = other_cell_color,
-        default_save_name = default_save_name, ...
+        default_save_name = default_save_name,
+        ...
     )
 }
 
@@ -868,11 +1077,13 @@ crossSectionPlot3D <- function(gobject,
 ####
 #' @title insertCrossSectionSpatPlot3D
 #' @name insertCrossSectionSpatPlot3D
-#' @description Visualize the meshgrid lines of cross section together with 
+#' @description Visualize the meshgrid lines of cross section together with
 #' cells
 #' @param gobject giotto object
+#' @param spat_unit spatial unit
+#' @param feat_type feature type
 #' @param spat_loc_name name of spatial locations
-#' @param crossSection_obj cross section object as alternative input. 
+#' @param crossSection_obj cross section object as alternative input.
 #' default = NULL.
 #' @param name name of virtual cross section to use
 #' @param spatial_network_name name of spatial network to use
@@ -885,36 +1096,52 @@ crossSectionPlot3D <- function(gobject,
 #' @param show_other_cells display not selected cells
 #' @param axis_scale axis_scale
 #' @param custom_ratio custom_ratio
-#' @param default_save_name default save name for saving, don't change, 
+#' @param default_save_name default save name for saving, don't change,
 #' change save_name in save_param
 #' @param ... parameters for spatPlot3D
 #' @returns ggplot
 #' @details Description of parameters.
 #' @export
-insertCrossSectionSpatPlot3D <- function(gobject,
-    spat_loc_name = "raw",
-    crossSection_obj = NULL,
-    name = NULL,
-    spatial_network_name = "Delaunay_network",
-    mesh_grid_color = "#1f77b4",
-    mesh_grid_width = 3,
-    mesh_grid_style = "dot",
-    sdimx = "sdimx", sdimy = "sdimy", sdimz = "sdimz",
-    show_other_cells = FALSE,
-    axis_scale = c("cube", "real", "custom"),
-    custom_ratio = NULL,
-    default_save_name = "spat3D_with_cross_section", ...) {
+insertCrossSectionSpatPlot3D <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        spat_loc_name = "raw",
+        crossSection_obj = NULL,
+        name = NULL,
+        spatial_network_name = "Delaunay_network",
+        mesh_grid_color = "#1f77b4",
+        mesh_grid_width = 3,
+        mesh_grid_style = "dot",
+        sdimx = "sdimx", sdimy = "sdimy", sdimz = "sdimz",
+        show_other_cells = FALSE,
+        axis_scale = c("cube", "real", "custom"),
+        custom_ratio = NULL,
+        default_save_name = "spat3D_with_cross_section",
+        ...) {
+    spat_unit <- set_default_spat_unit(
+        gobject = gobject, spat_unit = spat_unit
+    )
+    feat_type <- set_default_feat_type(
+        gobject = gobject, spat_unit = spat_unit, feat_type = feat_type
+    )
+
     # load cross section object
     if (!is.null(crossSection_obj)) {
         crossSection_obj <- crossSection_obj
     } else {
         crossSection_obj <- read_crossSection(
-            gobject, name = name, spatial_network_name = spatial_network_name)
+            gobject,
+            spat_unit = spat_unit,
+            name = name,
+            spatial_network_name = spatial_network_name
+        )
     }
 
 
-
     pl <- spatPlot3D(gobject,
+        spat_unit = spat_unit,
+        feat_type = feat_type,
         sdimx = sdimx,
         sdimy = sdimy,
         sdimz = sdimz,
@@ -922,28 +1149,40 @@ insertCrossSectionSpatPlot3D <- function(gobject,
         show_plot = FALSE,
         return_plot = TRUE,
         save_plot = FALSE,
-        default_save_name = default_save_name, ...
+        default_save_name = default_save_name,
+        ...
     )
 
     for (i in seq_len(dim(
-        crossSection_obj$mesh_obj$mesh_grid_lines$mesh_grid_lines_X)[2])) {
+        crossSection_obj$mesh_obj$mesh_grid_lines$mesh_grid_lines_X
+    )[2])) {
         pl <- pl %>% plotly::add_trace(
             x = crossSection_obj$mesh_obj$mesh_grid_lines$mesh_grid_lines_X[, i],
             y = crossSection_obj$mesh_obj$mesh_grid_lines$mesh_grid_lines_Y[, i],
             z = crossSection_obj$mesh_obj$mesh_grid_lines$mesh_grid_lines_Z[, i],
             mode = "lines", type = "scatter3d",
-            line = list(color = mesh_grid_color, 
-                        width = mesh_grid_width, dash = mesh_grid_style)
+            line = list(
+                color = mesh_grid_color,
+                width = mesh_grid_width, dash = mesh_grid_style
+            )
         )
     }
 
-    current_ratio <- plotly_axis_scale_3D(gobject@spatial_locs[[spat_loc_name]],
+    sl <- getSpatialLocations(
+        gobject = gobject, spat_unit = spat_unit, name = spat_loc_name,
+        output = "data.table", copy_obj = TRUE, verbose = FALSE,
+        set_defaults = TRUE
+    )
+
+    current_ratio <- plotly_axis_scale_3D(
+        cell_locations = sl,
         sdimx = sdimx, sdimy = sdimy, sdimz = sdimz,
         mode = axis_scale, custom_ratio = custom_ratio
     )
 
     new_ratio <- adapt_aspect_ratio(
-        current_ratio, gobject@spatial_locs[[spat_loc_name]],
+        current_ratio = current_ratio,
+        cell_locations = sl,
         sdimx = sdimx, sdimy = sdimy, sdimz = sdimz,
         mesh_obj = crossSection_obj$mesh_obj
     )
@@ -963,13 +1202,15 @@ insertCrossSectionSpatPlot3D <- function(gobject,
     return(pl)
 }
 ####
-#' @title insertCrossSectionGenePlot3D
-#' @name insertCrossSectionGenePlot3D
-#' @description Visualize cells and gene expression in a virtual cross section 
+#' @title insertCrossSectionFeatPlot3D
+#' @name insertCrossSectionFeatPlot3D
+#' @description Visualize cells and gene expression in a virtual cross section
 #' according to spatial coordinates
 #' @param gobject giotto object
+#' @param spat_unit spatial unit
+#' @param feat_type feature type
 #' @param spat_loc_name name of spatial locations
-#' @param crossSection_obj cross section object as alternative input. 
+#' @param crossSection_obj cross section object as alternative input.
 #' default = NULL.
 #' @param name name of virtual cross section to use
 #' @param spatial_network_name name of spatial network to use
@@ -985,74 +1226,96 @@ insertCrossSectionSpatPlot3D <- function(gobject,
 #' @param show_plot show plots
 #' @param return_plot return ggplot object
 #' @param save_plot logical. directly save the plot
-#' @param save_param list of saving parameters from 
+#' @param save_param list of saving parameters from
 #' [GiottoVisuals::all_plots_save_function]
-#' @param default_save_name default save name for saving, don't change, 
+#' @param default_save_name default save name for saving, don't change,
 #' change save_name in save_param
 #' @param ... parameters for spatGenePlot3D
 #' @returns ggplot
 #' @details Description of parameters.
 #' @md
 #' @export
-insertCrossSectionGenePlot3D <- function(
-        gobject,
-        spat_loc_name = "raw",
-        crossSection_obj = NULL,
-        name = NULL,
-        spatial_network_name = "Delaunay_network",
-        mesh_grid_color = "#1f77b4",
-        mesh_grid_width = 3,
-        mesh_grid_style = "dot",
-        sdimx = "sdimx", sdimy = "sdimy", sdimz = "sdimz",
-        show_other_cells = FALSE,
-        axis_scale = c("cube", "real", "custom"),
-        custom_ratio = NULL,
-        show_plot = NULL, return_plot = NULL, save_plot = NULL,
-        save_param = list(),
-        default_save_name = "spatGenePlot3D_with_cross_section",
-        ...) {
+insertCrossSectionFeatPlot3D <- function(gobject,
+    spat_unit = NULL,
+    feat_type = NULL,
+    spat_loc_name = "raw",
+    crossSection_obj = NULL,
+    name = NULL,
+    spatial_network_name = "Delaunay_network",
+    mesh_grid_color = "#1f77b4",
+    mesh_grid_width = 3,
+    mesh_grid_style = "dot",
+    sdimx = "sdimx", sdimy = "sdimy", sdimz = "sdimz",
+    show_other_cells = FALSE,
+    axis_scale = c("cube", "real", "custom"),
+    custom_ratio = NULL,
+    show_plot = NULL, return_plot = NULL, save_plot = NULL,
+    save_param = list(),
+    default_save_name = "spatGenePlot3D_with_cross_section",
+    ...) {
+    spat_unit <- set_default_spat_unit(
+        gobject = gobject, spat_unit = spat_unit
+    )
+    feat_type <- set_default_feat_type(
+        gobject = gobject, spat_unit = spat_unit, feat_type = feat_type
+    )
+
     # load cross section object
-    if (!is.null(crossSection_obj)) {
-        crossSection_obj <- crossSection_obj
-    } else {
+    if (is.null(crossSection_obj)) {
         crossSection_obj <- read_crossSection(
-            gobject, name = name, spatial_network_name = spatial_network_name)
+            gobject,
+            spat_unit = spat_unit,
+            name = name,
+            spatial_network_name = spatial_network_name
+        )
     }
 
-    pl <- spatGenePlot3D(gobject,
+    pl <- spatFeatPlot3D(gobject,
+        spat_unit = spat_unit,
+        feat_type = feat_type,
         show_other_cells = FALSE,
         axis_scale = axis_scale,
         custom_ratio = custom_ratio,
         show_plot = FALSE,
         return_plot = TRUE,
         save_plot = FALSE,
-        default_save_name = default_save_name, ...
+        default_save_name = default_save_name,
+        ...
     )
+
     for (i in seq_len(dim(
-        crossSection_obj$mesh_obj$mesh_grid_lines$mesh_grid_lines_X)[2])) {
+        crossSection_obj$mesh_obj$mesh_grid_lines$mesh_grid_lines_X
+    )[2])) {
         pl <- pl %>% plotly::add_trace(
             x = crossSection_obj$mesh_obj$mesh_grid_lines$mesh_grid_lines_X[, i],
             y = crossSection_obj$mesh_obj$mesh_grid_lines$mesh_grid_lines_Y[, i],
             z = crossSection_obj$mesh_obj$mesh_grid_lines$mesh_grid_lines_Z[, i],
             mode = "lines+markers", type = "scatter3d", color = mesh_grid_color,
             marker = list(color = alpha(mesh_grid_color, 0)),
-            line = list(color = mesh_grid_color, 
-                        width = mesh_grid_width, dash = mesh_grid_style)
+            line = list(
+                color = mesh_grid_color,
+                width = mesh_grid_width, dash = mesh_grid_style
+            )
         )
     }
 
-    current_ratio <- plotly_axis_scale_3D(gobject@spatial_locs[[spat_loc_name]],
-        sdimx = sdimx,
-        sdimy = sdimy,
-        sdimz = sdimz,
+    sl <- getSpatialLocations(
+        gobject = gobject, spat_unit = spat_unit, name = spat_loc_name,
+        output = "data.table", copy_obj = TRUE, verbose = FALSE,
+        set_defaults = TRUE
+    )
+
+
+    current_ratio <- plotly_axis_scale_3D(
+        cell_locations = sl,
+        sdimx = sdimx, sdimy = sdimy, sdimz = sdimz,
         mode = axis_scale, custom_ratio = custom_ratio
     )
 
     new_ratio <- adapt_aspect_ratio(
-        current_ratio, gobject@spatial_locs[[spat_loc_name]],
-        sdimx = sdimx,
-        sdimy = sdimy,
-        sdimz = sdimz,
+        current_ratio = current_ratio,
+        cell_locations = sl,
+        sdimx = sdimx, sdimy = sdimy, sdimz = sdimz,
         mesh_obj = crossSection_obj$mesh_obj
     )
 
diff --git a/R/differential_expression.R b/R/differential_expression.R
index e476e2e62..5eead79a0 100644
--- a/R/differential_expression.R
+++ b/R/differential_expression.R
@@ -35,18 +35,19 @@
 #'
 #' findScranMarkers(g, cluster_column = "leiden_clus")
 #' @export
-findScranMarkers <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    cluster_column,
-    subset_clusters = NULL,
-    group_1 = NULL,
-    group_1_name = NULL,
-    group_2 = NULL,
-    group_2_name = NULL,
-    verbose = TRUE,
-    ...) {
+findScranMarkers <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        cluster_column,
+        subset_clusters = NULL,
+        group_1 = NULL,
+        group_1_name = NULL,
+        group_2 = NULL,
+        group_2_name = NULL,
+        verbose = TRUE,
+        ...) {
     # verify if optional package is installed
     package_check(pkg_name = "scran", repository = "Bioc")
 
@@ -76,8 +77,11 @@ findScranMarkers <- function(gobject,
     # expression data
     values <- match.arg(
         expression_values,
-        choices = unique(c("normalized", "scaled", "custom",
-                          expression_values)))
+        choices = unique(c(
+            "normalized", "scaled", "custom",
+            expression_values
+        ))
+    )
     expr_data <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -104,20 +108,23 @@ findScranMarkers <- function(gobject,
         expr_data <- expr_data[, colnames(expr_data) %in% subset_cell_IDs]
     } else if (!is.null(group_1) & !is.null(group_2)) {
         cell_metadata <- cell_metadata[
-            get(cluster_column) %in% c(group_1, group_2)]
+            get(cluster_column) %in% c(group_1, group_2)
+        ]
 
         # create new pairwise group
         if (!is.null(group_1_name)) {
-            if (!is.character(group_1_name))
+            if (!is.character(group_1_name)) {
                 stop("group_1_name needs to be a character")
+            }
             group_1_name <- group_1_name
         } else {
             group_1_name <- paste0(group_1, collapse = "_")
         }
 
         if (!is.null(group_2_name)) {
-            if (!is.character(group_2_name))
+            if (!is.character(group_2_name)) {
                 stop("group_2_name needs to be a character")
+            }
             group_2_name <- group_2_name
         } else {
             group_2_name <- paste0(group_2, collapse = "_")
@@ -128,7 +135,8 @@ findScranMarkers <- function(gobject,
         pairwise_select_comp <- NULL
 
         cell_metadata[, pairwise_select_comp := ifelse(
-            get(cluster_column) %in% group_1, group_1_name, group_2_name)]
+            get(cluster_column) %in% group_1, group_1_name, group_2_name
+        )]
 
         cluster_column <- "pairwise_select_comp"
 
@@ -140,7 +148,8 @@ findScranMarkers <- function(gobject,
 
     ## SCRAN ##
     marker_results <- scran::findMarkers(
-        x = expr_data, groups = cell_metadata[[cluster_column]], ...)
+        x = expr_data, groups = cell_metadata[[cluster_column]], ...
+    )
 
     # data.table variables
     genes <- cluster <- feats <- NULL
@@ -180,18 +189,19 @@ findScranMarkers <- function(gobject,
 #'
 #' findScranMarkers_one_vs_all(g, cluster_column = "leiden_clus")
 #' @export
-findScranMarkers_one_vs_all <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    cluster_column,
-    subset_clusters = NULL,
-    pval = 0.01,
-    logFC = 0.5,
-    min_feats = 10,
-    min_genes = NULL,
-    verbose = TRUE,
-    ...) {
+findScranMarkers_one_vs_all <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        cluster_column,
+        subset_clusters = NULL,
+        pval = 0.01,
+        logFC = 0.5,
+        min_feats = 10,
+        min_genes = NULL,
+        verbose = TRUE,
+        ...) {
     ## deprecated arguments
     if (!is.null(min_genes)) {
         min_feats <- min_genes
@@ -203,12 +213,13 @@ findScranMarkers_one_vs_all <- function(gobject,
     package_check(pkg_name = "scran", repository = "Bioc")
 
     # print message with information #
-    if (verbose)
-      message("using 'Scran' to detect marker feats. If used in published
+    if (verbose) {
+        message("using 'Scran' to detect marker feats. If used in published
       research, please cite: Lun ATL, McCarthy DJ, Marioni JC (2016).
       'A step-by-step workflow for low-level analysis of single-cell RNA-seq
       data with Bioconductor.'
       F1000Res., 5, 2122. doi: 10.12688/f1000research.9501.2. ")
+    }
 
 
     # Set feat_type and spat_unit
@@ -225,8 +236,11 @@ findScranMarkers_one_vs_all <- function(gobject,
     # expression data
     values <- match.arg(
         expression_values,
-        choices = unique(c("normalized", "scaled", "custom",
-                          expression_values)))
+        choices = unique(c(
+            "normalized", "scaled", "custom",
+            expression_values
+        ))
+    )
 
     # cluster column
     cell_metadata <- getCellMetadata(gobject,
@@ -294,7 +308,8 @@ findScranMarkers_one_vs_all <- function(gobject,
                     unique(x$cluster) == selected_clus
                 }))
                 selected_table <- data.table::as.data.table(
-                    markers[select_bool])
+                    markers[select_bool]
+                )
 
                 # remove summary column from scran output if present
                 col_ind_keep <- !grepl("summary", colnames(selected_table))
@@ -302,9 +317,11 @@ findScranMarkers_one_vs_all <- function(gobject,
 
                 # change logFC.xxx name to logFC
                 data.table::setnames(
-                    selected_table, colnames(selected_table)[4], "logFC")
+                    selected_table, colnames(selected_table)[4], "logFC"
+                )
                 data.table::setnames(
-                    selected_table, colnames(selected_table)[5], "feats")
+                    selected_table, colnames(selected_table)[5], "feats"
+                )
 
                 # filter selected table
                 filtered_table <- selected_table[logFC > 0]
@@ -314,7 +331,8 @@ findScranMarkers_one_vs_all <- function(gobject,
                 p.value <- ranking <- NULL
 
                 filtered_table <- filtered_table[
-                    (p.value <= pval & logFC >= logFC) | (ranking <= min_feats)]
+                    (p.value <= pval & logFC >= logFC) | (ranking <= min_feats)
+                ]
 
                 pb(message = c("cluster ", clus_i, "/", length(uniq_clusters)))
                 return(filtered_table)
@@ -385,22 +403,23 @@ findScranMarkers_one_vs_all <- function(gobject,
 #'
 #' findGiniMarkers(g, cluster_column = "leiden_clus")
 #' @export
-findGiniMarkers <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    cluster_column,
-    subset_clusters = NULL,
-    group_1 = NULL,
-    group_1_name = NULL,
-    group_2 = NULL,
-    group_2_name = NULL,
-    min_expr_gini_score = 0.2,
-    min_det_gini_score = 0.2,
-    detection_threshold = 0,
-    rank_score = 1,
-    min_feats = 5,
-    min_genes = NULL) {
+findGiniMarkers <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        cluster_column,
+        subset_clusters = NULL,
+        group_1 = NULL,
+        group_1_name = NULL,
+        group_2 = NULL,
+        group_2_name = NULL,
+        min_expr_gini_score = 0.2,
+        min_det_gini_score = 0.2,
+        detection_threshold = 0,
+        rank_score = 1,
+        min_feats = 5,
+        min_genes = NULL) {
     ## deprecated arguments
     if (!is.null(min_genes)) {
         min_feats <- min_genes
@@ -422,7 +441,8 @@ findGiniMarkers <- function(gobject,
     ## select expression values
     values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
 
 
     # cluster column
@@ -441,7 +461,8 @@ findGiniMarkers <- function(gobject,
     # subset clusters
     if (!is.null(subset_clusters)) {
         cell_metadata[] <- cell_metadata[][
-            get(cluster_column) %in% subset_clusters]
+            get(cluster_column) %in% subset_clusters
+        ]
         subset_cell_IDs <- cell_metadata[][["cell_ID"]]
         gobject <- subsetGiotto(
             gobject = gobject,
@@ -451,20 +472,23 @@ findGiniMarkers <- function(gobject,
         )
     } else if (!is.null(group_1) & !is.null(group_2)) {
         cell_metadata[] <- cell_metadata[][
-            get(cluster_column) %in% c(group_1, group_2)]
+            get(cluster_column) %in% c(group_1, group_2)
+        ]
 
         # create new pairwise group
         if (!is.null(group_1_name)) {
-            if (!is.character(group_1_name))
+            if (!is.character(group_1_name)) {
                 stop("group_1_name needs to be a character")
+            }
             group_1_name <- group_1_name
         } else {
             group_1_name <- paste0(group_1, collapse = "_")
         }
 
         if (!is.null(group_2_name)) {
-            if (!is.character(group_2_name))
+            if (!is.character(group_2_name)) {
                 stop("group_2_name needs to be a character")
+            }
             group_2_name <- group_2_name
         } else {
             group_2_name <- paste0(group_2, collapse = "_")
@@ -473,7 +497,8 @@ findGiniMarkers <- function(gobject,
         pairwise_select_comp <- NULL
 
         cell_metadata[][, pairwise_select_comp := ifelse(
-            get(cluster_column) %in% group_1, group_1_name, group_2_name)]
+            get(cluster_column) %in% group_1, group_1_name, group_2_name
+        )]
 
         cluster_column <- "pairwise_select_comp"
 
@@ -526,9 +551,11 @@ findGiniMarkers <- function(gobject,
         detection_threshold = detection_threshold
     )
     aggr_detection_sc_clusters_DT <- data.table::as.data.table(
-        aggr_detection_sc_clusters)
+        aggr_detection_sc_clusters
+    )
     aggr_detection_sc_clusters_DT[, feats := rownames(
-        aggr_detection_sc_clusters)]
+        aggr_detection_sc_clusters
+    )]
     aggr_detection_sc_clusters_DT_melt <- data.table::melt.data.table(
         aggr_detection_sc_clusters_DT,
         variable.name = "cluster",
@@ -541,15 +568,20 @@ findGiniMarkers <- function(gobject,
     expression_gini <- detection_gini <- detection <- NULL
 
     aggr_sc_clusters_DT_melt[, expression_gini := mygini_fun(
-        expression), by = feats]
+        expression
+    ), by = feats]
     aggr_detection_sc_clusters_DT_melt[, detection_gini := mygini_fun(
-        detection), by = feats]
+        detection
+    ), by = feats]
 
 
     ## combine
-    aggr_sc <- cbind(aggr_sc_clusters_DT_melt,
-                    aggr_detection_sc_clusters_DT_melt[
-                    , .(detection, detection_gini)])
+    aggr_sc <- cbind(
+        aggr_sc_clusters_DT_melt,
+        aggr_detection_sc_clusters_DT_melt[
+            , .(detection, detection_gini)
+        ]
+    )
 
     ## create combined rank
 
@@ -561,13 +593,17 @@ findGiniMarkers <- function(gobject,
 
     aggr_sc[, expression_rank := rank(-expression), by = feats]
     aggr_sc[, expression_rank := scales::rescale(
-        expression_rank, to = c(1, 0.1)), by = cluster]
+        expression_rank,
+        to = c(1, 0.1)
+    ), by = cluster]
 
     # detection rank for each feat in all samples
     # rescale detection rank range between 1 and 0.1
     aggr_sc[, detection_rank := rank(-detection), by = feats]
     aggr_sc[, detection_rank := scales::rescale(
-        detection_rank, to = c(1, 0.1)), by = cluster]
+        detection_rank,
+        to = c(1, 0.1)
+    ), by = cluster]
 
     # create combine score based on rescaled ranks and gini scores
 
@@ -577,7 +613,7 @@ findGiniMarkers <- function(gobject,
     aggr_sc[, comb_score := (expression_gini * expression_rank) * (
         detection_gini * detection_rank)]
     setorder(aggr_sc, cluster, -comb_score)
-    aggr_sc[, comb_rank := 1:.N, by = cluster]
+    aggr_sc[, comb_rank := seq_len(.N), by = cluster]
 
     top_feats_scores <- aggr_sc[comb_rank <= min_feats | (
         expression_rank <= rank_score & detection_rank <= rank_score)]
@@ -590,7 +626,8 @@ findGiniMarkers <- function(gobject,
     original_uniq_cluster_names <- unique(cell_metadata[][[cluster_column]])
     if (sum(grepl("cluster_", original_uniq_cluster_names)) == 0) {
         top_feats_scores_filtered[, cluster := gsub(
-            x = cluster, "cluster_", "")]
+            x = cluster, "cluster_", ""
+        )]
     }
 
     return(top_feats_scores_filtered)
@@ -623,19 +660,20 @@ findGiniMarkers <- function(gobject,
 #'
 #' findGiniMarkers_one_vs_all(g, cluster_column = "leiden_clus")
 #' @export
-findGiniMarkers_one_vs_all <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    cluster_column,
-    subset_clusters = NULL,
-    min_expr_gini_score = 0.5,
-    min_det_gini_score = 0.5,
-    detection_threshold = 0,
-    rank_score = 1,
-    min_feats = 4,
-    min_genes = NULL,
-    verbose = TRUE) {
+findGiniMarkers_one_vs_all <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        cluster_column,
+        subset_clusters = NULL,
+        min_expr_gini_score = 0.5,
+        min_det_gini_score = 0.5,
+        detection_threshold = 0,
+        rank_score = 1,
+        min_feats = 4,
+        min_genes = NULL,
+        verbose = TRUE) {
     ## deprecated arguments
     if (!is.null(min_genes)) {
         min_feats <- min_genes
@@ -657,7 +695,8 @@ findGiniMarkers_one_vs_all <- function(gobject,
     ## select expression values
     values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
 
 
     # cluster column
@@ -767,21 +806,24 @@ findGiniMarkers_one_vs_all <- function(gobject,
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #'
-#' findMastMarkers(gobject = g, cluster_column = "leiden_clus", group_1 = 1,
-#' group_2 = 2)
+#' findMastMarkers(
+#'     gobject = g, cluster_column = "leiden_clus", group_1 = 1,
+#'     group_2 = 2
+#' )
 #' @export
-findMastMarkers <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    cluster_column,
-    group_1 = NULL,
-    group_1_name = NULL,
-    group_2 = NULL,
-    group_2_name = NULL,
-    adjust_columns = NULL,
-    verbose = FALSE,
-    ...) {
+findMastMarkers <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        cluster_column,
+        group_1 = NULL,
+        group_1_name = NULL,
+        group_2 = NULL,
+        group_2_name = NULL,
+        adjust_columns = NULL,
+        verbose = FALSE,
+        ...) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -797,16 +839,18 @@ findMastMarkers <- function(gobject,
     package_check(pkg_name = "MAST", repository = "Bioc")
 
     # print message with information #
-    if (verbose)
-      message("using 'MAST' to detect marker feats. If used in published
+    if (verbose) {
+        message("using 'MAST' to detect marker feats. If used in published
       research, please cite: McDavid A, Finak G, Yajima M (2020).
       MAST: Model-based Analysis of Single Cell Transcriptomics.
       R package version 1.14.0, https://github.com/RGLab/MAST/.")
+    }
 
     ## select expression values to use
     values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
 
     ## cluster column
     cell_metadata <- getCellMetadata(gobject,
@@ -826,7 +870,8 @@ findMastMarkers <- function(gobject,
 
     ## subset data based on group_1 and group_2
     cell_metadata[] <- cell_metadata[][
-        get(cluster_column) %in% c(group_1, group_2)]
+        get(cluster_column) %in% c(group_1, group_2)
+    ]
     if (nrow(cell_metadata[]) == 0) {
         stop("there are no cells for group_1 or group_2, check cluster column")
     }
@@ -839,7 +884,8 @@ findMastMarkers <- function(gobject,
     pairwise_select_comp <- NULL
 
     cell_metadata[][, pairwise_select_comp := ifelse(
-        get(cluster_column) %in% group_1, group_1_name, group_2_name)]
+        get(cluster_column) %in% group_1, group_1_name, group_2_name
+    )]
 
     if (nrow(cell_metadata[][pairwise_select_comp == group_1_name]) == 0) {
         stop("there are no cells for group_1, check cluster column")
@@ -873,8 +919,11 @@ findMastMarkers <- function(gobject,
     # expression data
     values <- match.arg(
         expression_values,
-        choices = unique(c("normalized", "scaled", "custom",
-                          expression_values)))
+        choices = unique(c(
+            "normalized", "scaled", "custom",
+            expression_values
+        ))
+    )
     expr_data <- getExpression(
         gobject = gobject,
         feat_type = feat_type,
@@ -914,7 +963,8 @@ findMastMarkers <- function(gobject,
     if (!is.null(adjust_columns)) {
         myformula <- stats::as.formula(paste0(
             "~ 1 + ", cluster_column, " + ",
-            paste(adjust_columns, collapse = " + ")))
+            paste(adjust_columns, collapse = " + ")
+        ))
     } else {
         myformula <- stats::as.formula(paste0("~ 1 + ", cluster_column))
     }
@@ -929,12 +979,15 @@ findMastMarkers <- function(gobject,
     sample <- paste0(cluster_column, group_1_name)
     summaryCond <- MAST::summary(zlmCond, doLRT = sample)
     summaryDt <- summaryCond$datatable
-    fcHurdle <- merge(summaryDt[
-        contrast == sample & component == "H",
-        .(primerid, `Pr(>Chisq)`)], # hurdle P values
+    fcHurdle <- merge(
+        summaryDt[
+            contrast == sample & component == "H",
+            .(primerid, `Pr(>Chisq)`)
+        ], # hurdle P values
         summaryDt[
             contrast == sample & component == "logFC",
-            .(primerid, coef, ci.hi, ci.lo)],
+            .(primerid, coef, ci.hi, ci.lo)
+        ],
         by = "primerid"
     ) # logFC coefficients
     fcHurdle[, fdr := stats::p.adjust(`Pr(>Chisq)`, "fdr")]
@@ -976,19 +1029,20 @@ findMastMarkers <- function(gobject,
 #'
 #' findMastMarkers_one_vs_all(gobject = g, cluster_column = "leiden_clus")
 #' @export
-findMastMarkers_one_vs_all <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    cluster_column,
-    subset_clusters = NULL,
-    adjust_columns = NULL,
-    pval = 0.001,
-    logFC = 1,
-    min_feats = 10,
-    min_genes = NULL,
-    verbose = TRUE,
-    ...) {
+findMastMarkers_one_vs_all <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        cluster_column,
+        subset_clusters = NULL,
+        adjust_columns = NULL,
+        pval = 0.001,
+        logFC = 1,
+        min_feats = 10,
+        min_genes = NULL,
+        verbose = TRUE,
+        ...) {
     ## deprecated arguments
     if (!is.null(min_genes)) {
         min_feats <- min_genes
@@ -1011,11 +1065,12 @@ findMastMarkers_one_vs_all <- function(gobject,
     package_check(pkg_name = "MAST", repository = "Bioc")
 
     # print message with information #
-    if (verbose)
+    if (verbose) {
         message("using 'MAST' to detect marker feats. If used in published
         research, please cite: McDavid A, Finak G, Yajima M (2020).
         MAST: Model-based Analysis of Single Cell Transcriptomics.
         R package version 1.14.0, https://github.com/RGLab/MAST/.")
+    }
 
 
     ## cluster column
@@ -1087,7 +1142,8 @@ findMastMarkers_one_vs_all <- function(gobject,
 
     result_dt[, ranking := seq_len(.N), by = "cluster"]
     filtered_result_dt <- result_dt[
-        ranking <= min_feats | (fdr < pval & coef > logFC)]
+        ranking <= min_feats | (fdr < pval & coef > logFC)
+    ]
 
     return(filtered_result_dt)
 }
@@ -1134,25 +1190,26 @@ findMastMarkers_one_vs_all <- function(gobject,
 #'
 #' findMarkers(g, cluster_column = "leiden_clus")
 #' @export
-findMarkers <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    cluster_column = NULL,
-    method = c("scran", "gini", "mast"),
-    subset_clusters = NULL,
-    group_1 = NULL,
-    group_2 = NULL,
-    min_expr_gini_score = 0.5,
-    min_det_gini_score = 0.5,
-    detection_threshold = 0,
-    rank_score = 1,
-    min_feats = 4,
-    min_genes = NULL,
-    group_1_name = NULL,
-    group_2_name = NULL,
-    adjust_columns = NULL,
-    ...) {
+findMarkers <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        cluster_column = NULL,
+        method = c("scran", "gini", "mast"),
+        subset_clusters = NULL,
+        group_1 = NULL,
+        group_2 = NULL,
+        min_expr_gini_score = 0.5,
+        min_det_gini_score = 0.5,
+        detection_threshold = 0,
+        rank_score = 1,
+        min_feats = 4,
+        min_genes = NULL,
+        group_1_name = NULL,
+        group_2_name = NULL,
+        adjust_columns = NULL,
+        ...) {
     ## deprecated arguments
     if (!is.null(min_genes)) {
         min_feats <- min_genes
@@ -1256,27 +1313,28 @@ findMarkers <- function(gobject,
 #'
 #' findMarkers_one_vs_all(g, cluster_column = "leiden_clus")
 #' @export
-findMarkers_one_vs_all <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    cluster_column,
-    subset_clusters = NULL,
-    method = c("scran", "gini", "mast"),
-    # scran & mast
-    pval = 0.01,
-    logFC = 0.5,
-    min_feats = 10,
-    min_genes = NULL,
-    # gini
-    min_expr_gini_score = 0.5,
-    min_det_gini_score = 0.5,
-    detection_threshold = 0,
-    rank_score = 1,
-    # mast specific
-    adjust_columns = NULL,
-    verbose = TRUE,
-    ...) {
+findMarkers_one_vs_all <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        cluster_column,
+        subset_clusters = NULL,
+        method = c("scran", "gini", "mast"),
+        # scran & mast
+        pval = 0.01,
+        logFC = 0.5,
+        min_feats = 10,
+        min_genes = NULL,
+        # gini
+        min_expr_gini_score = 0.5,
+        min_det_gini_score = 0.5,
+        detection_threshold = 0,
+        rank_score = 1,
+        # mast specific
+        adjust_columns = NULL,
+        verbose = TRUE,
+        ...) {
     ## deprecated arguments
     if (!is.null(min_genes)) {
         min_feats <- min_genes
diff --git a/R/dimension_reduction.R b/R/dimension_reduction.R
index e384b7f1b..0e4bcbf70 100644
--- a/R/dimension_reduction.R
+++ b/R/dimension_reduction.R
@@ -19,13 +19,14 @@
 #' @param seed_number seed number to use
 #' @keywords internal
 #' @returns list of eigenvalues, loadings and pca coordinates
-.run_pca_factominer <- function(x,
-    ncp = 100,
-    scale = TRUE,
-    rev = FALSE,
-    set_seed = TRUE,
-    seed_number = 1234,
-    ...) {
+.run_pca_factominer <- function(
+        x,
+        ncp = 100,
+        scale = TRUE,
+        rev = FALSE,
+        set_seed = TRUE,
+        seed_number = 1234,
+        ...) {
     # verify if optional package is installed
     package_check(pkg_name = "FactoMineR", repository = "CRAN")
 
@@ -47,7 +48,8 @@
         }
 
         pca_res <- FactoMineR::PCA(
-            X = x, ncp = ncp, scale.unit = scale, graph = FALSE, ...)
+            X = x, ncp = ncp, scale.unit = scale, graph = FALSE, ...
+        )
 
         # exit seed
         if (isTRUE(set_seed)) {
@@ -60,16 +62,19 @@
         # PC loading
         loadings <- pca_res$ind$coord
         rownames(loadings) <- rownames(x)
-        colnames(loadings) <- paste0("Dim.", 1:ncol(loadings))
+        colnames(loadings) <- paste0("Dim.", seq_len(ncol(loadings)))
 
         # coordinates
-        coords <- sweep(pca_res$var$coord, 
-                        2, sqrt(eigenvalues[1:ncp]), FUN = "/")
+        coords <- sweep(pca_res$var$coord,
+            2, sqrt(eigenvalues[seq_len(ncp)]),
+            FUN = "/"
+        )
         rownames(coords) <- colnames(x)
-        colnames(coords) <- paste0("Dim.", 1:ncol(coords))
+        colnames(coords) <- paste0("Dim.", seq_len(ncol(coords)))
 
         result <- list(
-            eigenvalues = eigenvalues, loadings = loadings, coords = coords)
+            eigenvalues = eigenvalues, loadings = loadings, coords = coords
+        )
     } else {
         if (ncp > ncol(x)) {
             warning("ncp > ncol(x), will be set to ncol(x)")
@@ -82,7 +87,8 @@
         }
 
         pca_res <- FactoMineR::PCA(
-            X = x, ncp = ncp, scale.unit = scale, graph = FALSE, ...)
+            X = x, ncp = ncp, scale.unit = scale, graph = FALSE, ...
+        )
 
         # exit seed
         if (isTRUE(set_seed)) {
@@ -94,21 +100,26 @@
 
         # PC loading
         loadings <- sweep(
-            pca_res$var$coord, 2, sqrt(eigenvalues[1:ncp]), FUN = "/")
+            pca_res$var$coord, 2, sqrt(eigenvalues[seq_len(ncp)]),
+            FUN = "/"
+        )
         rownames(loadings) <- colnames(x)
-        colnames(loadings) <- paste0("Dim.", 1:ncol(loadings))
+        colnames(loadings) <- paste0("Dim.", seq_len(ncol(loadings)))
 
         # coordinates
         coords <- pca_res$ind$coord
         rownames(coords) <- rownames(x)
-        colnames(coords) <- paste0("Dim.", 1:ncol(coords))
+        colnames(coords) <- paste0("Dim.", seq_len(ncol(coords)))
 
         result <- list(
-            eigenvalues = eigenvalues, loadings = loadings, coords = coords)
+            eigenvalues = eigenvalues, loadings = loadings, coords = coords
+        )
     }
 
-    vmsg(.is_debug = TRUE, 
-        "finished .run_pca_factominer, method == factominer")
+    vmsg(
+        .is_debug = TRUE,
+        "finished .run_pca_factominer, method == factominer"
+    )
 
     return(result)
 }
@@ -128,22 +139,23 @@
 #' @param BPPARAM BiocParallelParam object
 #' @keywords internal
 #' @returns list of eigenvalues, loadings and pca coordinates
-.run_pca_biocsingular <- function(x,
-    ncp = 100,
-    center = TRUE,
-    scale = TRUE,
-    rev = FALSE,
-    set_seed = TRUE,
-    seed_number = 1234,
-    BSPARAM = c("irlba", "exact", "random"),
-    BPPARAM = BiocParallel::SerialParam(),
-    ...) {
+.run_pca_biocsingular <- function(
+        x,
+        ncp = 100,
+        center = TRUE,
+        scale = TRUE,
+        rev = FALSE,
+        set_seed = TRUE,
+        seed_number = 1234,
+        BSPARAM = c("irlba", "exact", "random"),
+        BPPARAM = BiocParallel::SerialParam(),
+        ...) {
     BSPARAM <- match.arg(BSPARAM, choices = c("irlba", "exact", "random"))
 
     min_ncp <- min(dim(x))
 
     if (ncp >= min_ncp) {
-        warning("ncp >= minimum dimension of x, will be set to 
+        warning("ncp >= minimum dimension of x, will be set to
                 minimum dimension of x - 1")
         ncp <- min_ncp - 1
     }
@@ -189,13 +201,14 @@
         # PC loading
         loadings <- pca_res$x
         rownames(loadings) <- rownames(x)
-        colnames(loadings) <- paste0("Dim.", 1:ncol(loadings))
+        colnames(loadings) <- paste0("Dim.", seq_len(ncol(loadings)))
         # coordinates
         coords <- pca_res$rotation
         rownames(coords) <- colnames(x)
-        colnames(coords) <- paste0("Dim.", 1:ncol(coords))
+        colnames(coords) <- paste0("Dim.", seq_len(ncol(coords)))
         result <- list(
-            eigenvalues = eigenvalues, loadings = loadings, coords = coords)
+            eigenvalues = eigenvalues, loadings = loadings, coords = coords
+        )
     } else {
         if (BSPARAM == "irlba") {
             pca_res <- BiocSingular::runPCA(
@@ -228,13 +241,14 @@
         # PC loading
         loadings <- pca_res$rotation
         rownames(loadings) <- colnames(x)
-        colnames(loadings) <- paste0("Dim.", 1:ncol(loadings))
+        colnames(loadings) <- paste0("Dim.", seq_len(ncol(loadings)))
         # coordinates
         coords <- pca_res$x
         rownames(coords) <- rownames(x)
-        colnames(coords) <- paste0("Dim.", 1:ncol(coords))
+        colnames(coords) <- paste0("Dim.", seq_len(ncol(coords)))
         result <- list(
-            eigenvalues = eigenvalues, loadings = loadings, coords = coords)
+            eigenvalues = eigenvalues, loadings = loadings, coords = coords
+        )
     }
 
     # exit seed
@@ -263,12 +277,13 @@
 #' @param verbose verbosity
 #' @keywords internal
 #' @returns subsetted matrix based on selected features
-.create_feats_to_use_matrix <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    sel_matrix,
-    feats_to_use,
-    verbose = FALSE) {
+.create_feats_to_use_matrix <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        sel_matrix,
+        feats_to_use,
+        verbose = FALSE) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -291,11 +306,12 @@
         if (feats_to_use %in% colnames(feat_metadata)) {
             vmsg(
                 .v = verbose, str_double_quote(feats_to_use),
-                "column was found in the feats metadata information and will be 
+                "column was found in the feats metadata information and will be
                 used to select highly variable features"
             )
             feats_to_use <- feat_metadata[
-                get(feats_to_use) == "yes"][["feat_ID"]]
+                get(feats_to_use) == "yes"
+            ][["feat_ID"]]
             sel_matrix <- sel_matrix[rownames(sel_matrix) %in% feats_to_use, ]
         } else {
             vmsg(
@@ -305,13 +321,17 @@
             )
         }
     } else {
-        vmsg(.v = verbose, 
-            "a custom vector of genes will be used to subset the matrix")
+        vmsg(
+            .v = verbose,
+            "a custom vector of genes will be used to subset the matrix"
+        )
         sel_matrix <- sel_matrix[rownames(sel_matrix) %in% feats_to_use, ]
     }
 
-    vmsg(.v = verbose, .is_debug = TRUE, 
-        "class of selected matrix: ", class(sel_matrix))
+    vmsg(
+        .v = verbose, .is_debug = TRUE,
+        "class of selected matrix: ", class(sel_matrix)
+    )
 
     return(sel_matrix)
 }
@@ -349,7 +369,7 @@
 #'   \item feats_to_use = NULL: will use all features from the selected matrix
 #'   \item feats_to_use = <hvg name>: can be used to select a column name of
 #'   highly variable features, created by (see \code{\link{calculateHVF}})
-#'   \item feats_to_use = c('geneA', 'geneB', ...): will use all manually 
+#'   \item feats_to_use = c('geneA', 'geneB', ...): will use all manually
 #'   provided features
 #' }
 #' By default the number of principle components that we calculate is 100, which
@@ -357,27 +377,28 @@
 #' will calculate all the principle components.
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' runPCA(g)
 #' @export
-runPCA <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    reduction = c("cells", "feats"),
-    name = NULL,
-    feats_to_use = "hvf",
-    return_gobject = TRUE,
-    center = TRUE,
-    scale_unit = TRUE,
-    ncp = 100,
-    method = c("irlba", "exact", "random", "factominer"),
-    method_params = BiocParallel::SerialParam(),
-    rev = FALSE,
-    set_seed = TRUE,
-    seed_number = 1234,
-    verbose = TRUE,
-    ...) {
+runPCA <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        reduction = c("cells", "feats"),
+        name = NULL,
+        feats_to_use = "hvf",
+        return_gobject = TRUE,
+        center = TRUE,
+        scale_unit = TRUE,
+        ncp = 100,
+        method = c("irlba", "exact", "random", "factominer"),
+        method_params = BiocParallel::SerialParam(),
+        rev = FALSE,
+        set_seed = TRUE,
+        seed_number = 1234,
+        verbose = TRUE,
+        ...) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -400,8 +421,9 @@ runPCA <- function(gobject,
 
     # expression values to be used
     values <- match.arg(
-        expression_values, 
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        expression_values,
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_values <- getExpression(
         gobject = gobject,
         feat_type = feat_type,
@@ -463,7 +485,7 @@ runPCA <- function(gobject,
                 ...
             )
         } else {
-            stop("only PCA methods from the BiocSingular and factominer 
+            stop("only PCA methods from the BiocSingular and factominer
                 package have been implemented")
         }
     } else {
@@ -488,7 +510,7 @@ runPCA <- function(gobject,
                 set_seed = set_seed, seed_number = seed_number, ...
             )
         } else {
-            stop("only PCA methods from the irlba and factominer package have 
+            stop("only PCA methods from the irlba and factominer package have
                 been implemented")
         }
     }
@@ -497,18 +519,6 @@ runPCA <- function(gobject,
 
 
     if (isTRUE(return_gobject)) {
-        pca_names <- list_dim_reductions_names(
-            gobject = gobject,
-            data_type = reduction,
-            spat_unit = spat_unit,
-            feat_type = feat_type,
-            dim_type = "pca"
-        )
-
-        if (name %in% pca_names) {
-            cat(name, " has already been used, will be overwritten")
-        }
-
         if (reduction == "cells") {
             my_row_names <- colnames(expr_values)
         } else {
@@ -531,7 +541,9 @@ runPCA <- function(gobject,
         )
 
         ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
-        gobject <- set_dimReduction(gobject = gobject, dimObject = dimObject)
+        gobject <- set_dimReduction(
+            gobject = gobject, dimObject = dimObject, verbose = verbose
+        )
         ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
 
 
@@ -570,23 +582,24 @@ runPCA <- function(gobject,
 #' @param verbose verbosity level
 #' @keywords internal
 #' @returns list of eigenvalues, loadings and pca coordinates
-.run_pca_biocsingular_irlba_projection <- function(x,
-    ncp = 100,
-    center = TRUE,
-    scale = TRUE,
-    rev = FALSE,
-    set_seed = TRUE,
-    seed_number = 1234,
-    BPPARAM = BiocParallel::SerialParam(),
-    random_subset = 500,
-    verbose = TRUE,
-    ...) {
+.run_pca_biocsingular_irlba_projection <- function(
+        x,
+        ncp = 100,
+        center = TRUE,
+        scale = TRUE,
+        rev = FALSE,
+        set_seed = TRUE,
+        seed_number = 1234,
+        BPPARAM = BiocParallel::SerialParam(),
+        random_subset = 500,
+        verbose = TRUE,
+        ...) {
     x <- scale(x, center = center, scale = scale)
 
     min_ncp <- min(dim(x))
 
     if (ncp >= min_ncp) {
-        warning("ncp >= minimum dimension of x, will be set to minimum 
+        warning("ncp >= minimum dimension of x, will be set to minimum
                 dimension of x - 1")
         ncp <- min_ncp - 1
     }
@@ -607,7 +620,7 @@ runPCA <- function(gobject,
         cell_ID_order <- rownames(x)
 
         # create random selection
-        random_selection <- sort(sample(1:nrow(x), random_subset))
+        random_selection <- sort(sample(seq_len(nrow(x)), random_subset))
         subsample_matrix <- x[random_selection, ]
 
 
@@ -647,21 +660,22 @@ runPCA <- function(gobject,
         # PC loading
         loadings <- coords
         rownames(loadings) <- rownames(x)
-        colnames(loadings) <- paste0("Dim.", 1:ncol(loadings))
+        colnames(loadings) <- paste0("Dim.", seq_len(ncol(loadings)))
 
         # coordinates
         coords <- pca_res$rotation
         rownames(coords) <- colnames(x)
-        colnames(coords) <- paste0("Dim.", 1:ncol(coords))
+        colnames(coords) <- paste0("Dim.", seq_len(ncol(coords)))
 
         result <- list(
-            eigenvalues = eigenvalues, loadings = loadings, coords = coords)
+            eigenvalues = eigenvalues, loadings = loadings, coords = coords
+        )
     } else {
         # store cell ID order information
         cell_ID_order <- rownames(x)
 
         # create random selection
-        random_selection <- sort(sample(1:nrow(x), random_subset))
+        random_selection <- sort(sample(seq_len(nrow(x)), random_subset))
         subsample_matrix <- x[random_selection, ]
 
         if (verbose) message("pca random subset: start")
@@ -699,13 +713,14 @@ runPCA <- function(gobject,
         # PC loading
         loadings <- pca_res$rotation
         rownames(loadings) <- colnames(x)
-        colnames(loadings) <- paste0("Dim.", 1:ncol(loadings))
+        colnames(loadings) <- paste0("Dim.", seq_len(ncol(loadings)))
 
         # coordinates
-        colnames(coords) <- paste0("Dim.", 1:ncol(coords))
+        colnames(coords) <- paste0("Dim.", seq_len(ncol(coords)))
 
         result <- list(
-            eigenvalues = eigenvalues, loadings = loadings, coords = coords)
+            eigenvalues = eigenvalues, loadings = loadings, coords = coords
+        )
     }
 
     return(result)
@@ -721,7 +736,7 @@ runPCA <- function(gobject,
 
 #' @title runPCAprojection
 #' @name runPCAprojection
-#' @description runs a Principal Component Analysis on a random 
+#' @description runs a Principal Component Analysis on a random
 #' subset + projection
 #' @param gobject giotto object
 #' @param spat_unit spatial unit
@@ -743,7 +758,7 @@ runPCA <- function(gobject,
 #' @param verbose verbosity of the function
 #' @param ... additional parameters for PCA (see details)
 #' @returns giotto object with updated PCA dimension recuction
-#' @details See \code{\link[BiocSingular]{runPCA}} and 
+#' @details See \code{\link[BiocSingular]{runPCA}} and
 #' \code{\link[FactoMineR]{PCA}} for more information about other parameters.
 #' This PCA implementation is similar to  \code{\link{runPCA}}, except that it
 #' performs PCA on a subset of the cells or features, and predict on the others.
@@ -752,33 +767,34 @@ runPCA <- function(gobject,
 #'   \item feats_to_use = NULL: will use all features from the selected matrix
 #'   \item feats_to_use = <hvg name>: can be used to select a column name of
 #'   highly variable features, created by (see \code{\link{calculateHVF}})
-#'   \item feats_to_use = c('geneA', 'geneB', ...): will use all manually 
+#'   \item feats_to_use = c('geneA', 'geneB', ...): will use all manually
 #'   provided features
 #' }
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' runPCAprojection(g)
 #' @export
-runPCAprojection <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    reduction = c("cells", "feats"),
-    random_subset = 500,
-    name = "pca.projection",
-    feats_to_use = "hvf",
-    return_gobject = TRUE,
-    center = TRUE,
-    scale_unit = TRUE,
-    ncp = 100,
-    method = c("irlba"),
-    method_params = BiocParallel::SerialParam(),
-    rev = FALSE,
-    set_seed = TRUE,
-    seed_number = 1234,
-    verbose = TRUE,
-    ...) {
+runPCAprojection <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        reduction = c("cells", "feats"),
+        random_subset = 500,
+        name = "pca.projection",
+        feats_to_use = "hvf",
+        return_gobject = TRUE,
+        center = TRUE,
+        scale_unit = TRUE,
+        ncp = 100,
+        method = c("irlba"),
+        method_params = BiocParallel::SerialParam(),
+        rev = FALSE,
+        set_seed = TRUE,
+        seed_number = 1234,
+        verbose = TRUE,
+        ...) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -801,8 +817,9 @@ runPCAprojection <- function(gobject,
 
     # expression values to be used
     values <- match.arg(
-        expression_values, 
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        expression_values,
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_values <- getExpression(
         gobject = gobject,
         feat_type = feat_type,
@@ -946,7 +963,7 @@ runPCAprojection <- function(gobject,
 
 #' @title runPCAprojectionBatch
 #' @name runPCAprojectionBatch
-#' @description runs a Principal Component Analysis on multiple random 
+#' @description runs a Principal Component Analysis on multiple random
 #' batches + projection
 #' @param gobject giotto object
 #' @param spat_unit spatial unit
@@ -969,46 +986,49 @@ runPCAprojection <- function(gobject,
 #' @param verbose verbosity of the function
 #' @param ... additional parameters for PCA (see details)
 #' @returns giotto object with updated PCA dimension reduction
-#' @details See \code{\link[BiocSingular]{runPCA}} and 
+#' @details See \code{\link[BiocSingular]{runPCA}} and
 #' \code{\link[FactoMineR]{PCA}} for more information about other parameters.
-#' This PCA implementation is similar to  \code{\link{runPCA}} and  
+#' This PCA implementation is similar to  \code{\link{runPCA}} and
 #' \code{\link{runPCAprojection}},
-#' except that it performs PCA on multiple subsets (batches) of the cells or 
+#' except that it performs PCA on multiple subsets (batches) of the cells or
 #' features,
-#' and predict on the others. This can significantly increase speed without 
+#' and predict on the others. This can significantly increase speed without
 #' sacrificing accuracy too much.
 #' \itemize{
 #'   \item feats_to_use = NULL: will use all features from the selected matrix
 #'   \item feats_to_use = <hvg name>: can be used to select a column name of
 #'   highly variable features, created by (see \code{\link{calculateHVF}})
-#'   \item feats_to_use = c('geneA', 'geneB', ...): will use all manually 
+#'   \item feats_to_use = c('geneA', 'geneB', ...): will use all manually
 #'   provided features
 #' }
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' runPCAprojectionBatch(g)
+#'
+#' # set feats_to_use to NULL since there are not many hvfs
+#' # (only 48 in this mini dataset)
+#' runPCAprojectionBatch(g, feats_to_use = NULL)
 #' @export
-runPCAprojectionBatch <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    reduction = c("cells", "feats"),
-    random_subset = 500,
-    batch_number = 5,
-    name = "pca.projection.batch",
-    feats_to_use = "hvf",
-    return_gobject = TRUE,
-    center = TRUE,
-    scale_unit = TRUE,
-    ncp = 100,
-    method = c("irlba"),
-    method_params = BiocParallel::SerialParam(),
-    rev = FALSE,
-    set_seed = TRUE,
-    seed_number = 1234,
-    verbose = TRUE,
-    ...) {
+runPCAprojectionBatch <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        reduction = c("cells", "feats"),
+        random_subset = 500,
+        batch_number = 5,
+        name = "pca.projection.batch",
+        feats_to_use = "hvf",
+        return_gobject = TRUE,
+        center = TRUE,
+        scale_unit = TRUE,
+        ncp = 100,
+        method = c("irlba"),
+        method_params = BiocParallel::SerialParam(),
+        rev = FALSE,
+        set_seed = TRUE,
+        seed_number = 1234,
+        verbose = TRUE,
+        ...) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -1031,8 +1051,9 @@ runPCAprojectionBatch <- function(gobject,
 
     # expression values to be used
     values <- match.arg(
-        expression_values, 
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        expression_values,
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_values <- getExpression(
         gobject = gobject,
         feat_type = feat_type,
@@ -1071,7 +1092,6 @@ runPCAprojectionBatch <- function(gobject,
     }
 
 
-
     ## subset matrix
     if (!is.null(feats_to_use)) {
         expr_values <- .create_feats_to_use_matrix(
@@ -1085,6 +1105,15 @@ runPCAprojectionBatch <- function(gobject,
     }
 
 
+    if (ncp >= nrow(expr_values)) {
+        ncp <- nrow(expr_values) - 1L
+        warning(wrap_txt(
+            "ncp >= number of available features
+            ncp will be set to minimum of n features - 1"
+        ))
+    }
+
+
     # do PCA dimension reduction
     reduction <- match.arg(reduction, c("cells", "feats"))
 
@@ -1094,8 +1123,8 @@ runPCAprojectionBatch <- function(gobject,
     if (reduction == "cells") {
         pca_batch_results <- list()
 
-        for (batch in 1:batch_number) {
-            if (verbose) wrap_msg("start batch ", batch)
+        for (batch in seq_len(batch_number)) {
+            vmsg(.v = verbose, "start batch ", batch)
 
             if (isTRUE(set_seed)) {
                 seed_batch <- seed_number + batch
@@ -1119,15 +1148,16 @@ runPCAprojectionBatch <- function(gobject,
                 ...
             )
 
-            # adjust the sign of the coordinates and loadings vector relative 
+            # adjust the sign of the coordinates and loadings vector relative
             # to first batch this is necessary for the next averaging step
             if (batch == 1) {
                 pca_batch_results[[batch]] <- pca_object
             } else {
-                for (dimension in 1:ncol(pca_object[["coords"]])) {
+                for (dimension in seq_len(ncol(pca_object[["coords"]]))) {
                     sum_evaluation <- sum(sign(pca_batch_results[[1]][[
-                        "coords"]][1:20, dimension]) *
-                        sign(pca_object[["coords"]][1:20, dimension]))
+                        "coords"
+                    ]][seq_len(20), dimension]) *
+                        sign(pca_object[["coords"]][seq_len(20), dimension]))
                     if (sum_evaluation < 0) {
                         pca_object$coords[, dimension] <- -1 * pca_object$coords[, dimension]
                         pca_object$loadings[, dimension] <- -1 * pca_object$loadings[, dimension]
@@ -1137,7 +1167,7 @@ runPCAprojectionBatch <- function(gobject,
             }
         }
 
-        if (verbose) message("start averaging pca results of batches")
+        vmsg(.v = verbose, "start averaging pca results of batches")
 
         # calculate average eigenvalues, coordinates and loadings
         # TODO: test out DT approach, might be faster and more efficient for
@@ -1145,7 +1175,9 @@ runPCAprojectionBatch <- function(gobject,
 
         # eigenvalues
         eigenvalues_list <- lapply(
-            pca_batch_results, FUN = function(x) x$eigenvalues)
+            pca_batch_results,
+            FUN = function(x) x$eigenvalues
+        )
         eigenvalues_matrix <- do.call("cbind", eigenvalues_list)
         eigenvalues_mean <- rowMeans_flex(eigenvalues_matrix)
 
@@ -1153,9 +1185,10 @@ runPCAprojectionBatch <- function(gobject,
         coords_list <- lapply(pca_batch_results, FUN = function(x) x$coords)
         coords_vector <- do.call("c", coords_list)
         coords_array <- array(
-            data = coords_vector, 
-            dim = c(ncol(expr_values), ncp, length(pca_batch_results)))
-        coords_all <- apply(coords_array, MARGIN = c(1:2), function(arr) {
+            data = coords_vector,
+            dim = c(ncol(expr_values), ncp, length(pca_batch_results))
+        )
+        coords_all <- apply(coords_array, MARGIN = seq_len(2), function(arr) {
             mean(arr, na.rm = TRUE)
         })
         rownames(coords_all) <- rownames(pca_batch_results[[1]][["coords"]])
@@ -1165,22 +1198,27 @@ runPCAprojectionBatch <- function(gobject,
         loadings_list <- lapply(pca_batch_results, FUN = function(x) x$loadings)
         loadings_vector <- do.call("c", loadings_list)
         loadings_array <- array(
-            data = loadings_vector, 
-            dim = c(nrow(expr_values), ncp, length(pca_batch_results)))
-        loadings_all <- apply(loadings_array, MARGIN = c(1:2), function(arr) {
-            mean(arr, na.rm = TRUE)
-        })
+            data = loadings_vector,
+            dim = c(nrow(expr_values), ncp, length(pca_batch_results))
+        )
+        loadings_all <- apply(
+            loadings_array,
+            MARGIN = seq_len(2), function(arr) {
+                mean(arr, na.rm = TRUE)
+            }
+        )
         rownames(loadings_all) <- rownames(pca_batch_results[[1]][["loadings"]])
         colnames(loadings_all) <- colnames(pca_batch_results[[1]][["loadings"]])
 
 
         pca_object <- list(
-            eigenvalues = eigenvalues_mean, 
-            loadings = loadings_all, coords = coords_all)
+            eigenvalues = eigenvalues_mean,
+            loadings = loadings_all, coords = coords_all
+        )
     } else {
         pca_batch_results <- list()
 
-        for (batch in 1:batch_number) {
+        for (batch in seq_len(batch_number)) {
             if (verbose) wrap_msg("start batch ", batch)
 
             if (isTRUE(set_seed)) {
@@ -1206,15 +1244,16 @@ runPCAprojectionBatch <- function(gobject,
             )
 
 
-            # adjust the sign of the coordinates and loadings vector relative 
+            # adjust the sign of the coordinates and loadings vector relative
             # to first batch this is necessary for the next averaging step
             if (batch == 1) {
                 pca_batch_results[[batch]] <- pca_object
             } else {
-                for (dimension in 1:ncol(pca_object[["coords"]])) {
+                for (dimension in seq_len(ncol(pca_object[["coords"]]))) {
                     sum_evaluation <- sum(sign(pca_batch_results[[1]][[
-                        "coords"]][1:20, dimension]) *
-                        sign(pca_object[["coords"]][1:20, dimension]))
+                        "coords"
+                    ]][seq_len(20), dimension]) *
+                        sign(pca_object[["coords"]][seq_len(20), dimension]))
                     if (sum_evaluation < 0) {
                         pca_object$coords[, dimension] <- -1 * pca_object$coords[, dimension]
                         pca_object$loadings[, dimension] <- -1 * pca_object$loadings[, dimension]
@@ -1224,7 +1263,7 @@ runPCAprojectionBatch <- function(gobject,
             }
         }
 
-        if (verbose) wrap_msg("start averaging pca results of batches")
+        vmsg(.v = verbose, "start averaging pca results of batches")
 
         # calculate average eigenvalues, coordinates and loadings
         # TODO: test out DT approach, might be faster and more efficient for
@@ -1232,7 +1271,9 @@ runPCAprojectionBatch <- function(gobject,
 
         # eigenvalues
         eigenvalues_list <- lapply(
-            pca_batch_results, FUN = function(x) x$eigenvalues)
+            pca_batch_results,
+            FUN = function(x) x$eigenvalues
+        )
         eigenvalues_matrix <- do.call("cbind", eigenvalues_list)
         eigenvalues_mean <- rowMeans_flex(eigenvalues_matrix)
 
@@ -1240,9 +1281,10 @@ runPCAprojectionBatch <- function(gobject,
         coords_list <- lapply(pca_batch_results, FUN = function(x) x$coords)
         coords_vector <- do.call("c", coords_list)
         coords_array <- array(
-            data = coords_vector, 
-            dim = c(ncol(expr_values), ncp, length(pca_batch_results)))
-        coords_all <- apply(coords_array, MARGIN = c(1:2), function(arr) {
+            data = coords_vector,
+            dim = c(ncol(expr_values), ncp, length(pca_batch_results))
+        )
+        coords_all <- apply(coords_array, MARGIN = seq_len(2), function(arr) {
             mean(arr, na.rm = TRUE)
         })
         rownames(coords_all) <- rownames(pca_batch_results[[1]][["coords"]])
@@ -1252,18 +1294,23 @@ runPCAprojectionBatch <- function(gobject,
         loadings_list <- lapply(pca_batch_results, FUN = function(x) x$loadings)
         loadings_vector <- do.call("c", loadings_list)
         loadings_array <- array(
-            data = loadings_vector, 
-            dim = c(nrow(expr_values), ncp, length(pca_batch_results)))
-        loadings_all <- apply(loadings_array, MARGIN = c(1:2), function(arr) {
-            mean(arr, na.rm = TRUE)
-        })
+            data = loadings_vector,
+            dim = c(nrow(expr_values), ncp, length(pca_batch_results))
+        )
+        loadings_all <- apply(
+            loadings_array,
+            MARGIN = seq_len(2), function(arr) {
+                mean(arr, na.rm = TRUE)
+            }
+        )
         rownames(loadings_all) <- rownames(pca_batch_results[[1]][["loadings"]])
         colnames(loadings_all) <- colnames(pca_batch_results[[1]][["loadings"]])
 
 
         pca_object <- list(
-            eigenvalues = eigenvalues_mean, 
-            loadings = loadings_all, coords = coords_all)
+            eigenvalues = eigenvalues_mean,
+            loadings = loadings_all, coords = coords_all
+        )
     }
 
 
@@ -1324,7 +1371,7 @@ runPCAprojectionBatch <- function(gobject,
 
 #' @title screePlot
 #' @name screePlot
-#' @description identify significant principal components (PCs) using an 
+#' @description identify significant principal components (PCs) using an
 #' screeplot (a.k.a. elbowplot)
 #' @inheritParams data_access_params
 #' @inheritParams plot_output_params
@@ -1342,35 +1389,36 @@ runPCAprojectionBatch <- function(gobject,
 #' @returns ggplot object for scree method
 #' @details
 #'  Screeplot works by plotting the explained variance of each
-#'  individual PC in a barplot allowing you to identify which PC provides a 
+#'  individual PC in a barplot allowing you to identify which PC provides a
 #'  significant contribution (a.k.a 'elbow method'). \cr
-#'  Screeplot will use an available pca object, based on the parameter 'name', 
+#'  Screeplot will use an available pca object, based on the parameter 'name',
 #'  or it will create it if it's not available (see \code{\link{runPCA}})
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' screePlot(g)
 #' @export
-screePlot <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    name = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    reduction = c("cells", "feats"),
-    method = c("irlba", "exact", "random", "factominer"),
-    rev = FALSE,
-    feats_to_use = NULL,
-    center = FALSE,
-    scale_unit = FALSE,
-    ncp = 100,
-    ylim = c(0, 20),
-    verbose = TRUE,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "screePlot",
-    ...) {
+screePlot <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        name = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        reduction = c("cells", "feats"),
+        method = c("irlba", "exact", "random", "factominer"),
+        rev = FALSE,
+        feats_to_use = NULL,
+        center = FALSE,
+        scale_unit = FALSE,
+        ncp = 100,
+        ylim = c(0, 20),
+        verbose = TRUE,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "screePlot",
+        ...) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -1405,22 +1453,30 @@ screePlot <- function(gobject,
 
     # if pca already exists plot
     if (!is.null(pca_obj)) {
-        if (isTRUE(verbose)) 
-            wrap_msg("PCA with name: ", name, 
-                    " already exists and will be used for the screeplot")
+        if (isTRUE(verbose)) {
+            wrap_msg(
+                "PCA with name: ", name,
+                " already exists and will be used for the screeplot"
+            )
+        }
 
         screeplot <- create_screeplot(
-            eigs = slot(pca_obj, "misc")$eigenvalues, ncp = ncp, ylim = ylim)
+            eigs = slot(pca_obj, "misc")$eigenvalues, ncp = ncp, ylim = ylim
+        )
     } else {
         # if pca doesn't exists, then create pca and then plot
-        if (isTRUE(verbose)) 
-            wrap_msg("PCA with name: ", name, 
-                    " does NOT exist, PCA will be done first")
+        if (isTRUE(verbose)) {
+            wrap_msg(
+                "PCA with name: ", name,
+                " does NOT exist, PCA will be done first"
+            )
+        }
 
         # expression values to be used
         values <- match.arg(
-            expression_values, 
-            unique(c("normalized", "scaled", "custom", expression_values)))
+            expression_values,
+            unique(c("normalized", "scaled", "custom", expression_values))
+        )
         expr_values <- getExpression(
             gobject = gobject,
             spat_unit = spat_unit,
@@ -1464,10 +1520,11 @@ screePlot <- function(gobject,
                 )
             } else if (method == "factominer") {
                 pca_object <- .run_pca_factominer(
-                    x = t_flex(expr_values), 
-                    scale = scale_unit, ncp = ncp, rev = rev, ...)
+                    x = t_flex(expr_values),
+                    scale = scale_unit, ncp = ncp, rev = rev, ...
+                )
             } else {
-                stop("only PCA methods from the irlba and factominer package 
+                stop("only PCA methods from the irlba and factominer package
                     have been implemented")
             }
 
@@ -1487,8 +1544,9 @@ screePlot <- function(gobject,
             )
 
             screeplot <- create_screeplot(
-                eigs = slot(dimObject, "misc")$eigenvalues, 
-                ncp = ncp, ylim = ylim)
+                eigs = slot(dimObject, "misc")$eigenvalues,
+                ncp = ncp, ylim = ylim
+            )
         }
     }
 
@@ -1548,20 +1606,24 @@ create_screeplot <- function(eigs, ncp = 20, ylim = c(0, 20)) {
     pl <- ggplot2::ggplot()
     pl <- pl + ggplot2::theme_bw()
     pl <- pl + ggplot2::geom_bar(
-        data = screeDT[1:ncp], 
-        ggplot2::aes(x = PC, y = var_expl), stat = "identity")
+        data = screeDT[seq_len(ncp)],
+        ggplot2::aes(x = PC, y = var_expl), stat = "identity"
+    )
     pl <- pl + ggplot2::coord_cartesian(ylim = ylim)
     pl <- pl + ggplot2::theme(
-        axis.text.x = ggplot2::element_text(angle = 45, hjust = 1, vjust = 1))
+        axis.text.x = ggplot2::element_text(angle = 45, hjust = 1, vjust = 1)
+    )
     pl <- pl + ggplot2::labs(x = "", y = "% of variance explained per PC")
 
     cpl <- ggplot2::ggplot()
     cpl <- cpl + ggplot2::theme_bw()
     cpl <- cpl + ggplot2::geom_bar(
-        data = screeDT[1:ncp], 
-        ggplot2::aes(x = PC, y = var_expl_cum), stat = "identity")
+        data = screeDT[seq_len(ncp)],
+        ggplot2::aes(x = PC, y = var_expl_cum), stat = "identity"
+    )
     cpl <- cpl + ggplot2::theme(axis.text.x = ggplot2::element_text(
-        angle = 45, hjust = 1, vjust = 1))
+        angle = 45, hjust = 1, vjust = 1
+    ))
     cpl <- cpl + ggplot2::labs(x = "", y = "cumulative % of variance explained")
 
     savelist <- list(pl, cpl)
@@ -1602,32 +1664,33 @@ create_screeplot <- function(eigs, ncp = 20, ylim = c(0, 20)) {
 #' @param verbose show progress of jackstraw method
 #' @returns ggplot object for jackstraw method
 #' @details
-#'  The Jackstraw method uses the \code{\link[jackstraw]{permutationPA}} 
-#'  function. By systematically permuting genes it identifies robust, and thus 
+#'  The Jackstraw method uses the \code{\link[jackstraw]{permutationPA}}
+#'  function. By systematically permuting genes it identifies robust, and thus
 #'  significant, PCs.
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' jackstrawPlot(gobject = g)
 #' @export
-jackstrawPlot <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    reduction = c("cells", "feats"),
-    feats_to_use = NULL,
-    center = FALSE,
-    scale_unit = FALSE,
-    ncp = 20,
-    ylim = c(0, 1),
-    iter = 10,
-    threshold = 0.01,
-    verbose = TRUE,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "jackstrawPlot") {
+jackstrawPlot <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        reduction = c("cells", "feats"),
+        feats_to_use = NULL,
+        center = FALSE,
+        scale_unit = FALSE,
+        ncp = 20,
+        ylim = c(0, 1),
+        iter = 10,
+        threshold = 0.01,
+        verbose = TRUE,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "jackstrawPlot") {
     package_check(pkg_name = "jackstraw", repository = "CRAN")
 
     # Set feat_type and spat_unit
@@ -1642,20 +1705,22 @@ jackstrawPlot <- function(gobject,
     )
 
     # print message with information #
-    if (verbose) 
-        message("using 'jackstraw' to identify significant PCs If used in 
-        published research, please cite: 
+    if (verbose) {
+        message("using 'jackstraw' to identify significant PCs If used in
+        published research, please cite:
         Neo Christopher Chung and John D. Storey (2014).
-        'Statistical significance of variables driving systematic variation in 
+        'Statistical significance of variables driving systematic variation in
         high-dimensional data. Bioinformatics")
+    }
 
     # select direction of reduction
     reduction <- match.arg(reduction, c("cells", "feats"))
 
     # expression values to be used
     values <- match.arg(
-        expression_values, 
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        expression_values,
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_values <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -1681,22 +1746,29 @@ jackstrawPlot <- function(gobject,
     if (reduction == "cells") {
         if (scale_unit == TRUE | center == TRUE) {
             expr_values <- t_flex(scale(
-                t_flex(expr_values), center = center, scale = scale_unit))
+                t_flex(expr_values),
+                center = center, scale = scale_unit
+            ))
         }
 
         jtest <- jackstraw::permutationPA(
-            dat = as.matrix(expr_values), 
-            B = iter, threshold = threshold, verbose = verbose)
+            dat = as.matrix(expr_values),
+            B = iter, threshold = threshold, verbose = verbose
+        )
 
         ## results ##
         nr_sign_components <- jtest$r
-        if (verbose) 
-            cat("number of estimated significant components: ", 
-                nr_sign_components)
+        if (verbose) {
+            cat(
+                "number of estimated significant components: ",
+                nr_sign_components
+            )
+        }
         final_results <- jtest$p
         jackplot <- create_jackstrawplot(
-            jackstraw_data = final_results, 
-            ncp = ncp, ylim = ylim, threshold = threshold)
+            jackstraw_data = final_results,
+            ncp = ncp, ylim = ylim, threshold = threshold
+        )
     }
 
     return(plot_output_handler(
@@ -1723,10 +1795,11 @@ jackstrawPlot <- function(gobject,
 #' @keywords internal
 #' @returns ggplot
 #' @export
-create_jackstrawplot <- function(jackstraw_data,
-    ncp = 20,
-    ylim = c(0, 1),
-    threshold = 0.01) {
+create_jackstrawplot <- function(
+        jackstraw_data,
+        ncp = 20,
+        ylim = c(0, 1),
+        threshold = 0.01) {
     checkmate::assert_numeric(ncp, len = 1L)
     checkmate::assert_numeric(ylim, len = 2L)
     checkmate::assert_numeric(threshold, len = 1L)
@@ -1744,12 +1817,15 @@ create_jackstrawplot <- function(jackstraw_data,
     pl <- ggplot2::ggplot()
     pl <- pl + ggplot2::theme_bw()
     pl <- pl + ggplot2::geom_point(
-        data = testDT[1:ncp], 
-        ggplot2::aes(x = PC, y = p.val, fill = sign), shape = 21)
+        data = testDT[seq_len(ncp)],
+        ggplot2::aes(x = PC, y = p.val, fill = sign), shape = 21
+    )
     pl <- pl + ggplot2::scale_fill_manual(
-        values = c("n.s." = "lightgrey", "sign" = "darkorange"))
+        values = c("n.s." = "lightgrey", "sign" = "darkorange")
+    )
     pl <- pl + ggplot2::theme(
-        axis.text.x = ggplot2::element_text(angle = 45, hjust = 1, vjust = 1))
+        axis.text.x = ggplot2::element_text(angle = 45, hjust = 1, vjust = 1)
+    )
     pl <- pl + ggplot2::coord_cartesian(ylim = ylim)
     pl <- pl + ggplot2::theme(panel.grid.major.x = ggplot2::element_blank())
     pl <- pl + ggplot2::labs(x = "", y = "p-value per PC")
@@ -1785,44 +1861,45 @@ create_jackstrawplot <- function(jackstraw_data,
 #' @param jack_ylim y-axis limits on jackstraw plot
 #' @param verbose be verbose
 #' @returns ggplot object for scree method and maxtrix of p-values for jackstraw
-#' @details Two different methods can be used to assess the number of relevant 
+#' @details Two different methods can be used to assess the number of relevant
 #' or significant prinicipal components (PC's). \cr
 #'  1. Screeplot works by plotting the explained variance of each
-#'  individual PC in a barplot allowing you to identify which PC provides a 
+#'  individual PC in a barplot allowing you to identify which PC provides a
 #'  significant
 #'  contribution  (a.k.a. 'elbow method'). \cr
-#'  2. The Jackstraw method uses the \code{\link[jackstraw]{permutationPA}} 
-#'  function. By systematically permuting genes it identifies robust, and thus 
+#'  2. The Jackstraw method uses the \code{\link[jackstraw]{permutationPA}}
+#'  function. By systematically permuting genes it identifies robust, and thus
 #'  significant, PCs.
 #'  \cr
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' signPCA(g)
 #' @export
-signPCA <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    name = NULL,
-    method = c("screeplot", "jackstraw"),
-    expression_values = c("normalized", "scaled", "custom"),
-    reduction = c("cells", "feats"),
-    pca_method = c("irlba", "factominer"),
-    rev = FALSE,
-    feats_to_use = NULL,
-    center = TRUE,
-    scale_unit = TRUE,
-    ncp = 50,
-    scree_ylim = c(0, 10),
-    jack_iter = 10,
-    jack_threshold = 0.01,
-    jack_ylim = c(0, 1),
-    verbose = TRUE,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "signPCA") {
+signPCA <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        name = NULL,
+        method = c("screeplot", "jackstraw"),
+        expression_values = c("normalized", "scaled", "custom"),
+        reduction = c("cells", "feats"),
+        pca_method = c("irlba", "factominer"),
+        rev = FALSE,
+        feats_to_use = NULL,
+        center = TRUE,
+        scale_unit = TRUE,
+        ncp = 50,
+        scree_ylim = c(0, 10),
+        jack_iter = 10,
+        jack_threshold = 0.01,
+        jack_ylim = c(0, 1),
+        verbose = TRUE,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "signPCA") {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -1942,7 +2019,7 @@ signPCA <- function(gobject,
                 show_plot = show_plot,
                 default_save_name = default_save_name,
                 save_param = save_param,
-                else_return = jackplot 
+                else_return = jackplot
                 # TODO potentially return all results instead
             ))
         }
@@ -1985,7 +2062,7 @@ signPCA <- function(gobject,
 #' @param seed_number seed number to use
 #' @param verbose verbosity of function
 #' @param toplevel_params parameters to extract
-#' @inheritDotParams uwot::umap -X -n_neighbors -n_components -n_epochs 
+#' @inheritDotParams uwot::umap -X -n_neighbors -n_components -n_epochs
 #' -min_dist -n_threads -spread -seed -scale -pca -pca_center -pca_method
 #' @returns giotto object with updated UMAP dimension reduction
 #' @details See \code{\link[uwot]{umap}} for more information about these and
@@ -2002,31 +2079,32 @@ signPCA <- function(gobject,
 #' }
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' runUMAP(g)
 #' @export
-runUMAP <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    reduction = c("cells", "feats"),
-    dim_reduction_to_use = "pca",
-    dim_reduction_name = NULL,
-    dimensions_to_use = 1:10,
-    name = NULL,
-    feats_to_use = NULL,
-    return_gobject = TRUE,
-    n_neighbors = 40,
-    n_components = 2,
-    n_epochs = 400,
-    min_dist = 0.01,
-    n_threads = NA,
-    spread = 5,
-    set_seed = TRUE,
-    seed_number = 1234L,
-    verbose = TRUE,
-    toplevel_params = 2L,
-    ...) {
+runUMAP <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        reduction = c("cells", "feats"),
+        dim_reduction_to_use = "pca",
+        dim_reduction_name = NULL,
+        dimensions_to_use = 1:10,
+        name = NULL,
+        feats_to_use = NULL,
+        return_gobject = TRUE,
+        n_neighbors = 40,
+        n_components = 2,
+        n_epochs = 400,
+        min_dist = 0.01,
+        n_threads = NA,
+        spread = 5,
+        set_seed = TRUE,
+        seed_number = 1234L,
+        verbose = TRUE,
+        toplevel_params = 2L,
+        ...) {
     # NSE vars
     cell_ID <- NULL
 
@@ -2097,7 +2175,8 @@ runUMAP <- function(gobject,
                     "Ignoring dimensions_to_use that are outside the range."
                 ))
                 dimensions_to_use <- dimensions_to_use[
-                    dimensions_to_use %in% seq(ncol(matrix_to_use))]
+                    dimensions_to_use %in% seq(ncol(matrix_to_use))
+                ]
             }
 
             matrix_to_use <- matrix_to_use[, dimensions_to_use]
@@ -2105,8 +2184,9 @@ runUMAP <- function(gobject,
             ## using original matrix ##
             # expression values to be used
             values <- match.arg(
-                expression_values, 
-                unique(c("normalized", "scaled", "custom", expression_values)))
+                expression_values,
+                unique(c("normalized", "scaled", "custom", expression_values))
+            )
 
             expr_values <- getExpression(
                 gobject = gobject,
@@ -2198,8 +2278,10 @@ runUMAP <- function(gobject,
 
 
             ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
-            gobject <- set_dimReduction(gobject = gobject, 
-                                        dimObject = dimObject)
+            gobject <- set_dimReduction(
+                gobject = gobject,
+                dimObject = dimObject
+            )
             ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
 
 
@@ -2252,7 +2334,7 @@ runUMAP <- function(gobject,
 #' @param toplevel_params parameters to extract
 #' @param ... additional UMAP parameters
 #' @returns giotto object with updated UMAP dimension reduction
-#' @details See \code{\link[uwot]{umap}} for more information about these and 
+#' @details See \code{\link[uwot]{umap}} for more information about these and
 #' other parameters.
 #' \itemize{
 #'   \item Input for UMAP dimension reduction can be another dimension reduction (default = 'pca')
@@ -2263,32 +2345,33 @@ runUMAP <- function(gobject,
 #' }
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' runUMAPprojection(g)
 #' @export
-runUMAPprojection <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    reduction = c("cells", "feats"),
-    dim_reduction_to_use = "pca",
-    dim_reduction_name = NULL,
-    dimensions_to_use = 1:10,
-    random_subset = 500,
-    name = NULL,
-    feats_to_use = NULL,
-    return_gobject = TRUE,
-    n_neighbors = 40,
-    n_components = 2,
-    n_epochs = 400,
-    min_dist = 0.01,
-    n_threads = NA,
-    spread = 5,
-    set_seed = TRUE,
-    seed_number = 1234,
-    verbose = TRUE,
-    toplevel_params = 2,
-    ...) {
+runUMAPprojection <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        reduction = c("cells", "feats"),
+        dim_reduction_to_use = "pca",
+        dim_reduction_name = NULL,
+        dimensions_to_use = 1:10,
+        random_subset = 500,
+        name = NULL,
+        feats_to_use = NULL,
+        return_gobject = TRUE,
+        n_neighbors = 40,
+        n_components = 2,
+        n_epochs = 400,
+        min_dist = 0.01,
+        n_threads = NA,
+        spread = 5,
+        set_seed = TRUE,
+        seed_number = 1234,
+        verbose = TRUE,
+        toplevel_params = 2,
+        ...) {
     # NSE vars
     cell_ID <- NULL
 
@@ -2397,7 +2480,9 @@ runUMAPprojection <- function(gobject,
         cell_ID_order <- rownames(matrix_to_use)
 
         # create random selection
-        random_selection <- sort(sample(1:nrow(matrix_to_use), random_subset))
+        random_selection <- sort(sample(
+            seq_len(nrow(matrix_to_use)), random_subset
+        ))
         subsample_matrix <- matrix_to_use[random_selection, ]
 
         uwot_clus_subset <- uwot::umap(
@@ -2423,7 +2508,8 @@ runUMAPprojection <- function(gobject,
         # combine subset and prediction
         coords_umap <- rbind(uwot_clus_subset$embedding, uwot_clus_pred)
         coords_umap <- coords_umap[
-            match(cell_ID_order, rownames(coords_umap)), ]
+            match(cell_ID_order, rownames(coords_umap)),
+        ]
 
         coords_umap_DT <- data.table::as.data.table(coords_umap)
         coords_umap_DT[, cell_ID := rownames(coords_umap)]
@@ -2514,7 +2600,7 @@ runUMAPprojection <- function(gobject,
 #' @param verbose verbosity of the function
 #' @param ... additional tSNE parameters
 #' @returns giotto object with updated tSNE dimension recuction
-#' @details See \code{\link[Rtsne]{Rtsne}} for more information about these and 
+#' @details See \code{\link[Rtsne]{Rtsne}} for more information about these and
 #' other parameters. \cr
 #' \itemize{
 #'   \item Input for tSNE dimension reduction can be another dimension reduction (default = 'pca')
@@ -2525,28 +2611,29 @@ runUMAPprojection <- function(gobject,
 #' }
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' runtSNE(g)
 #' @export
-runtSNE <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    reduction = c("cells", "feats"),
-    dim_reduction_to_use = "pca",
-    dim_reduction_name = NULL,
-    dimensions_to_use = 1:10,
-    name = NULL,
-    feats_to_use = NULL,
-    return_gobject = TRUE,
-    dims = 2,
-    perplexity = 30,
-    theta = 0.5,
-    do_PCA_first = FALSE,
-    set_seed = TRUE,
-    seed_number = 1234,
-    verbose = TRUE,
-    ...) {
+runtSNE <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        reduction = c("cells", "feats"),
+        dim_reduction_to_use = "pca",
+        dim_reduction_name = NULL,
+        dimensions_to_use = 1:10,
+        name = NULL,
+        feats_to_use = NULL,
+        return_gobject = TRUE,
+        dims = 2,
+        perplexity = 30,
+        theta = 0.5,
+        do_PCA_first = FALSE,
+        set_seed = TRUE,
+        seed_number = 1234,
+        verbose = TRUE,
+        ...) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -2609,8 +2696,9 @@ runtSNE <- function(gobject,
             ## using original matrix ##
             # expression values to be used
             values <- match.arg(
-                expression_values, 
-                unique(c("normalized", "scaled", "custom", expression_values)))
+                expression_values,
+                unique(c("normalized", "scaled", "custom", expression_values))
+            )
             expr_values <- getExpression(
                 gobject = gobject,
                 spat_unit = spat_unit,
@@ -2690,8 +2778,10 @@ runtSNE <- function(gobject,
             )
 
             ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
-            gobject <- set_dimReduction(gobject = gobject, 
-                                        dimObject = dimObject)
+            gobject <- set_dimReduction(
+                gobject = gobject,
+                dimObject = dimObject
+            )
             ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
 
             ## update parameters used ##
@@ -2737,37 +2827,38 @@ runtSNE <- function(gobject,
 #' @param verbose be verbose
 #' @param ... additional \code{\link[harmony]{HarmonyMatrix}} parameters
 #' @returns giotto object with updated Harmony dimension reduction
-#' @details This is a simple wrapper for the HarmonyMatrix function in the 
+#' @details This is a simple wrapper for the HarmonyMatrix function in the
 #' Harmony package \doi{10.1038/s41592-019-0619-0}.
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' runGiottoHarmony(g, vars_use = "leiden_clus")
 #' @export
-runGiottoHarmony <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    vars_use = "list_ID",
-    do_pca = FALSE,
-    expression_values = c("normalized", "scaled", "custom"),
-    reduction = "cells",
-    dim_reduction_to_use = "pca",
-    dim_reduction_name = NULL,
-    dimensions_to_use = 1:10,
-    name = NULL,
-    feats_to_use = NULL,
-    set_seed = TRUE,
-    seed_number = 1234,
-    toplevel_params = 2,
-    return_gobject = TRUE,
-    verbose = NULL,
-    ...) {
+runGiottoHarmony <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        vars_use = "list_ID",
+        do_pca = FALSE,
+        expression_values = c("normalized", "scaled", "custom"),
+        reduction = "cells",
+        dim_reduction_to_use = "pca",
+        dim_reduction_name = NULL,
+        dimensions_to_use = 1:10,
+        name = NULL,
+        feats_to_use = NULL,
+        set_seed = TRUE,
+        seed_number = 1234,
+        toplevel_params = 2,
+        return_gobject = TRUE,
+        verbose = NULL,
+        ...) {
     # verify if optional package is installed
     package_check(pkg_name = "harmony", repository = "CRAN")
 
 
     # print message with information #
-    message("using 'Harmony' to integrate different datasets. If used in 
+    message("using 'Harmony' to integrate different datasets. If used in
             published research, please cite:")
 
     wrap_msg("Korsunsky, I., Millard, N., Fan, J. et al.
@@ -2841,8 +2932,9 @@ runGiottoHarmony <- function(gobject,
         ## using original matrix ##
         # expression values to be used
         values <- match.arg(
-            expression_values, 
-            unique(c("normalized", "scaled", "custom", expression_values)))
+            expression_values,
+            unique(c("normalized", "scaled", "custom", expression_values))
+        )
         expr_values <- getExpression(
             gobject = gobject,
             spat_unit = spat_unit,
@@ -2889,7 +2981,7 @@ runGiottoHarmony <- function(gobject,
     )
 
 
-    colnames(harmony_results) <- paste0("Dim.", 1:ncol(harmony_results))
+    colnames(harmony_results) <- paste0("Dim.", seq_len(ncol(harmony_results)))
     rownames(harmony_results) <- rownames(matrix_to_use)
 
     harmdimObject <- create_dim_obj(
@@ -2905,7 +2997,6 @@ runGiottoHarmony <- function(gobject,
 
     # return giotto object or harmony results
     if (isTRUE(return_gobject)) {
-
         harmony_names <- list_dim_reductions_names(
             gobject = gobject,
             data_type = reduction,
@@ -2915,13 +3006,17 @@ runGiottoHarmony <- function(gobject,
         )
 
         if (name %in% harmony_names) {
-            cat(name, 
-                " has already been used with harmony, will be overwritten")
+            cat(
+                name,
+                " has already been used with harmony, will be overwritten"
+            )
         }
 
         ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
-        gobject <- set_dimReduction(gobject = gobject, 
-                                    dimObject = harmdimObject)
+        gobject <- set_dimReduction(
+            gobject = gobject,
+            dimObject = harmdimObject
+        )
         ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
 
 
diff --git a/R/feature_set_enrichment.R b/R/feature_set_enrichment.R
index c30cb848a..c5bf382ea 100644
--- a/R/feature_set_enrichment.R
+++ b/R/feature_set_enrichment.R
@@ -4,28 +4,28 @@
 #' @param dryrun do a dry run, default TRUE.
 #' @param path_to_GSEA path to GSEA command line executable, e.g. gsea-XXX.jar.
 #' See details (1.) for more information.
-#' @param GSEA_dataset path to a Human/Mouse collection from GSEA, e.g. 
+#' @param GSEA_dataset path to a Human/Mouse collection from GSEA, e.g.
 #' Hallmarks C1. See details (2.) for more information.
-#' @param GSEA_ranked_file path to .rnk file for GSEA. See details (3.) for 
+#' @param GSEA_ranked_file path to .rnk file for GSEA. See details (3.) for
 #' more information
-#' @param output_folder path to which the GSEA results will be saved. Default 
+#' @param output_folder path to which the GSEA results will be saved. Default
 #' is current working directory.
-#' @param name_analysis_folder default output subdirectory prefix to which 
+#' @param name_analysis_folder default output subdirectory prefix to which
 #' results are saved.
-#'                             Will live within output_folder; equivalent of 
+#'                             Will live within output_folder; equivalent of
 #'                             "Analysis Name" in GSEA Application.
-#' @param collapse only 'false' is supported. This will use your dataset as-is, 
+#' @param collapse only 'false' is supported. This will use your dataset as-is,
 #' in the original format.
-#' @param mode option selected in Advanced Field "Collapsing Mode for 
+#' @param mode option selected in Advanced Field "Collapsing Mode for
 #' Probe Sets => 1 gene"
 #' @param norm normalization mode; only meandiv is supported.
 #' @param nperm number of permutations, default 1000
-#' @param scoring_scheme Default "weighted", equivalent of 
+#' @param scoring_scheme Default "weighted", equivalent of
 #' "enrichment statistic" in GSEA Application
 #' @param plot_top_x Default 20, number of enrichment plots to produce.
-#' @param set_max default 500, equivalent to "max size; exclude larger sets" 
+#' @param set_max default 500, equivalent to "max size; exclude larger sets"
 #' in Basic Fields in GSEA Application
-#' @param set_min default 15, equivalent to "min size; exclude smaller sets" 
+#' @param set_min default 15, equivalent to "min size; exclude smaller sets"
 #' in Basic Fields in GSEA Application
 #' @returns data.table
 #' @details
@@ -33,11 +33,11 @@
 #' 1. download and install the COMMAND line (all platforms) gsea-XXX.jar
 #' https://www.gsea-msigdb.org/gsea/downloads.jsp
 #' 1.1. download zip file
-#' 1.2. unzip and move to known location 
+#' 1.2. unzip and move to known location
 #' (e.g. in path/to/your/applications/gsea/GSEA_4.3.2)
 #'
 #' 2. download the Human and Mouse collections
-#' https://www.gsea-msigdb.org/gsea/msigdb/index.jsp or zipped folder 
+#' https://www.gsea-msigdb.org/gsea/msigdb/index.jsp or zipped folder
 #' https://www.gsea-msigdb.org/gsea/downloads.jsp (all downloaded)
 #'
 #' 3. create ranked gene lists
@@ -50,37 +50,40 @@
 #' please reference GSEA's documentation here:
 #' https://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideTEXT.htm#_Syntax
 #' @export
-doFeatureSetEnrichment <- function(dryrun = TRUE,
-    path_to_GSEA = NULL,
-    GSEA_dataset = NULL,
-    GSEA_ranked_file = NULL,
-    output_folder = NULL,
-    name_analysis_folder = "my_GSEA_analysis",
-    collapse = "false",
-    mode = c(
-        "Abs_max_of_probes",
-        "Max_probe",
-        "Median_of_probes",
-        "Mean_of_probes",
-        "Sum_of_probes"
-    ),
-    norm = "meandiv",
-    nperm = 1000,
-    scoring_scheme = "weighted",
-    plot_top_x = 20,
-    set_max = 500,
-    set_min = 15) {
+doFeatureSetEnrichment <- function(
+        dryrun = TRUE,
+        path_to_GSEA = NULL,
+        GSEA_dataset = NULL,
+        GSEA_ranked_file = NULL,
+        output_folder = NULL,
+        name_analysis_folder = "my_GSEA_analysis",
+        collapse = "false",
+        mode = c(
+            "Abs_max_of_probes",
+            "Max_probe",
+            "Median_of_probes",
+            "Mean_of_probes",
+            "Sum_of_probes"
+        ),
+        norm = "meandiv",
+        nperm = 1000,
+        scoring_scheme = "weighted",
+        plot_top_x = 20,
+        set_max = 500,
+        set_min = 15) {
     # set don't run to false as a start
     dont_run <- FALSE
 
     # SYSTEM CHECK FOR JAVA
     java_not_installed <- as.logical(system("java -version"))
-    # returns 0 if java is installed (i.e., command runs successfully), 
+    # returns 0 if java is installed (i.e., command runs successfully),
     # 1 otherwise
-    if (java_not_installed) 
-        stop(wrap_txt("Java must be installed for doFeatureSetEnrichment() to 
+    if (java_not_installed) {
+        stop(wrap_txt("Java must be installed for doFeatureSetEnrichment() to
                 run. Please install Java: https://www.java.com/en/download/",
-                errWidth = TRUE))
+            errWidth = TRUE
+        ))
+    }
 
 
     mode <- match.arg(mode, choices = c(
@@ -91,26 +94,33 @@ doFeatureSetEnrichment <- function(dryrun = TRUE,
         "Sum_of_probes"
     ))
 
-    if (is.null(output_folder)) output_folder <- paste0(
-        getwd(), "/Feature_set_enrichment_results/")
+    if (is.null(output_folder)) {
+        output_folder <- paste0(
+            getwd(), "/Feature_set_enrichment_results/"
+        )
+    }
 
     if (!dir.exists(output_folder)) {
-        wrap_msg(paste0("Directory does not yet exist. Creating directory at:", 
-                        output_folder))
+        wrap_msg(paste0(
+            "Directory does not yet exist. Creating directory at:",
+            output_folder
+        ))
         dir.create(output_folder)
     }
 
     # check for path to GSEA tool
-    if (is.null(path_to_GSEA)) 
+    if (is.null(path_to_GSEA)) {
         stop("Path to the GSEA directory needs to be provided")
-    if (!file.exists(path_to_GSEA)) 
+    }
+    if (!file.exists(path_to_GSEA)) {
         stop("Path to the GSEA directory does not exist")
+    }
 
     path_to_GSEA <- paste0('"', path_to_GSEA, '"')
 
     # check for path to GSEA dataset .gmt
     if (is.null(GSEA_dataset)) {
-        warning("Path to a GSEA dataset needs to be provided, only dryrun will 
+        warning("Path to a GSEA dataset needs to be provided, only dryrun will
                 work for testing")
         dont_run <- TRUE
         GSEA_dataset <- "test.gmt"
@@ -120,14 +130,15 @@ doFeatureSetEnrichment <- function(dryrun = TRUE,
 
     # check for GSRA ranked file (path or data.frame)
     if (is.null(GSEA_ranked_file)) {
-        warning("A ranked gene file needs to be provided, only dryrun will work 
+        warning("A ranked gene file needs to be provided, only dryrun will work
                 for testing")
         dont_run <- TRUE
         GSEA_ranked_file <- "my_ranked_file.rnk"
     } else if (inherits(GSEA_ranked_file, "character")) {
         message("The ranked list looks like a path to a file")
-        if (!file.exists(GSEA_ranked_file)) 
+        if (!file.exists(GSEA_ranked_file)) {
             stop("Path to the ranked file does not exist")
+        }
     } else if (inherits(GSEA_ranked_file, "data.frame")) {
         message("The ranked list looks like a data.frame")
 
@@ -155,10 +166,12 @@ doFeatureSetEnrichment <- function(dryrun = TRUE,
 
     if (my_os == "windows") {
         execution_path <- paste0(
-            path_to_GSEA, "/", "gsea-cli.bat", " ", operation)
+            path_to_GSEA, "/", "gsea-cli.bat", " ", operation
+        )
     } else {
         execution_path <- paste0(
-            path_to_GSEA, "/", "gsea-cli.sh", " ", operation)
+            path_to_GSEA, "/", "gsea-cli.sh", " ", operation
+        )
     }
 
     created_command <- sprintf(
diff --git a/R/general_help.R b/R/general_help.R
index 279a963e4..67e6e7c13 100644
--- a/R/general_help.R
+++ b/R/general_help.R
@@ -7,8 +7,9 @@
 #' @description calculate gini coefficient
 #' @keywords internal
 #' @returns gini coefficient
-mygini_fun <- function(x,
-    weights = rep(1, length(x))) {
+mygini_fun <- function(
+        x,
+        weights = rep(1, length(x))) {
     # adapted from R package GiniWegNeg
     dataset <- cbind(x, weights)
     ord_x <- order(x)
@@ -36,9 +37,10 @@ mygini_fun <- function(x,
 #' @description calculate gini coefficient on a minimum length vector
 #' @keywords internal
 #' @returns gini coefficient
-extended_gini_fun <- function(x,
-    weights = rep(1, length = length(x)),
-    minimum_length = 16) {
+extended_gini_fun <- function(
+        x,
+        weights = rep(1, length = length(x)),
+        minimum_length = 16) {
     if (length(x) < minimum_length) {
         difference <- minimum_length - length(x)
         min_value <- min(x)
@@ -57,16 +59,19 @@ extended_gini_fun <- function(x,
 #' @description create binarized scores from a vector using kmeans
 #' @returns numeric
 #' @keywords internal
-.kmeans_binarize <- function(x,
-    nstart = 3,
-    iter.max = 10,
-    seed = NULL) {
+.kmeans_binarize <- function(
+        x,
+        nstart = 3,
+        iter.max = 10,
+        seed = NULL) {
     if (!is.null(seed)) {
         on.exit(random_seed(), add = TRUE)
         set.seed(seed)
     }
     sel_gene_km <- stats::kmeans(
-        x, centers = 2, nstart = nstart, iter.max = iter.max)$cluster
+        x,
+        centers = 2, nstart = nstart, iter.max = iter.max
+    )$cluster
     mean_1 <- mean(x[sel_gene_km == 1])
     mean_2 <- mean(x[sel_gene_km == 2])
 
@@ -125,22 +130,25 @@ extended_gini_fun <- function(x,
 
 #' @title .kmeans_arma_subset_binarize
 #' @name .kmeans_arma_subset_binarize
-#' @description create binarized scores from a subsetted vector using 
+#' @description create binarized scores from a subsetted vector using
 #' kmeans_arma
 #' @returns numeric
 #' @keywords internal
-.kmeans_arma_subset_binarize <- function(x,
-    n_iter = 5,
-    extreme_nr = 20,
-    sample_nr = 200,
-    seed = NULL) {
+.kmeans_arma_subset_binarize <- function(
+        x,
+        n_iter = 5,
+        extreme_nr = 20,
+        sample_nr = 200,
+        seed = NULL) {
     length_x <- length(x)
 
     vector_x <- sort(x)
-    first_set <- vector_x[1:extreme_nr]
+    first_set <- vector_x[seq_len(extreme_nr)]
     last_set <- vector_x[(length_x - (extreme_nr - 1)):length_x]
     random_set <- sample(
-        vector_x[(extreme_nr + 1):(length_x - extreme_nr)], size = sample_nr)
+        vector_x[(extreme_nr + 1):(length_x - extreme_nr)],
+        size = sample_nr
+    )
     testset <- c(first_set, last_set, random_set)
 
     if (!is.null(seed)) {
@@ -182,15 +190,14 @@ extended_gini_fun <- function(x,
 #' @description wrapper for different binarization functions
 #' @returns matrix
 #' @keywords internal
-kmeans_binarize_wrapper <- function(
-        expr_values,
-        subset_feats = NULL,
-        kmeans_algo = c("kmeans", "kmeans_arma", "kmeans_arma_subset"),
-        nstart = 3,
-        iter_max = 10,
-        extreme_nr = 50,
-        sample_nr = 50,
-        seed = NULL) {
+kmeans_binarize_wrapper <- function(expr_values,
+    subset_feats = NULL,
+    kmeans_algo = c("kmeans", "kmeans_arma", "kmeans_arma_subset"),
+    nstart = 3,
+    iter_max = 10,
+    extreme_nr = 50,
+    sample_nr = 50,
+    seed = NULL) {
     # expression values
     if (!is.null(subset_feats)) {
         expr_values <- expr_values[rownames(expr_values) %in% subset_feats, ]
@@ -247,9 +254,10 @@ kmeans_binarize_wrapper <- function(
 #' @description wrapper for rank binarization function
 #' @returns matrix
 #' @keywords internal
-rank_binarize_wrapper <- function(expr_values,
-    subset_feats = NULL,
-    percentage_rank = 30) {
+rank_binarize_wrapper <- function(
+        expr_values,
+        subset_feats = NULL,
+        percentage_rank = 30) {
     # expression values
     if (!is.null(subset_feats)) {
         expr_values <- expr_values[rownames(expr_values) %in% subset_feats, ]
@@ -257,7 +265,8 @@ rank_binarize_wrapper <- function(expr_values,
 
     max_rank <- (ncol(expr_values) / 100) * percentage_rank
     bin_matrix <- t_flex(apply(
-        X = expr_values, MARGIN = 1, FUN = .rank_binarize, max_rank = max_rank))
+        X = expr_values, MARGIN = 1, FUN = .rank_binarize, max_rank = max_rank
+    ))
 
     return(bin_matrix)
 }
@@ -270,15 +279,16 @@ rank_binarize_wrapper <- function(expr_values,
 
 #' @title convertEnsemblToGeneSymbol
 #' @name convertEnsemblToGeneSymbol
-#' @description This function convert ensembl gene IDs from a matrix to 
+#' @description This function convert ensembl gene IDs from a matrix to
 #' official gene symbols
 #' @param matrix an expression matrix with ensembl gene IDs as rownames
 #' @param species species to use for gene symbol conversion
 #' @returns expression matrix with gene symbols as rownames
 #' @details This function requires that the biomaRt library is installed
 #' @export
-convertEnsemblToGeneSymbol <- function(matrix,
-    species = c("mouse", "human")) {
+convertEnsemblToGeneSymbol <- function(
+        matrix,
+        species = c("mouse", "human")) {
     # data.table: set global variable
     dupes <- mgi_symbol <- gene_symbol <- ensembl_gene_id <- hgnc_symbol <- NULL
 
@@ -306,10 +316,14 @@ convertEnsemblToGeneSymbol <- function(matrix,
             ifelse(mgi_symbol == "", ensembl_gene_id, "temporary")
         ), by = mgi_symbol]
         gene_names_DT[, gene_symbol := ifelse(
-            mgi_symbol == "", ensembl_gene_id, gene_symbol)]
+            mgi_symbol == "", ensembl_gene_id, gene_symbol
+        )]
         gene_names_DT[, gene_symbol := ifelse(
-            gene_symbol == "temporary", 
-            paste0(mgi_symbol, "--", 1:.N), gene_symbol), by = mgi_symbol]
+            gene_symbol == "temporary",
+            paste0(mgi_symbol, "--", seq_len(.N)), gene_symbol
+        ),
+        by = mgi_symbol
+        ]
 
         # filter
         matrix <- matrix[rownames(matrix) %in% gene_names_DT$ensembl_gene_id, ]
@@ -345,10 +359,14 @@ convertEnsemblToGeneSymbol <- function(matrix,
             ifelse(hgnc_symbol == "", ensembl_gene_id, "temporary")
         ), by = hgnc_symbol]
         gene_names_DT[, gene_symbol := ifelse(
-            hgnc_symbol == "", ensembl_gene_id, gene_symbol)]
+            hgnc_symbol == "", ensembl_gene_id, gene_symbol
+        )]
         gene_names_DT[, gene_symbol := ifelse(
-            gene_symbol == "temporary", 
-            paste0(hgnc_symbol, "--", 1:.N), gene_symbol), by = hgnc_symbol]
+            gene_symbol == "temporary",
+            paste0(hgnc_symbol, "--", seq_len(.N)), gene_symbol
+        ),
+        by = hgnc_symbol
+        ]
 
         # filter
         matrix <- matrix[rownames(matrix) %in% gene_names_DT$ensembl_gene_id, ]
@@ -383,17 +401,16 @@ convertEnsemblToGeneSymbol <- function(matrix,
 #' @name gpoly_from_dfr_smoothed_wrapped
 #' @returns giottoPolygon
 #' @keywords internal
-gpoly_from_dfr_smoothed_wrapped <- function(
-        segmdfr,
-        name = "cell",
-        calc_centroids = FALSE,
-        smooth_polygons = FALSE,
-        vertices = 20L,
-        k = 3L,
-        set_neg_to_zero = TRUE,
-        skip_eval_dfr = FALSE,
-        copy_dt = TRUE,
-        verbose = TRUE) {
+gpoly_from_dfr_smoothed_wrapped <- function(segmdfr,
+    name = "cell",
+    calc_centroids = FALSE,
+    smooth_polygons = FALSE,
+    vertices = 20L,
+    k = 3L,
+    set_neg_to_zero = TRUE,
+    skip_eval_dfr = FALSE,
+    copy_dt = TRUE,
+    verbose = TRUE) {
     gpoly <- createGiottoPolygonsFromDfr(
         segmdfr = segmdfr,
         name = name,
@@ -410,13 +427,18 @@ gpoly_from_dfr_smoothed_wrapped <- function(
             set_neg_to_zero = set_neg_to_zero
         )
     }
-    if (isTRUE(calc_centroids)) gpoly <- centroids(
-        gpoly, append_gpolygon = TRUE)
+    if (isTRUE(calc_centroids)) {
+        gpoly <- centroids(
+            gpoly,
+            append_gpolygon = TRUE
+        )
+    }
 
     slot(gpoly, "spatVector") <- terra::wrap(slot(gpoly, "spatVector"))
     if (isTRUE(calc_centroids)) {
         slot(gpoly, "spatVectorCentroids") <- terra::wrap(
-            slot(gpoly, "spatVectorCentroids"))
+            slot(gpoly, "spatVectorCentroids")
+        )
     }
     return(gpoly)
 }
@@ -427,33 +449,34 @@ gpoly_from_dfr_smoothed_wrapped <- function(
 
 #' @title get10Xmatrix
 #' @name get10Xmatrix
-#' @description This function creates an expression matrix from a 10X 
+#' @description This function creates an expression matrix from a 10X
 #' structured folder
 #' @param path_to_data path to the 10X folder
-#' @param gene_column_index which column from the features or genes .tsv file 
+#' @param gene_column_index which column from the features or genes .tsv file
 #' to use for row ids
 #' @param remove_zero_rows removes rows with sum equal to zero
-#' @param split_by_type split into multiple matrices based on 3rd column of 
+#' @param split_by_type split into multiple matrices based on 3rd column of
 #' features.tsv(.gz)
 #' @returns sparse expression matrix from 10X
-#' @details A typical 10X folder is named raw_feature_bc_matrix or 
+#' @details A typical 10X folder is named raw_feature_bc_matrix or
 #' filtered_feature_bc_matrix and it has 3 files:
 #' \itemize{
 #'   \item{barcodes.tsv(.gz)}
 #'   \item{features.tsv(.gz) or genes.tsv(.gz)}
 #'   \item{matrix.mtx(.gz)}
 #' }
-#' By default the first column of the features or genes .tsv file will be used, 
+#' By default the first column of the features or genes .tsv file will be used,
 #' however if multiple
-#' annotations are provided (e.g. ensembl gene ids and gene symbols) the user 
+#' annotations are provided (e.g. ensembl gene ids and gene symbols) the user
 #' can select another column.
 #' @export
-get10Xmatrix <- function(path_to_data,
-    gene_column_index = 1,
-    remove_zero_rows = TRUE,
-    split_by_type = TRUE) {
+get10Xmatrix <- function(
+        path_to_data,
+        gene_column_index = 1,
+        remove_zero_rows = TRUE,
+        split_by_type = TRUE) {
     # data.table variables
-    total <- gene_symbol <- gene_id <- gene_id_num <- cell_id <- 
+    total <- gene_symbol <- gene_id <- gene_id_num <- cell_id <-
         cell_id_num <- sort_gene_id_num <- NULL
 
     # data directory
@@ -462,14 +485,16 @@ get10Xmatrix <- function(path_to_data,
     # get barcodes and create vector
     barcodes_file <- grep(files_10X, pattern = "barcodes", value = TRUE)
     barcodesDT <- data.table::fread(
-        input = paste0(path_to_data, "/", barcodes_file), header = FALSE)
+        input = paste0(path_to_data, "/", barcodes_file), header = FALSE
+    )
     barcodes_vec <- barcodesDT$V1
-    names(barcodes_vec) <- 1:nrow(barcodesDT)
+    names(barcodes_vec) <- seq_len(nrow(barcodesDT))
 
     # get features and create vector
     features_file <- grep(files_10X, pattern = "features|genes", value = TRUE)
     featuresDT <- data.table::fread(
-        input = paste0(path_to_data, "/", features_file), header = FALSE)
+        input = paste0(path_to_data, "/", features_file), header = FALSE
+    )
 
     g_name <- colnames(featuresDT)[gene_column_index]
     ## convert ensembl gene id to gene symbol ##
@@ -477,10 +502,11 @@ get10Xmatrix <- function(path_to_data,
 
     featuresDT[, total := .N, by = get(g_name)]
     featuresDT[, gene_symbol := ifelse(
-        total > 1, paste0(get(g_name), "--", 1:.N), 
-        get(g_name)), by = get(g_name)]
+        total > 1, paste0(get(g_name), "--", seq_len(.N)),
+        get(g_name)
+    ), by = get(g_name)]
     features_vec <- featuresDT$gene_symbol
-    names(features_vec) <- 1:nrow(featuresDT)
+    names(features_vec) <- seq_len(nrow(featuresDT))
 
     # get matrix
     matrix_file <- grep(files_10X, pattern = "matrix", value = TRUE)
@@ -524,23 +550,24 @@ get10Xmatrix <- function(path_to_data,
 
 #' @title get10Xmatrix_h5
 #' @name get10Xmatrix_h5
-#' @description This function creates an expression matrix from a 10X h5 file 
+#' @description This function creates an expression matrix from a 10X h5 file
 #' path
 #' @param path_to_data path to the 10X .h5 file
-#' @param gene_ids use gene symbols (default) or ensembl ids for the gene 
+#' @param gene_ids use gene symbols (default) or ensembl ids for the gene
 #' expression matrix
 #' @inheritParams get10Xmatrix
 #' @returns (list of) sparse expression matrix from 10X
-#' @details If the .h5 10x file has multiple classes of features 
-#' (e.g. expression vs QC probes) or modalities (e.g. RNA and protein), and 
+#' @details If the .h5 10x file has multiple classes of features
+#' (e.g. expression vs QC probes) or modalities (e.g. RNA and protein), and
 #' \code{split_by_type} param is \code{TRUE}, multiple matrices will be returned
 #' @export
-get10Xmatrix_h5 <- function(path_to_data,
-    gene_ids = c("symbols", "ensembl"),
-    remove_zero_rows = TRUE,
-    split_by_type = TRUE) {
+get10Xmatrix_h5 <- function(
+        path_to_data,
+        gene_ids = c("symbols", "ensembl"),
+        remove_zero_rows = TRUE,
+        split_by_type = TRUE) {
     ## function inspired by and modified from the VISION package
-    ## see read_10x_h5_v3 in 
+    ## see read_10x_h5_v3 in
     ## https://github.com/YosefLab/VISION/blob/master/R/Utilities.R
 
     # verify if optional package is installed
@@ -593,9 +620,10 @@ get10Xmatrix_h5 <- function(path_to_data,
     # data.table variables
     nr_name <- name <- uniq_name <- NULL
 
-    features_dt[, nr_name := 1:.N, by = name]
+    features_dt[, nr_name := seq_len(.N), by = name]
     features_dt[, uniq_name := ifelse(
-        nr_name == 1, name, paste0(name, "_", (nr_name - 1)))]
+        nr_name == 1, name, paste0(name, "_", (nr_name - 1))
+    )]
 
 
     # dimension names
@@ -615,7 +643,8 @@ get10Xmatrix_h5 <- function(path_to_data,
 
         for (fclass in unique(feature_types)) {
             result_list[[fclass]] <- sparsemat[
-                features_dt$feature_type == fclass, ]
+                features_dt$feature_type == fclass,
+            ]
 
             # change names to gene symbols if it's expression
             if (fclass == "Gene Expression" & gene_ids == "symbols") {
@@ -660,11 +689,11 @@ get10Xmatrix_h5 <- function(path_to_data,
 
 #' @title readPolygonFilesVizgenHDF5
 #' @name readPolygonFilesVizgenHDF5_old
-#' @description Read and create polygons for all cells, or for only selected 
+#' @description Read and create polygons for all cells, or for only selected
 #' FOVs.
 #' @param boundaries_path path to the cell_boundaries folder
 #' @param fovs subset of fovs to use
-#' @param custom_polygon_names a character vector to provide custom polygon 
+#' @param custom_polygon_names a character vector to provide custom polygon
 #' names (optional)
 #' @param polygon_feat_types a vector containing the polygon feature types
 #' @param flip_x_axis flip x axis of polygon coordinates (multiply by -1)
@@ -677,21 +706,22 @@ get10Xmatrix_h5 <- function(path_to_data,
 #' @param verbose be verbose
 #' @seealso \code{\link{smoothGiottoPolygons}}
 #' @returns data.table
-#' @details Set H5Fopen_flags to "H5F_ACC_RDONLY" if you encounter permission 
+#' @details Set H5Fopen_flags to "H5F_ACC_RDONLY" if you encounter permission
 #' issues.
 #' @export
-readPolygonFilesVizgenHDF5_old <- function(boundaries_path,
-    fovs = NULL,
-    polygon_feat_types = 0:6,
-    custom_polygon_names = NULL,
-    flip_x_axis = FALSE,
-    flip_y_axis = FALSE,
-    smooth_polygons = TRUE,
-    smooth_vertices = 60,
-    set_neg_to_zero = FALSE,
-    H5Fopen_flags = "H5F_ACC_RDWR",
-    cores = NA,
-    verbose = TRUE) {
+readPolygonFilesVizgenHDF5_old <- function(
+        boundaries_path,
+        fovs = NULL,
+        polygon_feat_types = 0:6,
+        custom_polygon_names = NULL,
+        flip_x_axis = FALSE,
+        flip_y_axis = FALSE,
+        smooth_polygons = TRUE,
+        smooth_vertices = 60,
+        set_neg_to_zero = FALSE,
+        H5Fopen_flags = "H5F_ACC_RDWR",
+        cores = NA,
+        verbose = TRUE) {
     # necessary pkgs
     package_check(pkg_name = "rhdf5", repository = "Bioc")
 
@@ -707,12 +737,12 @@ readPolygonFilesVizgenHDF5_old <- function(boundaries_path,
     # provide your own custom names
     if (!is.null(custom_polygon_names)) {
         if (!is.character(custom_polygon_names)) {
-            stop(wrap_txt("If custom_polygon_names are provided, it needs to 
+            stop(wrap_txt("If custom_polygon_names are provided, it needs to
                         be a character vector"))
         }
 
         if (length(custom_polygon_names) != length(poly_feat_names)) {
-            stop(wrap_txt("length of custom names need to be same as 
+            stop(wrap_txt("length of custom names need to be same as
                         polygon_feat_types"))
         } else {
             poly_feat_names <- custom_polygon_names
@@ -727,14 +757,17 @@ readPolygonFilesVizgenHDF5_old <- function(boundaries_path,
         selected_hdf5s <- paste0("feature_data_", fovs, ".hdf5")
         selected_hdf5s_concatenated <- paste0(selected_hdf5s, collapse = "|")
         hdf5_boundary_selected_list <- grep(
-            selected_hdf5s_concatenated, x = hdf5_boundary_list, value = TRUE)
+            selected_hdf5s_concatenated,
+            x = hdf5_boundary_list, value = TRUE
+        )
     } else {
         hdf5_boundary_selected_list <- hdf5_boundary_list
     }
 
-    if (isTRUE(verbose)) 
-        wrap_msg("finished listing .hdf5 files start extracting .hdf5 
+    if (isTRUE(verbose)) {
+        wrap_msg("finished listing .hdf5 files start extracting .hdf5
                 information")
+    }
 
     # open selected polygon files
     hdf5_list_length <- length(hdf5_boundary_selected_list)
@@ -749,18 +782,21 @@ readPolygonFilesVizgenHDF5_old <- function(boundaries_path,
             function(bound_i) {
                 # get feature data
                 read_file <- rhdf5::H5Fopen(
-                    hdf5_boundary_selected_list[[bound_i]][[1]], 
-                    flags = H5Fopen_flags)
+                    hdf5_boundary_selected_list[[bound_i]][[1]],
+                    flags = H5Fopen_flags
+                )
                 fov_info <- read_file$featuredata
 
                 # update progress
-                if (verbose) 
+                if (verbose) {
                     print(basename(hdf5_boundary_selected_list[[bound_i]]))
+                }
                 elapsed <- (proc.time() - init)[[3L]]
                 step_time <- elapsed / bound_i
                 est <- (hdf5_list_length * step_time) - elapsed
                 pb(message = c(
-                    "// E:", time_format(elapsed), "| R:", time_format(est)))
+                    "// E:", time_format(elapsed), "| R:", time_format(est)
+                ))
                 rhdf5::H5Fclose(read_file)
                 return(fov_info)
             }
@@ -774,57 +810,67 @@ readPolygonFilesVizgenHDF5_old <- function(boundaries_path,
 
     # extract values for each z index and cell from read_list
     result_list <- lapply_flex(
-        seq_along(poly_feat_indexes), cores = cores, function(z_i) {
-        lapply_flex(seq_along(read_list), cores = cores, function(cell_i) {
-            singlearray <- read_list[[cell_i]][[
-                poly_feat_indexes[z_i]]]$p_0$coordinates
-            cell_name <- cell_names[[cell_i]]
-            if (!is.null(singlearray)) {
-                singlearraydt <- data.table::as.data.table(t_flex(
-                    as.matrix(singlearray[, , 1])))
-                data.table::setnames(
-                    singlearraydt, old = c("V1", "V2"), new = c("x", "y"))
-                if (flip_x_axis) singlearraydt[, x := -1 * x]
-                if (flip_y_axis) singlearraydt[, y := -1 * y]
-
-                singlearraydt[, cell_id := cell_name]
-            }
-        })
-    })
+        seq_along(poly_feat_indexes),
+        cores = cores, function(z_i) {
+            lapply_flex(seq_along(read_list), cores = cores, function(cell_i) {
+                singlearray <- read_list[[cell_i]][[
+                    poly_feat_indexes[z_i]
+                ]]$p_0$coordinates
+                cell_name <- cell_names[[cell_i]]
+                if (!is.null(singlearray)) {
+                    singlearraydt <- data.table::as.data.table(t_flex(
+                        as.matrix(singlearray[, , 1])
+                    ))
+                    data.table::setnames(
+                        singlearraydt,
+                        old = c("V1", "V2"), new = c("x", "y")
+                    )
+                    if (flip_x_axis) singlearraydt[, x := -1 * x]
+                    if (flip_y_axis) singlearraydt[, y := -1 * y]
+
+                    singlearraydt[, cell_id := cell_name]
+                }
+            })
+        }
+    )
     result_list_rbind <- lapply_flex(
-        seq_along(result_list), cores = cores, function(z_i) {
-        data.table::rbindlist(result_list[[z_i]])
-    })
+        seq_along(result_list),
+        cores = cores, function(z_i) {
+            data.table::rbindlist(result_list[[z_i]])
+        }
+    )
 
 
 
-    if (isTRUE(verbose)) 
+    if (isTRUE(verbose)) {
         wrap_msg("finished extracting .hdf5 files start creating polygons")
+    }
 
 
     # create Giotto polygons and add them to gobject
     progressr::with_progress({
         pb <- progressr::progressor(along = result_list_rbind)
-        smooth_cell_polygons_list <- lapply_flex(seq_along(result_list_rbind), 
-                                                cores = cores, function(i) {
-            dfr_subset <- result_list_rbind[[i]][, .(x, y, cell_id)]
-            cell_polygons <- createGiottoPolygonsFromDfr(
-                segmdfr = dfr_subset,
-                name = poly_feat_names[i],
-                verbose = verbose
-            )
+        smooth_cell_polygons_list <- lapply_flex(seq_along(result_list_rbind),
+            cores = cores, function(i) {
+                dfr_subset <- result_list_rbind[[i]][, .(x, y, cell_id)]
+                cell_polygons <- createGiottoPolygonsFromDfr(
+                    segmdfr = dfr_subset,
+                    name = poly_feat_names[i],
+                    verbose = verbose
+                )
 
-            pb(message = poly_feat_names[i])
+                pb(message = poly_feat_names[i])
 
-            if (smooth_polygons == TRUE) {
-                return(smoothGiottoPolygons(cell_polygons,
-                    vertices = smooth_vertices,
-                    set_neg_to_zero = set_neg_to_zero
-                ))
-            } else {
-                return(cell_polygons)
+                if (smooth_polygons == TRUE) {
+                    return(smoothGiottoPolygons(cell_polygons,
+                        vertices = smooth_vertices,
+                        set_neg_to_zero = set_neg_to_zero
+                    ))
+                } else {
+                    return(cell_polygons)
+                }
             }
-        })
+        )
     })
 
 
@@ -840,14 +886,14 @@ readPolygonFilesVizgenHDF5_old <- function(boundaries_path,
 #' @title readPolygonFilesVizgenHDF5
 #' @name readPolygonFilesVizgenHDF5
 #' @description Read polygon info for all cells or for only selected FOVs from
-#' Vizgen HDF5 files. Data is returned as a list of giottoPolygons or 
+#' Vizgen HDF5 files. Data is returned as a list of giottoPolygons or
 #' data.tables of the requested z indices.
 #' @param boundaries_path path to the cell_boundaries folder
 #' @param fovs subset of fovs to use
 #' @param z_indices z indices of polygons to use
 #' @param segm_to_use segmentation results to use (usually = 1. Depends on if
 #' alternative segmentations were generated)
-#' @param custom_polygon_names a character vector to provide custom polygon 
+#' @param custom_polygon_names a character vector to provide custom polygon
 #' names (optional)
 #' @param polygon_feat_types deprecated. Use \code{z_indices}
 #' @param flip_x_axis flip x axis of polygon coordinates (multiply by -1)
@@ -858,36 +904,37 @@ readPolygonFilesVizgenHDF5_old <- function(boundaries_path,
 #' @param calc_centroids calculate centroids (default = FALSE)
 #' @param H5Fopen_flags see \code{\link[rhdf5]{H5Fopen}} for more details
 #' @param cores cores to use
-#' @param create_gpoly_parallel (default = TRUE) Whether to run gpoly creation 
+#' @param create_gpoly_parallel (default = TRUE) Whether to run gpoly creation
 #' in parallel
 #' @param create_gpoly_bin (Optional, default = FALSE) Parallelization option.
-#' Accepts integer values as an binning size when generating giottoPolygon 
+#' Accepts integer values as an binning size when generating giottoPolygon
 #' objects
 #' @param verbose be verbose
 #' @param output whether to return as list of giottoPolygon or data.table
 #' @seealso \code{\link{smoothGiottoPolygons}}
 #' @returns list of giottoPolygon or data.table
-#' @details Set H5Fopen_flags to "H5F_ACC_RDONLY" if you encounter permission 
+#' @details Set H5Fopen_flags to "H5F_ACC_RDONLY" if you encounter permission
 #' issues.
 #' @export
-readPolygonFilesVizgenHDF5 <- function(boundaries_path,
-    fovs = NULL,
-    z_indices = 1L:7L,
-    segm_to_use = 1L,
-    custom_polygon_names = NULL,
-    flip_x_axis = FALSE,
-    flip_y_axis = TRUE,
-    calc_centroids = FALSE,
-    smooth_polygons = TRUE,
-    smooth_vertices = 60L,
-    set_neg_to_zero = FALSE,
-    H5Fopen_flags = "H5F_ACC_RDWR",
-    cores = determine_cores(),
-    create_gpoly_parallel = TRUE,
-    create_gpoly_bin = FALSE,
-    verbose = TRUE,
-    output = c("giottoPolygon", "data.table"),
-    polygon_feat_types = NULL) {
+readPolygonFilesVizgenHDF5 <- function(
+        boundaries_path,
+        fovs = NULL,
+        z_indices = 1L:7L,
+        segm_to_use = 1L,
+        custom_polygon_names = NULL,
+        flip_x_axis = FALSE,
+        flip_y_axis = TRUE,
+        calc_centroids = FALSE,
+        smooth_polygons = TRUE,
+        smooth_vertices = 60L,
+        set_neg_to_zero = FALSE,
+        H5Fopen_flags = "H5F_ACC_RDWR",
+        cores = determine_cores(),
+        create_gpoly_parallel = TRUE,
+        create_gpoly_bin = FALSE,
+        verbose = TRUE,
+        output = c("giottoPolygon", "data.table"),
+        polygon_feat_types = NULL) {
     # necessary pkgs
     package_check(pkg_name = "rhdf5", repository = "Bioc")
 
@@ -907,13 +954,14 @@ readPolygonFilesVizgenHDF5 <- function(boundaries_path,
     # provide your own custom names
     if (!is.null(custom_polygon_names)) {
         if (!is.character(custom_polygon_names)) {
-            stop(wrap_txt("If custom_polygon_names are provided, it needs to 
+            stop(wrap_txt("If custom_polygon_names are provided, it needs to
                         be a character vector"))
         }
 
         if (length(custom_polygon_names) != length(z_indices)) {
             stop(wrap_txt(
-                "length of custom names need to be same as z_indices"))
+                "length of custom names need to be same as z_indices"
+            ))
         }
     }
 
@@ -925,14 +973,17 @@ readPolygonFilesVizgenHDF5 <- function(boundaries_path,
         selected_hdf5s <- paste0("feature_data_", fovs, ".hdf5")
         selected_hdf5s_concatenated <- paste0(selected_hdf5s, collapse = "|")
         hdf5_boundary_selected_list <- grep(
-            selected_hdf5s_concatenated, x = hdf5_boundary_list, value = TRUE)
+            selected_hdf5s_concatenated,
+            x = hdf5_boundary_list, value = TRUE
+        )
     } else {
         hdf5_boundary_selected_list <- hdf5_boundary_list
     }
 
-    if (isTRUE(verbose)) 
-        message("finished listing .hdf5 files start extracting .hdf5 
+    if (isTRUE(verbose)) {
+        message("finished listing .hdf5 files start extracting .hdf5
                 information")
+    }
 
     # open selected polygon files
 
@@ -951,8 +1002,9 @@ readPolygonFilesVizgenHDF5 <- function(boundaries_path,
                 )
 
                 # update progress
-                if (verbose) 
+                if (verbose) {
                     print(basename(hdf5_boundary_selected_list[[bound_i]]))
+                }
                 if (bound_i %% 5 == 0) {
                     pb()
                 }
@@ -1010,15 +1062,16 @@ readPolygonFilesVizgenHDF5 <- function(boundaries_path,
 
 #' @keywords internal
 #' @noRd
-.create_giotto_polygons_vizgen <- function(z_read_DT,
-    poly_names = names(z_read_DT),
-    set_neg_to_zero = FALSE,
-    calc_centroids = FALSE,
-    smooth_polygons = TRUE,
-    smooth_vertices = 60L,
-    create_gpoly_parallel = TRUE,
-    create_gpoly_bin = FALSE,
-    verbose = TRUE) {
+.create_giotto_polygons_vizgen <- function(
+        z_read_DT,
+        poly_names = names(z_read_DT),
+        set_neg_to_zero = FALSE,
+        calc_centroids = FALSE,
+        smooth_polygons = TRUE,
+        smooth_vertices = 60L,
+        create_gpoly_parallel = TRUE,
+        create_gpoly_bin = FALSE,
+        verbose = TRUE) {
     checkmate::assert_list(z_read_DT)
     checkmate::assert_numeric(smooth_vertices)
 
@@ -1033,34 +1086,40 @@ readPolygonFilesVizgenHDF5 <- function(boundaries_path,
             pb <- progressr::progressor(along = z_read_DT)
             smooth_cell_polygons_list <- lapply(
                 seq_along(z_read_DT), function(i) {
-                dfr_subset <- z_read_DT[[i]][, .(x, y, cell_id)]
-                data.table::setnames(
-                    dfr_subset, old = "cell_id", new = "poly_ID")
-                cell_polygons <- createGiottoPolygonsFromDfr(
-                    segmdfr = dfr_subset,
-                    name = poly_names[i],
-                    calc_centroids = FALSE,
-                    skip_eval_dfr = TRUE,
-                    copy_dt = FALSE,
-                    verbose = verbose
-                )
-                if (isTRUE(smooth_polygons)) {
-                    cell_polygons <- smoothGiottoPolygons(
-                        gpolygon = cell_polygons,
-                        vertices = smooth_vertices,
-                        k = 3L,
-                        set_neg_to_zero = set_neg_to_zero
+                    dfr_subset <- z_read_DT[[i]][, .(x, y, cell_id)]
+                    data.table::setnames(
+                        dfr_subset,
+                        old = "cell_id", new = "poly_ID"
                     )
+                    cell_polygons <- createGiottoPolygonsFromDfr(
+                        segmdfr = dfr_subset,
+                        name = poly_names[i],
+                        calc_centroids = FALSE,
+                        skip_eval_dfr = TRUE,
+                        copy_dt = FALSE,
+                        verbose = verbose
+                    )
+                    if (isTRUE(smooth_polygons)) {
+                        cell_polygons <- smoothGiottoPolygons(
+                            gpolygon = cell_polygons,
+                            vertices = smooth_vertices,
+                            k = 3L,
+                            set_neg_to_zero = set_neg_to_zero
+                        )
+                    }
+                    if (isTRUE(calc_centroids)) {
+                        # NOTE: won't recalculate if centroids are already attached
+                        cell_polygons <- centroids(
+                            cell_polygons,
+                            append_gpolygon = TRUE
+                        )
+                    }
+                    pb(message = c(
+                        poly_names[i], " (", i, "/", length(z_read_DT), ")"
+                    ))
+                    return(cell_polygons)
                 }
-                if (isTRUE(calc_centroids)) {
-                    # NOTE: won't recalculate if centroids are already attached
-                    cell_polygons <- centroids(
-                        cell_polygons, append_gpolygon = TRUE)
-                }
-                pb(message = c(
-                    poly_names[i], " (", i, "/", length(z_read_DT), ")"))
-                return(cell_polygons)
-            })
+            )
         })
         return(smooth_cell_polygons_list)
     }
@@ -1077,7 +1136,9 @@ readPolygonFilesVizgenHDF5 <- function(boundaries_path,
                 function(i) {
                     dfr_subset <- z_read_DT[[i]][, .(x, y, cell_id)]
                     data.table::setnames(
-                        dfr_subset, old = "cell_id", new = "poly_ID")
+                        dfr_subset,
+                        old = "cell_id", new = "poly_ID"
+                    )
                     cell_polygons <- gpoly_from_dfr_smoothed_wrapped(
                         segmdfr = dfr_subset,
                         name = poly_names[i],
@@ -1091,7 +1152,8 @@ readPolygonFilesVizgenHDF5 <- function(boundaries_path,
                     )
 
                     pb(message = c(
-                        poly_names[i], " (", i, "/", length(z_read_DT), ")"))
+                        poly_names[i], " (", i, "/", length(z_read_DT), ")"
+                    ))
                     return(cell_polygons)
                 }
             )
@@ -1100,13 +1162,15 @@ readPolygonFilesVizgenHDF5 <- function(boundaries_path,
         # unwrap results
         smooth_cell_polygons_list <- lapply(
             smooth_cell_polygons_list, function(x) {
-            slot(x, "spatVector") <- terra::vect(slot(x, "spatVector"))
-            if (isTRUE(calc_centroids)) {
-                slot(x, "spatVectorCentroids") <- terra::vect(
-                    slot(x, "spatVectorCentroids"))
+                slot(x, "spatVector") <- terra::vect(slot(x, "spatVector"))
+                if (isTRUE(calc_centroids)) {
+                    slot(x, "spatVectorCentroids") <- terra::vect(
+                        slot(x, "spatVectorCentroids")
+                    )
+                }
+                return(x)
             }
-            return(x)
-        })
+        )
     } else {
         # with binning
 
@@ -1125,7 +1189,9 @@ readPolygonFilesVizgenHDF5 <- function(boundaries_path,
                 )
             )
             DT <- data.table::merge.data.table(
-                DT, bin_pid, by = "poly_ID", all.x = TRUE)
+                DT, bin_pid,
+                by = "poly_ID", all.x = TRUE
+            )
             DT <- split(DT, DT$bin_ID)
         }, bin = create_gpoly_bin)
 
@@ -1153,8 +1219,9 @@ readPolygonFilesVizgenHDF5 <- function(boundaries_path,
                             )
 
                             pb(message = c(
-                                poly_names[i], " (", i, "/", 
-                                length(dfr_subset), ")"))
+                                poly_names[i], " (", i, "/",
+                                length(dfr_subset), ")"
+                            ))
                             return(cell_polygons)
                         }
                     )
@@ -1165,18 +1232,20 @@ readPolygonFilesVizgenHDF5 <- function(boundaries_path,
         # unwrap results
         smooth_cell_polygons_list <- lapply(
             seq_along(smooth_cell_polygons_list), function(i) {
-            p_list <- lapply(smooth_cell_polygons_list[[i]], function(x) {
-                slot(x, "spatVector") <- terra::vect(slot(x, "spatVector"))
-                if (isTRUE(calc_centroids)) {
-                    slot(x, "spatVectorCentroids") <- terra::vect(
-                        slot(x, "spatVectorCentroids"))
-                }
-                return(x)
-            })
-            # rbind results
-            names(p_list) <- NULL
-            return(do.call("rbind", p_list))
-        })
+                p_list <- lapply(smooth_cell_polygons_list[[i]], function(x) {
+                    slot(x, "spatVector") <- terra::vect(slot(x, "spatVector"))
+                    if (isTRUE(calc_centroids)) {
+                        slot(x, "spatVectorCentroids") <- terra::vect(
+                            slot(x, "spatVectorCentroids")
+                        )
+                    }
+                    return(x)
+                })
+                # rbind results
+                names(p_list) <- NULL
+                return(do.call("rbind", p_list))
+            }
+        )
     }
 
 
@@ -1192,20 +1261,19 @@ readPolygonFilesVizgenHDF5 <- function(boundaries_path,
 #' @title Read MERSCOPE polygons from parquet
 #' @name readPolygonVizgenParquet
 #' @description
-#' Read Vizgen exported cell boundary parquet files as giottoPolyons. The z 
+#' Read Vizgen exported cell boundary parquet files as giottoPolyons. The z
 #' level can be selected.
 #' @param file parquet file to load
-#' @param z_index either 'all' or a numeric vector of z_indices to get polygons 
+#' @param z_index either 'all' or a numeric vector of z_indices to get polygons
 #' for
 #' @param calc_centroids calculate centroids for the polygons (default = TRUE)
 #' @param verbose be verbose
 #' @returns giottoPolygons
 #' @export
-readPolygonVizgenParquet <- function(
-        file,
-        z_index = "all",
-        calc_centroids = TRUE,
-        verbose = TRUE) {
+readPolygonVizgenParquet <- function(file,
+    z_index = "all",
+    calc_centroids = TRUE,
+    verbose = TRUE) {
     # package checks
     package_check("arrow")
     package_check("sf")
@@ -1226,7 +1294,7 @@ readPolygonVizgenParquet <- function(
     avail_z_idx <- arrow::open_dataset(file) %>%
         dplyr::distinct(ZIndex) %>%
         dplyr::pull() %>%
-        # dplyr::pull(as_vector = TRUE) %>% # switch to this in future and add 
+        # dplyr::pull(as_vector = TRUE) %>% # switch to this in future and add
         # arrow version requirement
         sort()
 
@@ -1235,13 +1303,14 @@ readPolygonVizgenParquet <- function(
     } else if (is.numeric(z_index)) {
         z_index <- as.integer(z_index)
         if (!all(z_index %in% avail_z_idx)) {
-            stop(paste("Not all z indices found in cell boundaries.\n 
+            stop(paste("Not all z indices found in cell boundaries.\n
                     Existing indices are:", paste(avail_z_idx, collapse = " ")))
         }
         z_index
     }
-    if (isTRUE(verbose)) 
+    if (isTRUE(verbose)) {
         message("loading poly z_indices: ", paste(get_z_idx, collapse = " "))
+    }
 
 
     # 2. collect by z index filter and convert WKB to multipolygon
@@ -1261,7 +1330,8 @@ readPolygonVizgenParquet <- function(
         future.seed = TRUE
     )
     names(multipolygons) <- lapply(
-        multipolygons, function(x) paste0("z", unique(x$ZIndex)))
+        multipolygons, function(x) paste0("z", unique(x$ZIndex))
+    )
 
 
     # 3. convert to giottoPolygons and append meta
@@ -1313,17 +1383,18 @@ readPolygonVizgenParquet <- function(
 #' @returns giotto object or cell polygons list
 #' @seealso \code{\link{smoothGiottoPolygons}}
 #' @export
-readPolygonFilesVizgen <- function(gobject,
-    boundaries_path,
-    fovs = NULL,
-    polygon_feat_types = 0:6,
-    flip_x_axis = FALSE,
-    flip_y_axis = FALSE,
-    smooth_polygons = TRUE,
-    smooth_vertices = 60,
-    set_neg_to_zero = FALSE,
-    return_gobject = TRUE,
-    verbose = TRUE) {
+readPolygonFilesVizgen <- function(
+        gobject,
+        boundaries_path,
+        fovs = NULL,
+        polygon_feat_types = 0:6,
+        flip_x_axis = FALSE,
+        flip_y_axis = FALSE,
+        smooth_polygons = TRUE,
+        smooth_vertices = 60,
+        set_neg_to_zero = FALSE,
+        return_gobject = TRUE,
+        verbose = TRUE) {
     # define names
     poly_feat_names <- paste0("z", polygon_feat_types)
     poly_feat_indexes <- paste0("zIndex_", polygon_feat_types)
@@ -1365,18 +1436,20 @@ readPolygonFilesVizgen <- function(gobject,
 
 
 
-#' @describeIn readPolygonFilesVizgen (internal) Optimized .hdf5 reading for 
+#' @describeIn readPolygonFilesVizgen (internal) Optimized .hdf5 reading for
 #' vizgen merscope output. Returns a data.table of xyz coords and cell_id
 #' @keywords internal
-.h5_read_vizgen <- function(h5File,
-    z_indices = 1L:7L,
-    segm_to_use = "p_0",
-    H5Fopen_flags = "H5F_ACC_RDWR") {
+.h5_read_vizgen <- function(
+        h5File,
+        z_indices = 1L:7L,
+        segm_to_use = "p_0",
+        H5Fopen_flags = "H5F_ACC_RDWR") {
     # data.table vars
     group <- name <- cell <- z_name <- otype <- d_name <- cell_id <- NULL
 
     h5_ls <- data.table::setDT(
-        rhdf5::h5ls(h5File, recursive = 5, datasetinfo = FALSE))
+        rhdf5::h5ls(h5File, recursive = 5, datasetinfo = FALSE)
+    )
     cell_names <- as.character(h5_ls[group == "/featuredata", name])
     z_names <- h5_ls[grep("zIndex", name), unique(name)]
 
@@ -1385,10 +1458,12 @@ readPolygonFilesVizgen <- function(gobject,
     dset_names <- dset_names[grep(segm_to_use, group), ]
     # tag cellnames
     dset_names[, cell := gsub(
-        pattern = "/featuredata/|/zIndex.*$", replacement = "", x = group)]
+        pattern = "/featuredata/|/zIndex.*$", replacement = "", x = group
+    )]
     # tag z_names
     dset_names[, z_name := gsub(
-        pattern = "^.*/(zIndex_\\d*).*$", replacement = "\\1", x = group)]
+        pattern = "^.*/(zIndex_\\d*).*$", replacement = "\\1", x = group
+    )]
     # subset by z_indices
     dset_names <- dset_names[z_name %in% z_names[z_indices], ]
     # create full file location
@@ -1401,7 +1476,9 @@ readPolygonFilesVizgen <- function(gobject,
         zvals <- .h5_read_bare(
             file = fid,
             name = paste0(
-                c("/featuredata", cell_name, "z_coordinates"), collapse = "/"),
+                c("/featuredata", cell_name, "z_coordinates"),
+                collapse = "/"
+            ),
             dapl = dapl
         )
         names(zvals) <- z_names
@@ -1411,13 +1488,16 @@ readPolygonFilesVizgen <- function(gobject,
 
         cell_data <- lapply(
             seq(nrow(cell_dsets)), function(fid, dapl, zvals, d_i) {
-            res <- .h5_read_bare(
-                file = fid, name = cell_dsets[d_i, d_name], dapl = dapl)
-            res <- t_flex(res[, , 1L])
-            res <- cbind(res, zvals[cell_dsets[d_i, z_name]])
-            colnames(res) <- c("x", "y", "z")
-            res
-        }, fid = fid, dapl = dapl, zvals = zvals)
+                res <- .h5_read_bare(
+                    file = fid, name = cell_dsets[d_i, d_name], dapl = dapl
+                )
+                res <- t_flex(res[, , 1L])
+                res <- cbind(res, zvals[cell_dsets[d_i, z_name]])
+                colnames(res) <- c("x", "y", "z")
+                res
+            },
+            fid = fid, dapl = dapl, zvals = zvals
+        )
         cell_data <- data.table::as.data.table(do.call("rbind", cell_data))
         cell_data[, cell_id := cell_name]
         cell_data
@@ -1444,7 +1524,7 @@ readPolygonFilesVizgen <- function(gobject,
         PACKAGE = "rhdf5"
     )
     invisible(.Call("_H5Dclose", did, PACKAGE = "rhdf5"))
-    
+
     res
 }
 
@@ -1464,8 +1544,9 @@ readPolygonFilesVizgen <- function(gobject,
 #' @param bin_size bin size to select from .gef file
 #' @returns transcript with coordinates
 #' @export
-getGEFtxCoords <- function(gef_file,
-    bin_size = "bin100") {
+getGEFtxCoords <- function(
+        gef_file,
+        bin_size = "bin100") {
     # data.table vars
     genes <- NULL
 
@@ -1487,9 +1568,9 @@ getGEFtxCoords <- function(gef_file,
     )
     setDT(geneDT)
 
-    # Step 3: Combine read expression and gene data by repeating count 
+    # Step 3: Combine read expression and gene data by repeating count
     # (match offset index)
-    # See STOMICS file format manual for more information about exprDT and 
+    # See STOMICS file format manual for more information about exprDT and
     # geneDT
     exprDT[, genes := rep(x = geneDT$gene, geneDT$count)]
 
diff --git a/R/giotto_viewer.R b/R/giotto_viewer.R
index 2517e5084..c21f66f75 100644
--- a/R/giotto_viewer.R
+++ b/R/giotto_viewer.R
@@ -1,19 +1,21 @@
 #' @title write_giotto_viewer_annotation
-#' @description write out factor-like annotation data from a giotto object for 
+#' @description write out factor-like annotation data from a giotto object for
 #' the Viewer
 #' @param annotation annotation from the data.table from giotto object
 #' @param annot_name name of the annotation
 #' @param output_directory directory where to save the files
 #' @returns write a .txt and .annot file for the selection annotation
 #' @keywords internal
-write_giotto_viewer_annotation <- function(annotation,
-    annot_name = "test",
-    output_directory = getwd()) {
+write_giotto_viewer_annotation <- function(
+        annotation,
+        annot_name = "test",
+        output_directory = getwd()) {
     if (is.numeric(annotation) == TRUE) {
         # annotation information and mapping
         sorted_unique_numbers <- sort(unique(annotation))
         annot_map <- data.table::data.table(
-            num = sorted_unique_numbers, fac = sorted_unique_numbers)
+            num = sorted_unique_numbers, fac = sorted_unique_numbers
+        )
         annot_information <- annotation
     } else {
         # factors to numerics
@@ -54,9 +56,10 @@ write_giotto_viewer_annotation <- function(annotation,
 #' @param output_directory directory where to save the files
 #' @returns write a .txt and .annot file for the selection annotation
 #' @keywords internal
-write_giotto_viewer_numeric_annotation <- function(annotation,
-    annot_name = "test",
-    output_directory = getwd()) {
+write_giotto_viewer_numeric_annotation <- function(
+        annotation,
+        annot_name = "test",
+        output_directory = getwd()) {
     # write to output directory
     annot_inf_map <- paste0(annot_name, "_num_annot_information", ".txt")
     write.table(annotation,
@@ -79,13 +82,16 @@ write_giotto_viewer_numeric_annotation <- function(annotation,
 #' @param output_directory directory where to save the files
 #' @returns write a .txt and .annot file for the selection annotation
 #' @keywords internal
-write_giotto_viewer_dim_reduction <- function(dim_reduction_cell,
-    dim_red = NULL,
-    dim_red_name = NULL,
-    dim_red_rounding = NULL,
-    dim_red_rescale = c(-20, 20),
-    output_directory = getwd()) {
-    dim_red_coord <- dim_reduction_cell[[dim_red]][[dim_red_name]]$coordinates[, 1:2]
+write_giotto_viewer_dim_reduction <- function(
+        dim_reduction_cell,
+        dim_red = NULL,
+        dim_red_name = NULL,
+        dim_red_rounding = NULL,
+        dim_red_rescale = c(-20, 20),
+        output_directory = getwd()) {
+    dim_red_coord <- dim_reduction_cell[[dim_red]][[
+        dim_red_name
+    ]]$coordinates[, seq_len(2)]
 
     if (is.null(dim_red_coord)) {
         cat("\n combination of ", dim_red, " and ", dim_red_name, " does not exist \n")
@@ -98,7 +104,8 @@ write_giotto_viewer_dim_reduction <- function(dim_reduction_cell,
         # rescale dimension reduction coordinates
         if (!is.null(dim_red_rescale) & length(dim_red_rescale) == 2) {
             dim_red_coord <- scales::rescale(
-                x = dim_red_coord, to = dim_red_rescale)
+                x = dim_red_coord, to = dim_red_rescale
+            )
         }
 
         dim_red_name <- paste0(dim_red, "_", dim_red_name, "_dim_coord.txt")
@@ -136,33 +143,34 @@ write_giotto_viewer_dim_reduction <- function(dim_reduction_cell,
 #' include the provided spatial enrichment name (default PAGE or rank)
 #' and add the gene signature names (.e.g cell types) to the numeric annotations parameter.
 #' @export
-exportGiottoViewer <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    spat_loc_name = "raw",
-    output_directory = NULL,
-    spat_enr_names = NULL,
-    factor_annotations = NULL,
-    numeric_annotations = NULL,
-    dim_reductions,
-    dim_reduction_names,
-    expression_values = c("scaled", "normalized", "custom"),
-    dim_red_rounding = NULL,
-    dim_red_rescale = c(-20, 20),
-    expression_rounding = 2,
-    overwrite_dir = TRUE,
-    verbose = TRUE) {
+exportGiottoViewer <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        spat_loc_name = "raw",
+        output_directory = NULL,
+        spat_enr_names = NULL,
+        factor_annotations = NULL,
+        numeric_annotations = NULL,
+        dim_reductions,
+        dim_reduction_names,
+        expression_values = c("scaled", "normalized", "custom"),
+        dim_red_rounding = NULL,
+        dim_red_rescale = c(-20, 20),
+        expression_rounding = 2,
+        overwrite_dir = TRUE,
+        verbose = TRUE) {
     ## output directory ##
     if (file.exists(output_directory)) {
         if (overwrite_dir == TRUE) {
-            message("output directory already exists, files will be 
+            message("output directory already exists, files will be
                     overwritten")
         } else {
-            stop("output directory already exists, change overwrite_dir = TRUE 
+            stop("output directory already exists, change overwrite_dir = TRUE
                 to overwrite files \n")
         }
     } else if (is.null(output_directory)) {
-        message("no output directory is provided, defaults to current 
+        message("no output directory is provided, defaults to current
                 directory: ", getwd(), "\n")
         output_directory <- getwd()
     } else {
@@ -264,10 +272,13 @@ exportGiottoViewer <- function(gobject,
             }
 
             annot_list <- data.table(
-                txtfiles = unlist(text_file_names), names = unlist(annot_names))
+                txtfiles = unlist(text_file_names), names = unlist(annot_names)
+            )
             write.table(annot_list,
-                file = paste0(output_directory, "/", 
-                            "annotation_list", "_", feat, ".txt"),
+                file = paste0(
+                    output_directory, "/",
+                    "annotation_list", "_", feat, ".txt"
+                ),
                 quote = FALSE, row.names = FALSE, col.names = FALSE, sep = " "
             )
         }
@@ -301,10 +312,13 @@ exportGiottoViewer <- function(gobject,
             }
 
             annot_list <- data.table(
-                txtfiles = unlist(text_file_names), names = unlist(annot_names))
+                txtfiles = unlist(text_file_names), names = unlist(annot_names)
+            )
             write.table(annot_list,
-                file = paste0(output_directory, "/", 
-                            "annotation_num_list", "_", feat, ".txt"),
+                file = paste0(
+                    output_directory, "/",
+                    "annotation_num_list", "_", feat, ".txt"
+                ),
                 quote = FALSE, row.names = FALSE, col.names = FALSE, sep = " "
             )
         }
@@ -356,45 +370,48 @@ exportGiottoViewer <- function(gobject,
         expr_values <- as.matrix(expr_values)
 
         # swap cell_IDs for numerical values
-        colnames(expr_values) <- 1:ncol(expr_values)
+        colnames(expr_values) <- seq_len(ncol(expr_values))
         # round values
         if (!is.null(expression_rounding)) {
             expr_values <- round(x = expr_values, digits = expression_rounding)
         }
         output_directory_norm <- normalizePath(output_directory)
         fileWrite_directory <- paste0(
-            output_directory_norm, "/", "giotto_expression.csv")
+            output_directory_norm, "/", "giotto_expression.csv"
+        )
         data.table::fwrite(
-            data.table::as.data.table(expr_values, keep.rownames = "gene"), 
-            file = fileWrite_directory, sep = ",", 
-            quote = FALSE, row.names = FALSE, col.names = TRUE)
+            data.table::as.data.table(expr_values, keep.rownames = "gene"),
+            file = fileWrite_directory, sep = ",",
+            quote = FALSE, row.names = FALSE, col.names = TRUE
+        )
 
 
-        if (verbose == TRUE) 
+        if (verbose == TRUE) {
             cat("finished writing giotto viewer files to", output_directory)
+        }
 
         if (verbose == TRUE) {
             message("=========================================================")
-            message("Next steps. Please manually run the following in a SHELL 
+            message("Next steps. Please manually run the following in a SHELL
                     terminal:")
             message("=========================================================")
             message(paste("cd ", output_directory))
-            message("giotto_setup_image --require-stitch=n --image=n 
-                    --image-multi-channel=n --segmentation=n --multi-fov=n 
+            message("giotto_setup_image --require-stitch=n --image=n
+                    --image-multi-channel=n --segmentation=n --multi-fov=n
                     --output-json=step1.json")
             message("smfish_step1_setup -c step1.json")
-            message("giotto_setup_viewer --num-panel=2 
-                    --input-preprocess-json=step1.json 
-                    --panel-1=PanelPhysicalSimple --panel-2=PanelTsne 
-                    --output-json=step2.json 
+            message("giotto_setup_viewer --num-panel=2
+                    --input-preprocess-json=step1.json
+                    --panel-1=PanelPhysicalSimple --panel-2=PanelTsne
+                    --output-json=step2.json
                     --input-annotation-list=annotation_list.txt")
-            message("smfish_read_config -c step2.json -o test.dec6.js 
+            message("smfish_read_config -c step2.json -o test.dec6.js
                     -p test.dec6.html -q test.dec6.css")
             message("giotto_copy_js_css --output .")
             message("python3 -m http.server")
             message("=========================================================")
-            message("Finally, open your browser, navigate to 
-                    http://localhost:8000/. Then click on the file 
+            message("Finally, open your browser, navigate to
+                    http://localhost:8000/. Then click on the file
                     test.dec6.html to see the viewer.")
             message("For more information, http://spatialgiotto.rc.fas.harvard.edu/giotto.viewer.setup3.html", "\n")
         }
diff --git a/R/gstop.R b/R/gstop.R
index be2a805e1..d83ad98d8 100644
--- a/R/gstop.R
+++ b/R/gstop.R
@@ -2,14 +2,15 @@
 # .n should be increased when called from a nested location if capturing the
 # original call is desired.
 # .n should be increased to 2L when within a generic method
-.gstop <- function(...,
-    sep = " ",
-    strWidth = 100,
-    errWidth = FALSE,
-    .prefix = " ",
-    .initial = "",
-    .n = 1L,
-    .call = FALSE) {
+.gstop <- function(
+        ...,
+        sep = " ",
+        strWidth = 100,
+        errWidth = FALSE,
+        .prefix = " ",
+        .initial = "",
+        .n = 1L,
+        .call = FALSE) {
     GiottoUtils::gstop(
         ...,
         sep = sep,
diff --git a/R/image_registration.R b/R/image_registration.R
index 677e66246..e671ae779 100644
--- a/R/image_registration.R
+++ b/R/image_registration.R
@@ -5,7 +5,7 @@
 
 #' @name .trakem2_rigid_transforms
 #' @title Read trakem2 rigid transforms
-#' @description Extract rigid registration transformation values from FIJI 
+#' @description Extract rigid registration transformation values from FIJI
 #' TrakEM2 xml file. Generated through register_virtual_stack_slices.
 #' @param inputstring string read in from TrakeEM2 xml file
 #' @returns rigid registration transformation values
@@ -56,18 +56,20 @@
 
     out <- c(out, 0, 0)
     out <- data.table::data.table(t(matrix(out)))
-    colnames(out) <- c("Theta", "Xtransform", "Ytransform", "itx", "ity", 
-                    "XFinalTransform", "YFinalTransform")
+    colnames(out) <- c(
+        "Theta", "Xtransform", "Ytransform", "itx", "ity",
+        "XFinalTransform", "YFinalTransform"
+    )
 
-    # itx and ity are additional values in the trakem2 xml files that must be 
-    # added to Xtransform and Ytransform in order to get the final 
+    # itx and ity are additional values in the trakem2 xml files that must be
+    # added to Xtransform and Ytransform in order to get the final
     # transformation values.
-    # only relevant for sampleset with more than 1 slice away from the 
+    # only relevant for sampleset with more than 1 slice away from the
     # reference image
     out$XFinalTransform <- out$Xtransform + out$itx
     out$YFinalTransform <- out$Ytransform + out$ity
 
-    # Multiply theta by -1 due to differences in R and image plotting 
+    # Multiply theta by -1 due to differences in R and image plotting
     # coordinates
     out$Theta <- -out$Theta
 
@@ -78,7 +80,7 @@
 
 #' @title Rigid transform spatial locations
 #' @name .rigid_transform_spatial_locations
-#' @description Performs appropriate transforms to align spatial locations 
+#' @description Performs appropriate transforms to align spatial locations
 #' with registered images.
 #' @param spatlocs input spatial locations
 #' @param transform_values transformation values to use
@@ -86,16 +88,18 @@
 #' @returns spatlocs
 #' @keywords internal
 # Rotation is performed first, followed by XY transform.
-.rigid_transform_spatial_locations <- function(spatlocs,
-    transform_values,
-    method) {
+.rigid_transform_spatial_locations <- function(
+        spatlocs,
+        transform_values,
+        method) {
     if (method == "fiji") {
         spatlocsXY <- spatlocs[, c("sdimx", "sdimy")]
         # These functions must be performed in positive y values
         spatlocsXY$sdimy <- -1 * spatlocsXY$sdimy
 
         spatlocsXY <- spin(spatlocsXY, GiottoUtils::degrees(
-            transform_values$Theta)) %>%
+            transform_values$Theta
+        )) %>%
             spatShift(
                 dx = transform_values$XFinalTransform,
                 dy = transform_values$YFinalTransform
@@ -118,7 +122,7 @@
 
         return(spatlocs)
     } else {
-        stop('Image registration method must be provided. Only "fiji" and 
+        stop('Image registration method must be provided. Only "fiji" and
             "rvision" methods currently supported.')
     }
 }
@@ -135,34 +139,37 @@
 #' @returns list
 #' @keywords internal
 # Automatically account for changes in image size due to alignment
-.reg_img_minmax_finder <- function(gobject_list,
-    image_unreg = NULL,
-    largeImage_unreg = NULL, # TODO Currently unused
-    scale_factor,
-    transform_values,
-    method) {
+.reg_img_minmax_finder <- function(
+        gobject_list,
+        image_unreg = NULL,
+        largeImage_unreg = NULL, # TODO Currently unused
+        scale_factor,
+        transform_values,
+        method) {
     # Find image spatial info from original image if possible
-    # Check to make sure that image_unreg finds an existing image in each 
+    # Check to make sure that image_unreg finds an existing image in each
     # gobject to be registered
     imgPresent <- function(gobject, image, img_type) {
         image %in% list_images_names(gobject = gobject, img_type = img_type)
     }
 
     if (!is.null(image_unreg)) img_type <- "image" # TODO needs reworking
-    if (!is.null(largeImage_unreg)) img_type <- "largeImage" # TODO needs 
-    # reworking - currently only pays attention to 'image' and not 
+    if (!is.null(largeImage_unreg)) img_type <- "largeImage" # TODO needs
+    # reworking - currently only pays attention to 'image' and not
     # 'largeImage' types
 
     if (all(as.logical(lapply(
-        X = gobject_list, FUN = imgPresent, image = image_unreg, 
-        img_type = img_type)))) {
+        X = gobject_list, FUN = imgPresent, image = image_unreg,
+        img_type = img_type
+    )))) {
         giottoImage_list <- lapply(
-            X = gobject_list, FUN = get_giottoImage, name = image_unreg, 
-            image_type = img_type)
+            X = gobject_list, FUN = get_giottoImage, name = image_unreg,
+            image_type = img_type
+        )
         image_corners <- lapply(giottoImage_list, .get_img_corners)
 
         # Infer image corners of registered images PRIOR TO REGISTRATION
-        # scale unreg_image corners to registered image (use 
+        # scale unreg_image corners to registered image (use
         # reg_scalefactor/unreg_scalefactor as scale factor)
         image_corners <- lapply_flex(
             seq_along(gobject_list),
@@ -175,7 +182,7 @@
             }
         )
 
-        # register corners based on transform values (only possible at 
+        # register corners based on transform values (only possible at
         # reg_image scaling)
         image_corners_reg <- lapply(
             seq_along(image_corners),
@@ -193,7 +200,9 @@
             seq_along(image_corners_reg),
             function(x) {
                 rescale(
-                image_corners_reg[[x]], (1 / scale_factor[[x]]), x0 = 0, y0 = 0)
+                    image_corners_reg[[x]], (1 / scale_factor[[x]]),
+                    x0 = 0, y0 = 0
+                )
             }
         )
 
@@ -209,7 +218,7 @@
         # return the minmax values - already scaled to spatlocs
         return(minmaxRegVals)
     } else {
-        warning("Original images must be supplied for registered images to be 
+        warning("Original images must be supplied for registered images to be
                 aligned.")
     }
 }
@@ -217,7 +226,7 @@
 
 #' @title Get image corners
 #' @name .get_img_corners
-#' @description finds four corner spatial coords of giottoImages or 
+#' @description finds four corner spatial coords of giottoImages or
 #' magick-images
 #' @param img_object giottoImage or magick-image to use
 #' @returns data.frame
@@ -253,46 +262,47 @@
 
 #' @title registerGiottoObjectList
 #' @name registerGiottoObjectList
-#' @description Wrapper function for registerGiottoObjectListFiji and 
+#' @description Wrapper function for registerGiottoObjectListFiji and
 #' registerGiottoObjectListRvision
 #' @param gobject_list List of gobjects to register
 #' @param spat_unit spatial unit
-#' @param method Method used to align gobjects. Current options are either 
+#' @param method Method used to align gobjects. Current options are either
 #' using FIJI register_virtual_stack_slices output or rvision
 #' @param image_unreg Gobject image slot to use. Defaults to 'image' (optional)
-#' @param image_reg_name Arbitrary image slot name for registered images to 
+#' @param image_reg_name Arbitrary image slot name for registered images to
 #' occupy. Defaults to replacement of 'image' slot (optional)
 #' @param image_list RVISION - under construction
 #' @param save_dir RVISION - under construction
-#' @param spatloc_unreg Unregistered spatial locations to align. Defaults to 
+#' @param spatloc_unreg Unregistered spatial locations to align. Defaults to
 #' 'raw' slot (optional)
-#' @param spatloc_reg_name Arbitrary name for registered spatial locations. 
+#' @param spatloc_reg_name Arbitrary name for registered spatial locations.
 #' Defaults to replacement of 'raw' slot (optional)
 #' @param fiji_xml_files Filepaths to FIJI registration XML outputs
-#' @param fiji_registered_images Registered images output by FIJI 
+#' @param fiji_registered_images Registered images output by FIJI
 #' register_virtual_stack_slices
 #' @param scale_factor Scaling to be applied to spatial coordinates
-#' @param allow_rvision_autoscale Whether or not to allow rvision to 
+#' @param allow_rvision_autoscale Whether or not to allow rvision to
 #' automatically scale the images when performing image registration
 #' @param verbose Be verbose
-#' @returns List of registered giotto objects where the registered images and 
+#' @returns List of registered giotto objects where the registered images and
 #' spatial locations
 #' @export
-registerGiottoObjectList <- function(gobject_list,
-    spat_unit = NULL,
-    method = c("fiji", "rvision"),
-    image_unreg = "image",
-    image_reg_name = "image",
-    image_list = NULL, # Rvision
-    save_dir = NULL, # Rvision
-    spatloc_unreg = "raw",
-    spatloc_reg_name = "raw",
-    fiji_xml_files,
-    fiji_registered_images,
-    scale_factor = NULL,
-    allow_rvision_autoscale = TRUE, # Rvision
-    # auto_comp_reg_border = TRUE,
-    verbose = TRUE) {
+registerGiottoObjectList <- function(
+        gobject_list,
+        spat_unit = NULL,
+        method = c("fiji", "rvision"),
+        image_unreg = "image",
+        image_reg_name = "image",
+        image_list = NULL, # Rvision
+        save_dir = NULL, # Rvision
+        spatloc_unreg = "raw",
+        spatloc_reg_name = "raw",
+        fiji_xml_files,
+        fiji_registered_images,
+        scale_factor = NULL,
+        allow_rvision_autoscale = TRUE, # Rvision
+        # auto_comp_reg_border = TRUE,
+        verbose = TRUE) {
     method <- match.arg(method, choices = c("fiji", "rvision"))
 
     if (method == "fiji") {
@@ -318,7 +328,7 @@ registerGiottoObjectList <- function(gobject_list,
             verbose = verbose
         )
     } else {
-        stop("Invalid method input\n Only fiji and rvision methods are 
+        stop("Invalid method input\n Only fiji and rvision methods are
             currently supported.")
     }
 
@@ -328,43 +338,44 @@ registerGiottoObjectList <- function(gobject_list,
 
 #' @title registerGiottoObjectListFiji
 #' @name registerGiottoObjectListFiji
-#' @description Function to spatially align gobject data based on FIJI image 
+#' @description Function to spatially align gobject data based on FIJI image
 #' registration.
 #' @param gobject_list list of gobjects to register
 #' @param spat_unit spatial unit
-#' @param image_unreg name of original unregistered images. Defaults to 
+#' @param image_unreg name of original unregistered images. Defaults to
 #' 'image' (optional)
-#' @param image_reg_name arbitrary name for registered images to occupy. 
+#' @param image_reg_name arbitrary name for registered images to occupy.
 #' Defaults to replacement of 'image' (optional)
-#' @param image_replace_name arbitrary name for any images replaced due to 
+#' @param image_replace_name arbitrary name for any images replaced due to
 #' image_reg_name argument (optional)
-#' @param registered_images registered images output by FIJI 
+#' @param registered_images registered images output by FIJI
 #' register_virtual_stack_slices
 #' @param spatloc_unreg spatial locations to use. Defaults to 'raw' (optional)
-#' @param spatloc_reg_name name for registered spatial locations. Defaults to 
+#' @param spatloc_reg_name name for registered spatial locations. Defaults to
 #' replacement of 'raw' (optional)
-#' @param spatloc_replace_name arbitrary name for any spatial locations 
+#' @param spatloc_replace_name arbitrary name for any spatial locations
 #' replaced due to spatloc_reg_name argument (optional)
-#' @param xml_files atomic vector of filepaths to xml outputs from FIJI 
+#' @param xml_files atomic vector of filepaths to xml outputs from FIJI
 #' register_virtual_stack_slices
-#' @param scale_factor vector of scaling factors of images used in registration 
+#' @param scale_factor vector of scaling factors of images used in registration
 #' vs spatlocs
 #' @param verbose be verbose
-#' @returns list of registered giotto objects where the registered images and 
+#' @returns list of registered giotto objects where the registered images and
 #' spatial locations
 #' @export
-registerGiottoObjectListFiji <- function(gobject_list,
-    spat_unit = NULL,
-    image_unreg = "image",
-    image_reg_name = "image",
-    image_replace_name = "unregistered",
-    registered_images = NULL,
-    spatloc_unreg = "raw",
-    spatloc_reg_name = "raw",
-    spatloc_replace_name = "unregistered",
-    xml_files,
-    scale_factor = NULL,
-    verbose = TRUE) {
+registerGiottoObjectListFiji <- function(
+        gobject_list,
+        spat_unit = NULL,
+        image_unreg = "image",
+        image_reg_name = "image",
+        image_replace_name = "unregistered",
+        registered_images = NULL,
+        spatloc_unreg = "raw",
+        spatloc_reg_name = "raw",
+        spatloc_replace_name = "unregistered",
+        xml_files,
+        scale_factor = NULL,
+        verbose = TRUE) {
     # set spat_unit based on first gobject
     spat_unit <- set_default_spat_unit(
         gobject = gobject_list[[1]],
@@ -373,20 +384,22 @@ registerGiottoObjectListFiji <- function(gobject_list,
 
     ## 0. Check Params ##
     if (length(gobject_list) != length(xml_files)) {
-        stop("xml spatial transforms must be supplied for every gobject to be 
+        stop("xml spatial transforms must be supplied for every gobject to be
             registered.")
     }
 
     if (is.null(registered_images) == FALSE) {
-        # If there are not the same number of registered images as gobjects, 
+        # If there are not the same number of registered images as gobjects,
         # stop
         if (length(registered_images) != length(gobject_list)) {
-            stop("A registered image should be supplied for every gobject to 
+            stop("A registered image should be supplied for every gobject to
                 align")
         }
         if (sum(as.logical(lapply(
-            registered_images, methods::is, class2 = "giottoImage"))) > 0) {
-            stop("Registered images should be supplied as either magick-objects 
+            registered_images, methods::is,
+            class2 = "giottoImage"
+        ))) > 0) {
+            stop("Registered images should be supplied as either magick-objects
                 or filepaths")
         }
     }
@@ -395,15 +408,15 @@ registerGiottoObjectListFiji <- function(gobject_list,
         if (!is.numeric(scale_factor)) {
             stop("scale_factor only accepts numerics")
         }
-        if ((length(scale_factor) != length(gobject_list)) && 
+        if ((length(scale_factor) != length(gobject_list)) &&
             (length(scale_factor) != 1)) {
-            stop("If more than one scale_factor is given, there must be one for 
+            stop("If more than one scale_factor is given, there must be one for
                 each gobject to be registered.")
         }
     }
 
 
-    # scale_factors will always be given externally. Registered images do not 
+    # scale_factors will always be given externally. Registered images do not
     # have gobjects yet.
     # expand scale_factor if given as a single value
     scale_list <- c()
@@ -435,7 +448,9 @@ registerGiottoObjectListFiji <- function(gobject_list,
         t_file <- xml_files[[file_i]]
         #------ Put all transform files together
         transf_list[[file_i]] <- paste(
-            readLines(t_file, warn = FALSE), collapse = "\n")
+            readLines(t_file, warn = FALSE),
+            collapse = "\n"
+        )
     }
 
     # Select useful info out of the TrakEM2 files
@@ -492,19 +507,23 @@ registerGiottoObjectListFiji <- function(gobject_list,
         # Params check for conflicting names
         if (verbose == TRUE) {
             if (image_unreg == image_reg_name) {
-                cat("Registered image name already used. Previous image named ",
-                    image_reg_name, " renamed to ", image_replace_name)
+                cat(
+                    "Registered image name already used. Previous image named ",
+                    image_reg_name, " renamed to ", image_replace_name
+                )
             }
             if (spatloc_unreg == spatloc_reg_name) {
-                cat("Registered spatloc name already used. 
-                    Previous spatloc named ", spatloc_reg_name, 
-                    " renamed to ", spatloc_replace_name)
+                cat(
+                    "Registered spatloc name already used.
+                    Previous spatloc named ", spatloc_reg_name,
+                    " renamed to ", spatloc_replace_name
+                )
             }
         }
 
 
         # Update Spatial
-        # Rename original spatial locations to 'unregistered' if conflicting 
+        # Rename original spatial locations to 'unregistered' if conflicting
         # with output
         if (spatloc_unreg == spatloc_reg_name) {
             gobj <- set_spatial_locations(
@@ -531,7 +550,7 @@ registerGiottoObjectListFiji <- function(gobject_list,
 
 
         # Update images
-        # If there is an existing image with the image_reg_name, rename it 
+        # If there is an existing image with the image_reg_name, rename it
         # "unregistered"
         # Move the original image to 'unregistered'
         if (image_unreg == image_reg_name) {
@@ -571,7 +590,8 @@ registerGiottoObjectListFiji <- function(gobject_list,
                 ))
 
                 names(boundaries) <- c(
-                    "xmax_adj", "xmin_adj", "ymax_adj", "ymin_adj")
+                    "xmax_adj", "xmin_adj", "ymax_adj", "ymin_adj"
+                )
 
                 gobj@images[[image_reg_name]]@boundaries <- boundaries
             }
@@ -581,30 +601,31 @@ registerGiottoObjectListFiji <- function(gobject_list,
     return(gobject_list)
 }
 
-# TODO check if spatloc is actually provided in createGiottoImage() and ignore 
+# TODO check if spatloc is actually provided in createGiottoImage() and ignore
 # auto align if not.
 
 #' @title registerGiottoObjectListRvision
 #' @name registerGiottoObjectListRvision
-#' @description Function to spatially align gobject data based on Rvision image 
+#' @description Function to spatially align gobject data based on Rvision image
 #' registration.
 #' @param gobject_list list of gobjects to register
 #' @param image_list Filepaths to unregistered images
 #' @param save_dir (Optional) If given, save registered images to this directory
 #' @param spatloc_unreg spatial locations to use
-#' @param spatloc_reg_name name for registered spatial locations to. Defaults 
+#' @param spatloc_reg_name name for registered spatial locations to. Defaults
 #' to replacement of spat_unreg (optional)
 #' @param verbose be verbose
-#' @returns list of registered giotto objects where the registered images and 
+#' @returns list of registered giotto objects where the registered images and
 #' spatial locations
 #' @export
 # Register giotto objects when given raw images and spatial locations
-registerGiottoObjectListRvision <- function(gobject_list = gobject_list,
-    image_list = NULL,
-    save_dir = NULL,
-    spatloc_unreg = NULL,
-    spatloc_reg_name = "raw",
-    verbose = TRUE) { # Not used
+registerGiottoObjectListRvision <- function(
+        gobject_list = gobject_list,
+        image_list = NULL,
+        save_dir = NULL,
+        spatloc_unreg = NULL,
+        spatloc_reg_name = "raw",
+        verbose = TRUE) { # Not used
 
     package_check(
         pkg_name = "Rvision",
@@ -635,11 +656,13 @@ registerGiottoObjectListRvision <- function(gobject_list = gobject_list,
     color_images <- c()
     for (path in image_list) {
         unreg_images <- append(
-            unreg_images, Rvision::image(filename = path), 
-            after = length(unreg_images))
+            unreg_images, Rvision::image(filename = path),
+            after = length(unreg_images)
+        )
         color_images <- append(
-            color_images, Rvision::image(filename = path), 
-            after = length(color_images))
+            color_images, Rvision::image(filename = path),
+            after = length(color_images)
+        )
     }
 
     ## 3. Perform preprocessing
@@ -648,7 +671,9 @@ registerGiottoObjectListRvision <- function(gobject_list = gobject_list,
     for (image_i in seq_along(unreg_images)) {
         # Make images grayscale
         Rvision::changeColorSpace(
-            unreg_images[[image_i]], colorspace = "GRAY", target = "self")
+            unreg_images[[image_i]],
+            colorspace = "GRAY", target = "self"
+        )
         # Retrieve image dimensions
         dims <- dim(unreg_images[[image_i]])
         rows <- append(rows, dims[[1]], after = length(rows))
@@ -662,16 +687,24 @@ registerGiottoObjectListRvision <- function(gobject_list = gobject_list,
     for (i in seq_along(unreg_images)) {
         # Add border so all images have same square dimensions
         Rvision::border(
-            unreg_images[[i]], squmax - rows[[i]], 0, 
-            squmax - cols[[i]], 0, border_color = "white", target = "self")
+            unreg_images[[i]], squmax - rows[[i]], 0,
+            squmax - cols[[i]], 0,
+            border_color = "white", target = "self"
+        )
         Rvision::border(
-            color_images[[i]], squmax - rows[[i]], 0, 
-            squmax - cols[[i]], 0, border_color = "white", target = "self")
+            color_images[[i]], squmax - rows[[i]], 0,
+            squmax - cols[[i]], 0,
+            border_color = "white", target = "self"
+        )
         # Apply scaling so all images of reasonable size for processing
         unreg_images[[i]] <- Rvision::resize(
-            unreg_images[[i]], height = enddim, width = enddim, target = "new")
+            unreg_images[[i]],
+            height = enddim, width = enddim, target = "new"
+        )
         color_images[[i]] <- Rvision::resize(
-            color_images[[i]], height = enddim, width = enddim, target = "new")
+            color_images[[i]],
+            height = enddim, width = enddim, target = "new"
+        )
     }
     rm(cols, rows)
 
@@ -683,8 +716,10 @@ registerGiottoObjectListRvision <- function(gobject_list = gobject_list,
     transfs <- base::vector(mode = "list", length = length(unreg_images))
     for (i in seq_along(unreg_images)) {
         transfs[[i]] <- Rvision::findTransformECC(
-            refImage, unreg_images[[i]], warp_mode = "euclidean", 
-            filt_size = 101)
+            refImage, unreg_images[[i]],
+            warp_mode = "euclidean",
+            filt_size = 101
+        )
     }
     rm(refImage)
 
@@ -693,10 +728,14 @@ registerGiottoObjectListRvision <- function(gobject_list = gobject_list,
     for (i in seq_along(unreg_images)) {
         # Apply scaling
         spatloc_list[[i]][] <- rescale(
-            spatloc_list[[i]][], enddim / squmax, x0 = 0, y0 = 0)
+            spatloc_list[[i]][], enddim / squmax,
+            x0 = 0, y0 = 0
+        )
         # Apply transform to spatlocs
         spatloc_list[[i]][] <- .rigid_transform_spatial_locations(
-            spatloc_list[[i]][], transfs[[i]], method = "rvision")
+            spatloc_list[[i]][], transfs[[i]],
+            method = "rvision"
+        )
     }
     rm(squmax, enddim)
 
@@ -733,9 +772,13 @@ registerGiottoObjectListRvision <- function(gobject_list = gobject_list,
         # Apply transform to image
         transf_images <- c()
         for (i in seq_along(unreg_images)) {
-            transf_images <- append(transf_images, Rvision::warpAffine(
-                color_images[[i]], transfs[[i]], target = "new"), 
-                length(transf_images))
+            transf_images <- append(
+                transf_images, Rvision::warpAffine(
+                    color_images[[i]], transfs[[i]],
+                    target = "new"
+                ),
+                length(transf_images)
+            )
         }
         # Save images to save directory
         for (image_i in seq_along(transf_images)) {
@@ -777,8 +820,10 @@ fiji <- function(fijiPath = NULL) {
         fijiPath <- getOption("giotto.fiji")
         if (!is.null(fijiPath)) {
             if (!file.exists(fijiPath)) {
-                stop("fiji is not at: ", fijiPath, 
-                    " as specified by options('giotto.fiji')!")
+                stop(
+                    "fiji is not at: ", fijiPath,
+                    " as specified by options('giotto.fiji')!"
+                )
             }
         } else {
             # look for it in sensible places
@@ -789,7 +834,7 @@ fiji <- function(fijiPath = NULL) {
                 } else {
                     stop(
                         "Unable to find fiji! ",
-                        "Set options('giotto.fiji') to point to the fiji 
+                        "Set options('giotto.fiji') to point to the fiji
                         command line executable!"
                     )
                 }
@@ -804,13 +849,13 @@ fiji <- function(fijiPath = NULL) {
 
 #' @title registerImagesFIJI
 #' @name registerImagesFIJI
-#' @description Wrapper function for Register Virtual Stack Slices plugin in 
+#' @description Wrapper function for Register Virtual Stack Slices plugin in
 #' FIJI
 #' @param source_img_dir Folder containing images to be registered
 #' @param output_img_dir Folder to save registered images to
-#' @param transforms_save_dir (jython implementation only) Folder to save 
+#' @param transforms_save_dir (jython implementation only) Folder to save
 #' transforms to
-#' @param ref_img_name (jython implementation only) File name of reference 
+#' @param ref_img_name (jython implementation only) File name of reference
 #' image for the registration
 #' @param init_gauss_blur Point detector option: initial image blurring
 #' @param steps_per_scale_octave Point detector option
@@ -834,41 +879,42 @@ fiji <- function(fijiPath = NULL) {
 #'   \code{options(giotto.fiji="/some/path")})
 #' @param DryRun Whether to return the command to be run rather than actually
 #'   executing it.
-#' @returns list of registered giotto objects where the registered images and 
+#' @returns list of registered giotto objects where the registered images and
 #' spatial locations
-#' @details This function was adapted from runFijiMacro function in 
+#' @details This function was adapted from runFijiMacro function in
 #' jimpipeline by jefferislab
 #'
 #' @export
-registerImagesFIJI <- function(source_img_dir,
-    output_img_dir,
-    transforms_save_dir,
-    ref_img_name,
-    # Scale Invariant Interest Point Detector Options
-    init_gauss_blur = 1.6,
-    steps_per_scale_octave = 3,
-    min_img_size = 64,
-    max_img_size = 1024,
-    # Feature Descriptor Options
-    feat_desc_size = 8,
-    feat_desc_orient_bins = 8,
-    closest_next_closest_ratio = 0.92,
-    # Geometric Consensus Filter Options
-    max_align_err = 25,
-    inlier_ratio = 0.05,
-    # FIJI Options
-    headless = FALSE,
-    batch = TRUE,
-    MinMem = MaxMem,
-    MaxMem = 2500,
-    IncrementalGC = TRUE,
-    Threads = NULL,
-    fijiArgs = NULL,
-    javaArgs = NULL,
-    ijArgs = NULL,
-    jython = FALSE,
-    fijiPath = fiji(),
-    DryRun = FALSE) {
+registerImagesFIJI <- function(
+        source_img_dir,
+        output_img_dir,
+        transforms_save_dir,
+        ref_img_name,
+        # Scale Invariant Interest Point Detector Options
+        init_gauss_blur = 1.6,
+        steps_per_scale_octave = 3,
+        min_img_size = 64,
+        max_img_size = 1024,
+        # Feature Descriptor Options
+        feat_desc_size = 8,
+        feat_desc_orient_bins = 8,
+        closest_next_closest_ratio = 0.92,
+        # Geometric Consensus Filter Options
+        max_align_err = 25,
+        inlier_ratio = 0.05,
+        # FIJI Options
+        headless = FALSE,
+        batch = TRUE,
+        MinMem = MaxMem,
+        MaxMem = 2500,
+        IncrementalGC = TRUE,
+        Threads = NULL,
+        fijiArgs = NULL,
+        javaArgs = NULL,
+        ijArgs = NULL,
+        jython = FALSE,
+        fijiPath = fiji(),
+        DryRun = FALSE) {
     # Check if output directory exists. If not, create the directory
     if (!file.exists(output_img_dir)) {
         dir.create(output_img_dir)
@@ -882,20 +928,24 @@ registerImagesFIJI <- function(source_img_dir,
     if (headless) fijiArgs <- c(fijiArgs, "--headless")
     fijiArgs <- paste(fijiArgs, collapse = " ")
 
-    javaArgs <- c(paste("-Xms", MinMem, "m", sep = ""), 
-                paste("-Xmx", MaxMem, "m", sep = ""), javaArgs)
+    javaArgs <- c(
+        paste("-Xms", MinMem, "m", sep = ""),
+        paste("-Xmx", MaxMem, "m", sep = ""), javaArgs
+    )
     if (IncrementalGC) javaArgs <- c(javaArgs, "-Xincgc")
     javaArgs <- paste(javaArgs, collapse = " ")
 
     threadAdjust <- ifelse(
-        is.null(Threads), "", 
-        paste("run(\"Memory & Threads...\", \"parallel=", Threads, "\");", 
-            sep = ""))
+        is.null(Threads), "",
+        paste("run(\"Memory & Threads...\", \"parallel=", Threads, "\");",
+            sep = ""
+        )
+    )
 
     if (jython == TRUE) {
         # TODO Add check to see if jython script is installed.
-        message('jython implementation requires Headless_RVSS.py in 
-        "/Giotto/inst/fiji/" to be copied to 
+        message('jython implementation requires Headless_RVSS.py in
+        "/Giotto/inst/fiji/" to be copied to
         "/Applications/Fiji.app/plugins/Scripts/MyScripts/Headless_RVSS.py"')
 
         macroCall <- paste(" -eval '",
@@ -1009,15 +1059,15 @@ parse_affine <- function(x) {
 # install_FIJI_scripts = function(fiji = fiji()) {}
 
 # TODO These things require a correct set of boundary values
-# - Subset images in Giotto using Magick and followup reassignment as the 
+# - Subset images in Giotto using Magick and followup reassignment as the
 # default 'image'
 # - Follow this up with potential registration
-# - Need a way to determine the pixel distances between spots to get an idea of 
+# - Need a way to determine the pixel distances between spots to get an idea of
 # which regions of image 'belong' to a spot
-# - Would be nice to be able to put together an image mask even in magick and 
-# apply it to the image to aid with img_reg and take care of jagged lines after 
+# - Would be nice to be able to put together an image mask even in magick and
+# apply it to the image to aid with img_reg and take care of jagged lines after
 # image subsetting
 # - A shiny app to subset tissue regions would be nice
 # The shiny app should be able to select spots in a 2d plane by default
-# If given the ability, it should also select spots of a single plane or within 
+# If given the ability, it should also select spots of a single plane or within
 # a certain range of z values and plot them as a 2D for selection purposes
diff --git a/R/interactivity.R b/R/interactivity.R
index 4446a0704..16e61af35 100644
--- a/R/interactivity.R
+++ b/R/interactivity.R
@@ -2,7 +2,7 @@
 
 #' Select image regions by plotting interactive polygons
 #'
-#' @description Plot interactive polygons on an image and retrieve the polygons 
+#' @description Plot interactive polygons on an image and retrieve the polygons
 #' coordinates.
 #' @param x A `ggplot` or `rast` plot object to draw polygons on
 #' @param width,height An integer, defining the width/height in pixels.
@@ -11,10 +11,11 @@
 #' @returns A `data.table` containing x,y coordinates from the plotted polygons.
 #'
 #' @export
-plotInteractivePolygons <- function(x,
-    width = "auto",
-    height = "auto",
-    ...) {
+plotInteractivePolygons <- function(
+        x,
+        width = "auto",
+        height = "auto",
+        ...) {
     package_check(pkg_name = "miniUI", repository = "CRAN")
     package_check(pkg_name = "shiny", repository = "CRAN")
 
@@ -29,8 +30,10 @@ plotInteractivePolygons <- function(x,
             miniUI::gadgetTitleBar("Plot Interactive Polygons"),
             miniUI::miniContentPanel(
                 shiny::textInput(
-                    "polygon_name", label = "Polygon name", 
-                    value = "polygon 1"),
+                    "polygon_name",
+                    label = "Polygon name",
+                    value = "polygon 1"
+                ),
                 shiny::sliderInput("xrange",
                     label = "x coordinates",
                     min = min(terra::ext(x))[1],
@@ -57,8 +60,10 @@ plotInteractivePolygons <- function(x,
             miniUI::gadgetTitleBar("Plot Interactive Polygons"),
             miniUI::miniContentPanel(
                 shiny::textInput(
-                    "polygon_name", label = "Polygon name", 
-                    value = "polygon 1"),
+                    "polygon_name",
+                    label = "Polygon name",
+                    value = "polygon 1"
+                ),
                 shiny::sliderInput("xrange",
                     label = "x coordinates",
                     min = min(x[["layers"]][[1]]$data$sdimx),
@@ -89,9 +94,11 @@ plotInteractivePolygons <- function(x,
                     x$coordinates$default <- TRUE
                     x +
                         geom_polygon(
-                            data = clicklist(), 
-                            aes(x, y, color = name, fill = name),
-                            alpha = 0, ...
+                            data = clicklist(),
+                            aes(x, y, color = name),
+                            alpha = 0, 
+                            show.legend = FALSE,
+                            ...
                         ) +
                         coord_fixed(
                             xlim = c(input$xrange[1], input$xrange[2]),
@@ -100,8 +107,10 @@ plotInteractivePolygons <- function(x,
                         theme(legend.position = "none")
                 } else {
                     terra::plot(x)
-                    lapply(split(clicklist(), by = "name"), 
-                        function(x) graphics::polygon(x$x, x$y, ...))
+                    lapply(
+                        split(clicklist(), by = "name"),
+                        function(x) graphics::polygon(x$x, x$y, ...)
+                    )
                 }
             },
             res = 96,
@@ -110,14 +119,16 @@ plotInteractivePolygons <- function(x,
         )
 
         clicklist <- shiny::reactiveVal(data.table::data.table(
-            x = numeric(), y = numeric(), name = character())) # empty table
+            x = numeric(), y = numeric(), name = character()
+        )) # empty table
         shiny::observeEvent(input$plot_click, {
             click_x <- input$plot_click$x
             click_y <- input$plot_click$y
             polygon_name <- input$polygon_name
             temp <- clicklist() # get the table of past clicks
             temp <- rbind(temp, data.table::data.table(
-                x = click_x, y = click_y, name = polygon_name))
+                x = click_x, y = click_y, name = polygon_name
+            ))
             clicklist(temp)
         })
 
@@ -139,20 +150,23 @@ plotInteractivePolygons <- function(x,
 #' @param polygon_name name of polygon selections
 #' @param spat_unit spatial unit, default = 'cell'
 #' @param spat_loc_name name of spatial locations to use, default = 'raw'
-#' @param polygons character. A vector with polygon names to extract cells 
+#' @param polygons character. A vector with polygon names to extract cells
 #' from. If NULL, cells from all polygons are retrieved
 #'
-#' @returns A terra 'SpatVector' with cell ID, x y coordinates, and polygon ID 
+#' @returns A terra 'SpatVector' with cell ID, x y coordinates, and polygon ID
 #' where each cell is located in.
 #' @examples
 #' ## Plot interactive polygons
 #' g <- GiottoData::loadGiottoMini("visium")
-#' my_polygon_coords <- data.frame(poly_ID = rep("polygon1", 3), 
-#' sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202))
+#' my_polygon_coords <- data.frame(
+#'     poly_ID = rep("polygon1", 3),
+#'     sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202)
+#' )
 #'
 #' ## Add polygon coordinates to Giotto object
-#' my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords, 
-#' name = "selections")
+#' my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords,
+#'     name = "selections"
+#' )
 #' g <- addGiottoPolygons(
 #'     gobject = g,
 #'     gpolygons = list(my_giotto_polygons)
@@ -165,33 +179,35 @@ plotInteractivePolygons <- function(x,
 #' getCellsFromPolygon(g)
 #'
 #' @export
-getCellsFromPolygon <- function(gobject,
-    polygon_name = "selections",
-    spat_unit = "cell",
-    spat_loc_name = "raw",
-    polygons = NULL) {
+getCellsFromPolygon <- function(
+        gobject,
+        polygon_name = "selections",
+        spat_unit = "cell",
+        spat_loc_name = "raw",
+        polygons = NULL) {
     if (!inherits(gobject, "giotto")) {
         stop("gobject needs to be a giotto object")
     }
 
     ## get polygons spatial info
-    polygon_spatVector <- get_polygon_info(
+    polygon_spatVector <- getPolygonInfo(
         gobject = gobject,
         polygon_name = polygon_name,
         return_giottoPolygon = FALSE
     )
 
     ## get cell spatial locations
-    spatial_locs <- get_spatial_locations(
+    spatial_locs <- getSpatialLocations(
         gobject = gobject,
         spat_unit = spat_unit,
-        spat_loc_name = spat_loc_name,
+        name = spat_loc_name,
         output = "data.table",
         copy_obj = TRUE
     )
 
     ## convert cell spatial locations to spatVector
-    cells_spatVector <- terra::vect(as.matrix(spatial_locs[, 1:2]),
+    cells_spatVector <- terra::vect(
+        as.matrix(spatial_locs[, c("sdimx", "sdimy")]),
         type = "points",
         atts = spatial_locs
     )
@@ -200,7 +216,8 @@ getCellsFromPolygon <- function(gobject,
 
     if (!is.null(polygons)) {
         polygonCells <- terra::subset(
-            polygonCells, polygonCells$poly_ID %in% polygons)
+            polygonCells, polygonCells$poly_ID %in% polygons
+        )
     }
 
     return(polygonCells)
@@ -214,51 +231,60 @@ getCellsFromPolygon <- function(gobject,
 #' @param feat_type feature name where metadata will be added
 #' @param spat_unit spatial unit
 #' @param spat_loc_name name of spatial locations to use
-#' @param polygons polygon names to plot (e.g. 'polygon_1'). If NULL, plots 
+#' @param polygons polygon names to plot (e.g. 'polygon_1'). If NULL, plots
 #' all available polygons
-#' @param na.label polygon label for cells located outside of polygons area. 
+#' @param na.label polygon label for cells located outside of polygons area.
 #' Default = "no_polygon"
 #'
 #' @returns A Giotto object with a modified cell_metadata slot that includes the
-#' polygon name where each cell is located or no_polygon label if the cell is 
+#' polygon name where each cell is located or no_polygon label if the cell is
 #' not located within a polygon area
 #' @examples
 #' ## Plot interactive polygons
 #' g <- GiottoData::loadGiottoMini("visium")
-#' my_polygon_coords <- data.frame(poly_ID = rep("polygon1", 3), 
-#' sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202))
+#' my_polygon_coords <- data.frame(
+#'     poly_ID = rep("polygon1", 3),
+#'     sdimx = c(5477, 5959, 4720),
+#'     sdimy = c(-4125, -2808, -5202)
+#' )
 #'
 #' ## Add polygon coordinates to Giotto object
-#' my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords, 
-#' name = "selections")
+#' my_giotto_polygons <- createGiottoPolygon(
+#'     my_polygon_coords,
+#'     name = "selections"
+#' )
+#'
 #' g <- addGiottoPolygons(
 #'     gobject = g,
 #'     gpolygons = list(my_giotto_polygons)
 #' )
 #'
 #' ## Add polygon IDs to cell metadata
-#' addPolygonCells(g)
+#' g <- addPolygonCells(g)
+#' pDataDT(g)
 #' @export
-addPolygonCells <- function(gobject,
-    polygon_name = "selections",
-    spat_unit = "cell",
-    spat_loc_name = "raw",
-    feat_type = "rna",
-    polygons = NULL,
-    na.label = "no_polygon") {
+addPolygonCells <- function(
+        gobject,
+        polygon_name = "selections",
+        spat_unit = "cell",
+        spat_loc_name = "raw",
+        feat_type = "rna",
+        polygons = NULL,
+        na.label = "no_polygon") {
     ## verify gobject
     if (!inherits(gobject, "giotto")) {
         stop("gobject needs to be a giotto object")
     }
 
     ## get cells within each polygon
-    polygon_cells <- data.table::as.data.table(getCellsFromPolygon(
+    polygon_cells <- getCellsFromPolygon(
         gobject = gobject,
         polygon_name = polygon_name,
         spat_unit = spat_unit,
         spat_loc_name = spat_loc_name,
         polygons = polygons
-    ))
+    )
+    polygon_cells <- data.table::as.data.table(polygon_cells)
     data.table::setnames(polygon_cells, old = "poly_ID", new = polygon_name)
 
     ## get original cell metadata
@@ -282,7 +308,8 @@ addPolygonCells <- function(gobject,
     ## assign a default ID to cells outside of polygons
     selection_values <- new_cell_metadata[[polygon_name]]
     selection_values <- ifelse(
-        is.na(selection_values), na.label, selection_values)
+        is.na(selection_values), na.label, selection_values
+    )
     new_cell_metadata[, c(polygon_name) := selection_values]
 
     ## keep original order of cells
@@ -309,9 +336,9 @@ addPolygonCells <- function(gobject,
 #' @param spat_unit spatial unit (e.g. "cell")
 #' @param feat_type feature type (e.g. "rna", "dna", "protein")
 #' @param selected_feats vector of selected features to plot
-#' @param expression_values gene expression values to use 
+#' @param expression_values gene expression values to use
 #' ("normalized", "scaled", "custom")
-#' @param method method to use to detect differentially expressed feats 
+#' @param method method to use to detect differentially expressed feats
 #' ("scran", "gini", "mast")
 #' @param \dots Arguments passed to \link[ComplexHeatmap]{Heatmap}
 #'
@@ -319,12 +346,15 @@ addPolygonCells <- function(gobject,
 #' @examples
 #' ## Plot interactive polygons
 #' g <- GiottoData::loadGiottoMini("visium")
-#' my_polygon_coords <- data.frame(poly_ID = rep("polygon1", 3), 
-#' sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202))
+#' my_polygon_coords <- data.frame(
+#'     poly_ID = rep("polygon1", 3),
+#'     sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202)
+#' )
 #'
 #' ## Add polygon coordinates to Giotto object
-#' my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords, 
-#' name = "selections")
+#' my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords,
+#'     name = "selections"
+#' )
 #' g <- addGiottoPolygons(
 #'     gobject = g,
 #'     gpolygons = list(my_giotto_polygons)
@@ -332,17 +362,18 @@ addPolygonCells <- function(gobject,
 #'
 #' ## Add polygon cells
 #' g <- addPolygonCells(g)
-#' 
+#'
 #' comparePolygonExpression(g)
 #' @export
-comparePolygonExpression <- function(gobject,
-    polygon_name = "selections",
-    spat_unit = "cell",
-    feat_type = "rna",
-    selected_feats = "top_genes",
-    expression_values = "normalized",
-    method = "scran",
-    ...) {
+comparePolygonExpression <- function(
+        gobject,
+        polygon_name = "selections",
+        spat_unit = "cell",
+        feat_type = "rna",
+        selected_feats = "top_genes",
+        expression_values = "normalized",
+        method = "scran",
+        ...) {
     # verify gobject
     if (!inherits(gobject, "giotto")) {
         stop("gobject needs to be a giotto object")
@@ -394,7 +425,7 @@ comparePolygonExpression <- function(gobject,
         sd_expression_gene <- stats::sd(my_expression[gene, ])
         for (cell in colnames(my_expression)) {
             my_zscores[gene, cell] <- (
-                my_expression[gene, cell] - mean_expression_gene) / 
+                my_expression[gene, cell] - mean_expression_gene) /
                 sd_expression_gene
         }
     }
@@ -437,12 +468,15 @@ comparePolygonExpression <- function(gobject,
 #' @examples
 #' ## Plot interactive polygons
 #' g <- GiottoData::loadGiottoMini("visium")
-#' my_polygon_coords <- data.frame(poly_ID = rep("polygon1", 3), 
-#' sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202))
+#' my_polygon_coords <- data.frame(
+#'     poly_ID = rep("polygon1", 3),
+#'     sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202)
+#' )
 #'
 #' ## Add polygon coordinates to Giotto object
-#' my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords, 
-#' name = "selections")
+#' my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords,
+#'     name = "selections"
+#' )
 #' g <- addGiottoPolygons(
 #'     gobject = g,
 #'     gpolygons = list(my_giotto_polygons)
@@ -450,15 +484,16 @@ comparePolygonExpression <- function(gobject,
 #'
 #' ## Add polygon cells
 #' g <- addPolygonCells(g)
-#' 
+#'
 #' compareCellAbundance(g)
 #' @export
-compareCellAbundance <- function(gobject,
-    polygon_name = "selections",
-    spat_unit = "cell",
-    feat_type = "rna",
-    cell_type_column = "leiden_clus",
-    ...) {
+compareCellAbundance <- function(
+        gobject,
+        polygon_name = "selections",
+        spat_unit = "cell",
+        feat_type = "rna",
+        cell_type_column = "leiden_clus",
+        ...) {
     # verify gobject
     if (!inherits(gobject, "giotto")) {
         stop("gobject needs to be a giotto object")
@@ -503,21 +538,24 @@ compareCellAbundance <- function(gobject,
 #' @param polygon_name name of polygon selections
 #' @param x A ggplot2, spatPlot or terra::rast object
 #' @param spat_unit spatial unit
-#' @param polygons character. Vector of polygon names to plot. If NULL, all 
+#' @param polygons character. Vector of polygon names to plot. If NULL, all
 #' polygons are plotted
-#' @param ... Additional parameters passed to ggplot2::geom_polygon() or 
+#' @param ... Additional parameters passed to ggplot2::geom_polygon() or
 #' graphics::polygon
 #'
 #' @returns A ggplot2 image
 #' @examples
 #' ## Plot interactive polygons
 #' g <- GiottoData::loadGiottoMini("visium")
-#' my_polygon_coords <- data.frame(poly_ID = rep("polygon1", 3), 
-#' sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202))
+#' my_polygon_coords <- data.frame(
+#'     poly_ID = rep("polygon1", 3),
+#'     sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202)
+#' )
 #'
 #' ## Add polygon coordinates to Giotto object
-#' my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords, 
-#' name = "selections")
+#' my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords,
+#'     name = "selections"
+#' )
 #' g <- addGiottoPolygons(
 #'     gobject = g,
 #'     gpolygons = list(my_giotto_polygons)
@@ -525,18 +563,19 @@ compareCellAbundance <- function(gobject,
 #'
 #' ## Add polygon cells
 #' g <- addPolygonCells(g)
-#' 
+#'
 #' ## Create spatplot
 #' x <- spatPlot2D(g, return_plot = TRUE)
-#' 
+#'
 #' plotPolygons(g, x = x)
 #' @export
-plotPolygons <- function(gobject,
-    polygon_name = "selections",
-    x,
-    spat_unit = "cell",
-    polygons = NULL,
-    ...) {
+plotPolygons <- function(
+        gobject,
+        polygon_name = "selections",
+        x,
+        spat_unit = "cell",
+        polygons = NULL,
+        ...) {
     ## verify gobject
     if (!inherits(gobject, "giotto")) {
         stop("gobject must be a Giotto object")
@@ -594,21 +633,22 @@ plotPolygons <- function(gobject,
 #' @param gobject giotto object
 #' @param spat_unit spatial unit (e.g. "cell")
 #' @param feat_type feature type (e.g. "rna", "dna", "protein")
-#' @param cell_color character. What to color cells by 
+#' @param cell_color character. What to color cells by
 #' (e.g. metadata col or spatial enrichment col)
-#' @param cell_color_code character. discrete colors to use. Palette to use or 
+#' @param cell_color_code character. discrete colors to use. Palette to use or
 #' named vector of colors
 #' @param point_size size of point (cell)
 #' @param width plot width
 #' @param height plot height
 #'
-#' @returns data.table with selected cell_IDs, spatial coordinates, and 
+#' @returns data.table with selected cell_IDs, spatial coordinates, and
 #' cluster_ID.
 #' @export
-plotInteractive3D <- function(gobject, spat_unit = "cell", feat_type = "rna",
-    cell_color = "leiden_clus",
-    cell_color_code = NULL, point_size = 0.5,
-    width = "100%", height = "400px") {
+plotInteractive3D <- function(
+        gobject, spat_unit = "cell", feat_type = "rna",
+        cell_color = "leiden_clus",
+        cell_color_code = NULL, point_size = 0.5,
+        width = "100%", height = "400px") {
     # NSE vars
     sdimx <- sdimy <- sdimz <- cell_ID <- NULL
 
@@ -675,8 +715,9 @@ plotInteractive3D <- function(gobject, spat_unit = "cell", feat_type = "rna",
             data[data[[cell_color]] %in% input$clusters, ] %>%
                 plotly::filter(
                     sdimx >= input$xrange[1] & sdimx <= input$xrange[2] &
-                    sdimy >= input$yrange[1] & sdimy <= input$yrange[2] &
-                    sdimz >= input$zrange[1] & sdimz <= input$zrange[2]) %>%
+                        sdimy >= input$yrange[1] & sdimy <= input$yrange[2] &
+                        sdimz >= input$zrange[1] & sdimz <= input$zrange[2]
+                ) %>%
                 plotly::select(cell_ID, sdimx, sdimy, sdimz, cell_color)
         })
 
diff --git a/R/kriging.R b/R/kriging.R
index 681419cd6..ff3da4505 100644
--- a/R/kriging.R
+++ b/R/kriging.R
@@ -43,19 +43,22 @@ NULL
 #' @export
 setMethod(
     "interpolateFeature", signature(x = "giotto", y = "missing"),
-    function(x,
-    spat_unit = NULL,
-    feat_type = NULL,
-    feats,
-    spatvalues_params = list(),
-    spat_loc_name = "raw",
-    ext = NULL,
-    buffer = 50,
-    name_fmt = "%s",
-    savedir = file.path(getwd(), "interp_rasters"),
-    overwrite = FALSE,
-    verbose = NULL,
-    ...) {
+    function(
+        x,
+        spat_unit = NULL,
+        feat_type = NULL,
+        feats,
+        spatvalues_params = list(),
+        spat_loc_name = "raw",
+        ext = NULL,
+        buffer = 50,
+        name_fmt = "%s",
+        savedir = file.path(getwd(), "interp_rasters"),
+        overwrite = FALSE,
+        verbose = NULL,
+        ...) {
+        sl <- NULL
+
         # This method prepares the data from the giotto object to pass
         # downstream where the actual interpolation happens
 
@@ -130,9 +133,6 @@ setMethod(
 #' @rdname interpolateFeature
 #' @param rastersize numeric. Length of major axis in px of interpolation
 #' raster to create.
-#' @param name name of interpolation `giottoLargeImage` to generate
-#' @param filename character. Output filename. Default is \[`name`\].tif within
-#' the working directory.
 #' @param overwrite logical. Whether raster outputs should be overwritten if
 #' the same `filename` is provided.
 #' @details
@@ -142,15 +142,16 @@ setMethod(
 setMethod(
     "interpolateFeature",
     signature(x = "spatLocsObj", y = "data.frame"),
-    function(x, y,
-    ext = NULL,
-    buffer = 50,
-    rastersize = 500,
-    name_fmt = "%s",
-    savedir = file.path(getwd(), "interp_rasters"),
-    overwrite = FALSE,
-    # cores = GiottoUtils::determine_cores(),
-    ...) {
+    function(
+        x, y,
+        ext = NULL,
+        buffer = 50,
+        rastersize = 500,
+        name_fmt = "%s",
+        savedir = file.path(getwd(), "interp_rasters"),
+        overwrite = FALSE,
+        # cores = GiottoUtils::determine_cores(),
+        ...) {
         checkmate::assert_character(savedir)
         checkmate::assert_character(name_fmt)
         checkmate::assert_logical(overwrite)
diff --git a/R/poly_influence.R b/R/poly_influence.R
index 18fefd9b8..cf5a2a031 100644
--- a/R/poly_influence.R
+++ b/R/poly_influence.R
@@ -2,10 +2,10 @@
 #' @name showPolygonSizeInfluence
 #' @param gobject giotto object
 #' @param spat_unit spatial unit
-#' @param alt_spat_unit alternaitve spatial unit which represents resized 
+#' @param alt_spat_unit alternaitve spatial unit which represents resized
 #' polygon data
 #' @param feat_type feature type
-#' @param clus_name name of cluster column in cell_metadata for given spat_unit 
+#' @param clus_name name of cluster column in cell_metadata for given spat_unit
 #' and alt_spat_unit, i.e. "kmeans"
 #' @param return_plot logical. whether to return the plot object
 #' @param verbose be verbose
@@ -16,29 +16,31 @@
 #' New columns, resize_switch and cluster_interaction, will be created within
 #' cell_metadata for spat_unit-feat_type.
 #'
-#' These new columns will describe if a given cell switched cluster number when 
+#' These new columns will describe if a given cell switched cluster number when
 #' resized.
 #' If the same amount of clusters exist for spat_unit-feat_type and
 #' alt_spat_unit-feat_type, then clusters are determined to be
 #' corresponding based on % overlap in cell_IDs in each cluster.
 #'
-#' Otherwise, multiple clusters from the spatial unit feature type pair are 
+#' Otherwise, multiple clusters from the spatial unit feature type pair are
 #' condensed to align with the smaller number of clusters and ensure overlap.
 #'
 #' @export
-showPolygonSizeInfluence <- function(gobject = NULL,
-    spat_unit = NULL,
-    alt_spat_unit = NULL,
-    feat_type = NULL,
-    clus_name = "kmeans",
-    return_plot = FALSE,
-    verbose = FALSE) {
+showPolygonSizeInfluence <- function(
+        gobject = NULL,
+        spat_unit = NULL,
+        alt_spat_unit = NULL,
+        feat_type = NULL,
+        clus_name = "kmeans",
+        return_plot = FALSE,
+        verbose = FALSE) {
     # NSE vars
     cell_ID <- total_expr <- cluster_interactions <- N <- resize_switch <- NULL
 
     # Guards
-    if (!c("giotto") %in% class(gobject)) 
+    if (!c("giotto") %in% class(gobject)) {
         stop(wrap_txt("Please provide a valid Giotto Object.", errWidth = TRUE))
+    }
 
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -52,28 +54,35 @@ showPolygonSizeInfluence <- function(gobject = NULL,
 
     if (!alt_spat_unit %in% names(gobject@expression)) {
         stop(wrap_txt(paste0(
-            "Alternative spatial unit ", alt_spat_unit, 
-            " not found. Please ensure it exists."), errWidth = TRUE))
+            "Alternative spatial unit ", alt_spat_unit,
+            " not found. Please ensure it exists."
+        ), errWidth = TRUE))
     }
 
     meta_cols <- names(getCellMetadata(
-        gobject, 
-        spat_unit = spat_unit, 
-        feat_type = feat_type, 
-        output = "data.table"))
+        gobject,
+        spat_unit = spat_unit,
+        feat_type = feat_type,
+        output = "data.table"
+    ))
 
     if (!clus_name %in% meta_cols) {
-        stop(wrap_txt(paste0(
-            "Cluster name ", clus_name, 
-            " not found within cell metadata. Please ensure it exists."), 
-            errWidth = TRUE))
+        stop(wrap_txt(
+            paste0(
+                "Cluster name ", clus_name,
+                " not found within cell metadata. Please ensure it exists."
+            ),
+            errWidth = TRUE
+        ))
     }
 
 
     if (c("cluster_interactions") %in% meta_cols) {
-        warning((wrap_txt(paste0("Switch interactions already found within 
-        cell_metadata for spat_unit feat_type pair:`", spat_unit, "-", 
-        feat_type, "`. They will be overwritten."), errWidth = TRUE)))
+        warning((wrap_txt(paste0(
+            "Switch interactions already found within
+        cell_metadata for spat_unit feat_type pair:`", spat_unit, "-",
+            feat_type, "`. They will be overwritten."
+        ), errWidth = TRUE)))
     }
     ## Compare clustering results between cell and smallcell data #######
     # ----------------------------------------------------------------- #
@@ -86,7 +95,8 @@ showPolygonSizeInfluence <- function(gobject = NULL,
     cell_meta <- merge.data.table(cell_meta, new_clus_table, by = "cell_ID")
 
     cell_meta[, cluster_interactions := paste0(cell_meta[[
-        paste0(clus_name, ".x")]], "-", cell_meta[[paste0(clus_name, ".y")]])]
+        paste0(clus_name, ".x")
+    ]], "-", cell_meta[[paste0(clus_name, ".y")]])]
     switches2 <- cell_meta[, .N, by = "cluster_interactions"]
     setorder(switches2, N)
 
@@ -116,13 +126,15 @@ showPolygonSizeInfluence <- function(gobject = NULL,
     }
 
     cell_meta[, resize_switch := ifelse(
-        cluster_interactions %in% switch_strs, "same", "switch")]
+        cluster_interactions %in% switch_strs, "same", "switch"
+    )]
     gobject <- addCellMetadata(
         gobject = gobject,
         spat_unit = spat_unit,
         feat_type = feat_type,
         new_metadata = cell_meta[
-            , .(cell_ID, resize_switch, cluster_interactions)],
+            , .(cell_ID, resize_switch, cluster_interactions)
+        ],
         by_column = TRUE,
         column_cell_ID = "cell_ID"
     )
@@ -144,11 +156,13 @@ showPolygonSizeInfluence <- function(gobject = NULL,
     )
 
     num_cells_switched <- sum(
-        getCellMetadata(gobject)$resize_switch == "switch")
+        getCellMetadata(gobject)$resize_switch == "switch"
+    )
     num_cells_same <- sum(getCellMetadata(gobject)$resize_switch == "same")
     if (verbose) print(paste0(num_cells_switched, " cells switched clusters."))
-    if (verbose) 
+    if (verbose) {
         print(paste0(num_cells_same, " cells remained in the same cluster."))
+    }
 
     if (return_plot) {
         return(poly_plot)
@@ -169,22 +183,23 @@ showPolygonSizeInfluence <- function(gobject = NULL,
 #'    y_m is a cluster number from the resized spatial unit
 #'    n is the number of clusters
 #'
-#' Clusters are determined to be corresponding based on % overlap in cell_IDs 
+#' Clusters are determined to be corresponding based on % overlap in cell_IDs
 #' in each cluster.
 #'
 #' @keywords internal
-.determine_switch_string_equal <- function(cell_meta = NULL,
-    cell_meta_new = NULL,
-    clus_name = NULL) {
+.determine_switch_string_equal <- function(
+        cell_meta = NULL,
+        cell_meta_new = NULL,
+        clus_name = NULL) {
     k_clusters <- sort(unique(cell_meta[[clus_name]]))
     num_clusters <- k_clusters[length(k_clusters)]
 
-    k_match_clusters <- 1:num_clusters
+    k_match_clusters <- seq_len(num_clusters)
     switch_strs <- c()
-    for (i in 1:num_clusters) {
+    for (i in seq_len(num_clusters)) {
         thresh <- 0
         clus_match <- NULL
-        for (j in 1:num_clusters) {
+        for (j in seq_len(num_clusters)) {
             c_df <- cell_meta[cell_meta[[clus_name]] == i]$cell_ID
             nc_df <- cell_meta_new[cell_meta_new[[clus_name]] == j]$cell_ID
 
@@ -198,7 +213,7 @@ showPolygonSizeInfluence <- function(gobject = NULL,
         k_match_clusters[i] <- clus_match
     }
 
-    for (idx in 1:num_clusters) {
+    for (idx in seq_len(num_clusters)) {
         p1 <- k_clusters[[idx]]
         p2 <- k_match_clusters[[idx]]
         switch_strs <- c(switch_strs, paste0(p1, "-", p2))
@@ -212,7 +227,7 @@ showPolygonSizeInfluence <- function(gobject = NULL,
 #' @param num_orig sorted vector of cluster numbers in the original metadata
 #' @param num_new sorted vector of cluster numbers in the new, resized metadata
 #' @returns switch_str, a vector of corresponding cluster numbers in strings
-#' @details determines how to create a string in the format 
+#' @details determines how to create a string in the format
 #' c("x_1-y_1", "x_2-y_2"..."x_n, y_m")
 #' Where:
 #'    x_n is a cluster number from the original spatial unit
@@ -223,8 +238,9 @@ showPolygonSizeInfluence <- function(gobject = NULL,
 #' Essentially determines iteration order for .create_switch_string_unequal()
 #'
 #' @keywords internal
-.determine_switch_string_unequal <- function(num_orig = NULL,
-    num_new = NULL) {
+.determine_switch_string_unequal <- function(
+        num_orig = NULL,
+        num_new = NULL) {
     switch_strs <- c()
 
     orig_first <- TRUE
@@ -261,15 +277,22 @@ showPolygonSizeInfluence <- function(gobject = NULL,
 #'    n is the number of clusters in the original spatial unit
 #'    m is the number of clusters in the new spatial unit
 #' @keywords internal
-.create_switch_string_unequal <- function(num_first = NULL,
-    num_second = NULL,
-    switch_strs = NULL) {
+.create_switch_string_unequal <- function(
+        num_first = NULL,
+        num_second = NULL,
+        switch_strs = NULL) {
     for (o in num_first) {
         for (n in num_second) {
-            if (as.integer(o) == as.integer(n)) switch_strs <- c(
-                switch_strs, paste0(as.character(o), "-", as.character(n)))
-            if (o > n && n == num_second[length(num_second)]) switch_strs <- c(
-                switch_strs, paste0(as.character(o), "-", as.character(n)))
+            if (as.integer(o) == as.integer(n)) {
+                switch_strs <- c(
+                    switch_strs, paste0(as.character(o), "-", as.character(n))
+                )
+            }
+            if (o > n && n == num_second[length(num_second)]) {
+                switch_strs <- c(
+                    switch_strs, paste0(as.character(o), "-", as.character(n))
+                )
+            }
         }
     }
 
@@ -284,20 +307,22 @@ showPolygonSizeInfluence <- function(gobject = NULL,
 #' @param spat_unit spatial unit
 #' @param feat_type feature type
 #' @returns ggplot
-#' @details Creates a pie chart showing how many cells switched clusters after 
+#' @details Creates a pie chart showing how many cells switched clusters after
 #' annotation resizing.
-#' The function showPolygonSizeInfluence() must have been run on the Giotto 
+#' The function showPolygonSizeInfluence() must have been run on the Giotto
 #' Object for this function to run.
 #' @export
-showCellProportionSwitchedPie <- function(gobject = NULL,
-    spat_unit = NULL,
-    feat_type = NULL) {
+showCellProportionSwitchedPie <- function(
+        gobject = NULL,
+        spat_unit = NULL,
+        feat_type = NULL) {
     # NSE vars
     cluster_status <- num_cells <- resize_switch <- perc <- ypos <- NULL
 
     # Guards
-    if (!"giotto" %in% class(gobject)) 
+    if (!"giotto" %in% class(gobject)) {
         stop(wrap_txt("Please provide a valid Giotto Object.", errWidth = TRUE))
+    }
 
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -319,14 +344,15 @@ showCellProportionSwitchedPie <- function(gobject = NULL,
     )
 
     if (!c("resize_switch") %in% names(cmeta)) {
-        stop(wrap_txt("Column 'resize_switch' not found in cell metadata. 
+        stop(wrap_txt("Column 'resize_switch' not found in cell metadata.
             Ensure showPolygonSizeInfluence() has been run.", errWidth = TRUE))
     }
 
     plotdf <- data.table::data.table()
     plotdf[, cluster_status := c("switch", "same")]
     plotdf[, num_cells := c(sum(cmeta[
-        , resize_switch == "switch"]), sum(cmeta[, resize_switch == "same"]))]
+        , resize_switch == "switch"
+    ]), sum(cmeta[, resize_switch == "same"]))]
 
     per_switch <- plotdf$num_cells[[1]] / sum(plotdf$num_cells) * 100
     per_same <- plotdf$num_cells[[2]] / sum(plotdf$num_cells) * 100
@@ -341,7 +367,8 @@ showCellProportionSwitchedPie <- function(gobject = NULL,
     print(plotdf)
 
     ggplot(
-        as.data.frame(plotdf), aes(x = "", y = perc, fill = cluster_status)) +
+        as.data.frame(plotdf), aes(x = "", y = perc, fill = cluster_status)
+    ) +
         coord_polar("y", start = 0) +
         geom_bar(stat = "identity", width = 1) +
         theme_void() +
@@ -350,26 +377,28 @@ showCellProportionSwitchedPie <- function(gobject = NULL,
 
 #' @title showCellProportionSwitchedSanKey
 #' @name showCellProportionSwitchedSanKey
-#' @param gobject giotto object which contains metadata for both spat_unit and 
+#' @param gobject giotto object which contains metadata for both spat_unit and
 #' alt_spat_unit
 #' @param spat_unit spatial unit
-#' @param alt_spat_unit alternative spatial unit which stores data after 
+#' @param alt_spat_unit alternative spatial unit which stores data after
 #' resizing annotations
 #' @param feat_type feature type
 #' @returns D3 JavaScript Sankey diagram
 #' @details Creates a Sankey Diagram to illustrate cluster switching behavior.
 #' Currently only supports displaying cluster switching for kmeans clusters.
 #' @export
-showCellProportionSwitchedSanKey <- function(gobject = NULL,
-    spat_unit = NULL,
-    alt_spat_unit = NULL,
-    feat_type = NULL) {
+showCellProportionSwitchedSanKey <- function(
+        gobject = NULL,
+        spat_unit = NULL,
+        alt_spat_unit = NULL,
+        feat_type = NULL) {
     # NSE vars
     kmeans_small <- cell_ID <- NULL
 
     # Guards
-    if (!"giotto" %in% class(gobject)) 
+    if (!"giotto" %in% class(gobject)) {
         stop(wrap_txt("Please provide a valid Giotto Object.", errWidth = TRUE))
+    }
 
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -382,8 +411,9 @@ showCellProportionSwitchedSanKey <- function(gobject = NULL,
     )
     if (!alt_spat_unit %in% names(gobject@expression)) {
         stop(wrap_txt(paste0(
-            "Alternative spatial unit ", alt_spat_unit, 
-            " not found. Please ensure it exists."), errWidth = TRUE))
+            "Alternative spatial unit ", alt_spat_unit,
+            " not found. Please ensure it exists."
+        ), errWidth = TRUE))
     }
 
     package_check("networkD3")
@@ -397,7 +427,7 @@ showCellProportionSwitchedSanKey <- function(gobject = NULL,
     )
 
     if (!c("resize_switch") %in% names(cmeta)) {
-        stop(wrap_txt("Column 'resize_switch' not found in cell metadata. 
+        stop(wrap_txt("Column 'resize_switch' not found in cell metadata.
             Ensure showPolygonSizeInfluence() has been run.", errWidth = TRUE))
     }
 
@@ -422,7 +452,9 @@ showCellProportionSwitchedSanKey <- function(gobject = NULL,
     small_cmeta_clus$kmeans <- NULL
 
     merged_cmeta <- data.table::merge.data.table(
-        cmeta, small_cmeta_clus, by.x = "cell_ID", by.y = "cell_ID")
+        cmeta, small_cmeta_clus,
+        by.x = "cell_ID", by.y = "cell_ID"
+    )
 
 
     k1 <- unique(merged_cmeta$kmeans)
@@ -436,7 +468,7 @@ showCellProportionSwitchedSanKey <- function(gobject = NULL,
     idx1 <- 1
     idx2 <- 1
 
-    for (i in 1:flen) {
+    for (i in seq_len(flen)) {
         c_k1[i] <- k1[idx1] - 1 # java zero-index
         c_k2[i] <- k2[idx2] - 1 # java zero-index
 
@@ -447,9 +479,10 @@ showCellProportionSwitchedSanKey <- function(gobject = NULL,
 
     num_occ <- c()
 
-    for (i in 1:flen) {
+    for (i in seq_len(flen)) {
         num_occ[i] <- dim(na.omit(merged_cmeta[kmeans == (c_k1[i] + 1)][
-            merged_cmeta[kmeans_small == (c_k2[i] + 1)]]))[[1]]
+            merged_cmeta[kmeans_small == (c_k2[i] + 1)]
+        ]))[[1]]
     }
 
     fdt[, "k1"] <- c_k1
@@ -459,7 +492,8 @@ showCellProportionSwitchedSanKey <- function(gobject = NULL,
 
     label_dt <- data.table::data.table()
     label_dt[, "name"] <- c(paste0("original_", as.character(sort(k1))), paste0(
-        "resized_", as.character(sort(k2))))
+        "resized_", as.character(sort(k2))
+    ))
     label_dt
 
     master <- list(fdt, label_dt)
diff --git a/R/python_hmrf.R b/R/python_hmrf.R
index 49f09175b..76bb4a503 100644
--- a/R/python_hmrf.R
+++ b/R/python_hmrf.R
@@ -8,16 +8,16 @@
 #' @param spatial_network_name name of spatial network to use for HMRF
 #' @param spat_loc_name name of spatial locations
 #' @param spatial_genes spatial genes to use for HMRF
-#' @param spatial_dimensions select spatial dimensions to use, default is all 
+#' @param spatial_dimensions select spatial dimensions to use, default is all
 #' possible dimensions
 #' @param dim_reduction_to_use use another dimension reduction set as input
 #' @param dim_reduction_name name of dimension reduction set to use
 #' @param dimensions_to_use number of dimensions to use as input
 #' @param name name of HMRF run
 #' @param k  number of HMRF domains
-#' @param seed seed to fix random number generator 
+#' @param seed seed to fix random number generator
 #' (for creating initialization of HMRF) (-1 if no fixing)
-#' @param betas betas to test for. three numbers: start_beta, beta_increment, 
+#' @param betas betas to test for. three numbers: start_beta, beta_increment,
 #' num_betas e.g. c(0, 2.0, 50)
 #' @param tolerance tolerance
 #' @param zscore zscore
@@ -25,15 +25,17 @@
 #' @param python_path python path to use
 #' @param output_folder output folder to save results
 #' @param overwrite_output overwrite output folder
-#' @returns Creates a directory with results that can be viewed with 
+#' @returns Creates a directory with results that can be viewed with
 #' viewHMRFresults
 #' @details Description of HMRF parameters ...
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #' spat_genes <- binSpect(g)
-#' 
-#' doHMRF(g, spatial_genes = spat_genes[seq_len(10)]$feats, 
-#' output_folder = tempdir())
+#'
+#' doHMRF(g,
+#'     spatial_genes = spat_genes[seq_len(10)]$feats,
+#'     output_folder = tempdir()
+#' )
 #' @export
 doHMRF <- function(gobject,
     spat_unit = NULL,
@@ -56,15 +58,8 @@ doHMRF <- function(gobject,
     python_path = NULL,
     output_folder = NULL,
     overwrite_output = TRUE) {
-    if (!requireNamespace("smfishHmrf", quietly = TRUE)) {
-        stop("package ", "smfishHmrf", " is not yet installed \n",
-        "To install: \n",
-        "remotes::install_bitbucket(repo = 'qzhudfci/smfishhmrf-r', ref='master')",
-        "see http://spatial.rc.fas.harvard.edu/install.html for more information",
-        call. = FALSE
-        )
-    }
 
+    package_check("smfishHmrf", repository = "pip")
 
     # data.table set global variable
     to <- from <- NULL
@@ -95,7 +90,8 @@ doHMRF <- function(gobject,
         output_folder <- paste0(getwd(), "/", "HMRF_output")
         if (!file.exists(output_folder)) {
             dir.create(
-                path = paste0(getwd(), "/", "HMRF_output"), recursive = TRUE)
+                path = paste0(getwd(), "/", "HMRF_output"), recursive = TRUE
+            )
         }
     }
     # folder path specified
@@ -108,12 +104,11 @@ doHMRF <- function(gobject,
 
 
     ## first write necessary txt files to output folder ##
-    # cell location / spatial network / expression data and selected spatial 
+    # cell location / spatial network / expression data and selected spatial
     # genes
 
     ## 1. expression values
     if (!is.null(dim_reduction_to_use)) {
-
         expr_values <- getDimReduction(
             gobject = gobject,
             spat_unit = spat_unit,
@@ -128,7 +123,8 @@ doHMRF <- function(gobject,
     } else {
         values <- match.arg(
             expression_values,
-            unique(c("normalized", "scaled", "custom", expression_values)))
+            unique(c("normalized", "scaled", "custom", expression_values))
+        )
         expr_values <- getExpression(
             gobject = gobject,
             spat_unit = spat_unit,
@@ -139,7 +135,7 @@ doHMRF <- function(gobject,
     }
 
     if (!"matrix" %in% class(expr_values)) {
-        warning("this matrix will be converted to a dense and memory intensive 
+        warning("this matrix will be converted to a dense and memory intensive
                 base matrix ...")
         expr_values <- as.matrix(expr_values)
     }
@@ -149,21 +145,22 @@ doHMRF <- function(gobject,
 
     # overwrite if exists
     if (file.exists(expression_file) & overwrite_output == TRUE) {
-        message("expression_matrix.txt already exists at this location, will be 
+        message("expression_matrix.txt already exists at this location, will be
                 overwritten")
         data.table::fwrite(
-            data.table::as.data.table(expr_values, keep.rownames = "gene"), 
-            file = expression_file, quote = FALSE, col.names = TRUE, 
-            row.names = FALSE, sep = " ")
-
+            data.table::as.data.table(expr_values, keep.rownames = "gene"),
+            file = expression_file, quote = FALSE, col.names = TRUE,
+            row.names = FALSE, sep = " "
+        )
     } else if (file.exists(expression_file) & overwrite_output == FALSE) {
         message("expression_matrix.txt already exists at this location, will be
                 used again")
     } else {
         data.table::fwrite(
-            data.table::as.data.table(expr_values, keep.rownames = "gene"), 
-            file = expression_file, quote = FALSE, col.names = TRUE, 
-            row.names = FALSE, sep = " ")
+            data.table::as.data.table(expr_values, keep.rownames = "gene"),
+            file = expression_file, quote = FALSE, col.names = TRUE,
+            row.names = FALSE, sep = " "
+        )
     }
 
 
@@ -176,26 +173,28 @@ doHMRF <- function(gobject,
         dimred_rownames <- rownames(expr_values)
         spatial_genes_detected <- dimred_rownames[dimensions_to_use]
         spatial_genes_detected <- spatial_genes_detected[
-            !is.na(spatial_genes_detected)]
+            !is.na(spatial_genes_detected)
+        ]
     } else {
         if (is.null(spatial_genes)) {
             stop("you need to provide a vector of spatial genes (~500)")
         }
         spatial_genes_detected <- spatial_genes[
-            spatial_genes %in% rownames(expr_values)]
+            spatial_genes %in% rownames(expr_values)
+        ]
     }
     spatial_genes_file <- paste0(output_folder, "/", "spatial_genes.txt")
 
     # overwrite if exists
     if (file.exists(spatial_genes_file) & overwrite_output == TRUE) {
-        message("spatial_genes.txt already exists at this location, will be 
+        message("spatial_genes.txt already exists at this location, will be
                 overwritten")
         write.table(spatial_genes_detected,
             file = spatial_genes_file,
             quote = FALSE, col.names = FALSE, row.names = FALSE
         )
     } else if (file.exists(spatial_genes_file) & overwrite_output == FALSE) {
-        message("spatial_genes.txt already exists at this location, will be 
+        message("spatial_genes.txt already exists at this location, will be
                 used again")
     } else {
         write.table(spatial_genes_detected,
@@ -218,14 +217,14 @@ doHMRF <- function(gobject,
     spatial_network_file <- paste0(output_folder, "/", "spatial_network.txt")
 
     if (file.exists(spatial_network_file) & overwrite_output == TRUE) {
-        message("spatial_network.txt already exists at this location, will be 
+        message("spatial_network.txt already exists at this location, will be
                 overwritten")
         write.table(spatial_network,
             file = spatial_network_file,
             row.names = FALSE, col.names = FALSE, quote = FALSE, sep = "\t"
         )
     } else if (file.exists(spatial_network_file) & overwrite_output == FALSE) {
-        message("spatial_network.txt already exists at this location, will be 
+        message("spatial_network.txt already exists at this location, will be
                 used again")
     } else {
         write.table(spatial_network,
@@ -248,21 +247,25 @@ doHMRF <- function(gobject,
 
     # select spatial dimensions that are available #
     spatial_dimensions <- spatial_dimensions[
-        spatial_dimensions %in% colnames(spatial_location)]
+        spatial_dimensions %in% colnames(spatial_location)
+    ]
     spatial_location <- spatial_location[
-        , c(spatial_dimensions, "cell_ID"), with = FALSE]
+        , c(spatial_dimensions, "cell_ID"),
+        with = FALSE
+    ]
     spatial_location_file <- paste0(
-        output_folder, "/", "spatial_cell_locations.txt")
+        output_folder, "/", "spatial_cell_locations.txt"
+    )
 
     if (file.exists(spatial_location_file) & overwrite_output == TRUE) {
-        message("spatial_cell_locations.txt already exists at this location, 
+        message("spatial_cell_locations.txt already exists at this location,
                 will be overwritten")
         write.table(spatial_location,
             file = spatial_location_file,
             row.names = FALSE, col.names = FALSE, quote = FALSE, sep = "\t"
         )
     } else if (file.exists(spatial_location_file)) {
-        message("spatial_cell_locations.txt already exists at this location, 
+        message("spatial_cell_locations.txt already exists at this location,
                 will be used again")
     } else {
         write.table(spatial_location,
@@ -348,18 +351,23 @@ doHMRF <- function(gobject,
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #' x <- tempdir()
-#' doHMRF(g, spatial_genes = c("Gna12", "Ccnd2"), output_folder = x,
-#' betas = c(0, 2, 50))
-#' 
-#' loadHMRF(output_folder_used = x, betas_used = c(0, 2, 50), 
-#' python_path_used = NULL)
-#' 
+#' doHMRF(g,
+#'     spatial_genes = c("Gna12", "Ccnd2"), output_folder = x,
+#'     betas = c(0, 2, 50)
+#' )
+#'
+#' loadHMRF(
+#'     output_folder_used = x, betas_used = c(0, 2, 50),
+#'     python_path_used = NULL
+#' )
+#'
 #' @export
-loadHMRF <- function(name_used = "test",
-    output_folder_used,
-    k_used = 10,
-    betas_used,
-    python_path_used) {
+loadHMRF <- function(
+        name_used = "test",
+        output_folder_used,
+        k_used = 10,
+        betas_used,
+        python_path_used) {
     output_data <- paste0(output_folder_used, "/", "result.spatial.zscore")
     if (!file.exists(output_data)) {
         stop("doHMRF was not run in this output directory")
@@ -395,12 +403,13 @@ loadHMRF <- function(name_used = "test",
 #' @returns spatial plots with HMRF domains
 #' @seealso \code{\link{spatPlot2D}} and \code{\link{spatPlot3D}}
 #' @export
-viewHMRFresults <- function(gobject,
-    HMRFoutput,
-    k = NULL,
-    betas_to_view = NULL,
-    third_dim = FALSE,
-    ...) {
+viewHMRFresults <- function(
+        gobject,
+        HMRFoutput,
+        k = NULL,
+        betas_to_view = NULL,
+        third_dim = FALSE,
+        ...) {
     if (!"HMRFoutput" %in% class(HMRFoutput)) {
         stop("HMRFoutput needs to be output from doHMRFextend")
     }
@@ -408,7 +417,9 @@ viewHMRFresults <- function(gobject,
     ## reader.py and get_result.py paths
     # TODO: part of the package
     get_result_path <- system.file(
-        "python", "get_result2.py", package = "Giotto")
+        "python", "get_result2.py",
+        package = "Giotto"
+    )
 
     # paths and name
     name <- HMRFoutput$name
@@ -423,8 +434,10 @@ viewHMRFresults <- function(gobject,
 
     # betas
     betas <- HMRFoutput$betas
-    possible_betas <- seq(betas[1], to = betas[1] + (betas[2] * (betas[3] - 1)),
-                        by = betas[2])
+    possible_betas <- seq(betas[1],
+        to = betas[1] + (betas[2] * (betas[3] - 1)),
+        by = betas[2]
+    )
 
     betas_to_view_detected <- betas_to_view[betas_to_view %in% possible_betas]
 
@@ -447,18 +460,20 @@ viewHMRFresults <- function(gobject,
         title_name <- paste0("k = ", k, " b = ", b)
 
         spatPlot2D(
-            gobject = gobject, 
-            cell_color = output, 
-            show_plot = TRUE, 
-            title = title_name, 
-            ...)
+            gobject = gobject,
+            cell_color = output,
+            show_plot = TRUE,
+            title = title_name,
+            ...
+        )
 
         if (third_dim == TRUE) {
             spatPlot3D(
-                gobject = gobject, 
-                cell_color = output, 
-                show_plot = TRUE, 
-                ...)
+                gobject = gobject,
+                cell_color = output,
+                show_plot = TRUE,
+                ...
+            )
         }
     }
 }
@@ -475,11 +490,12 @@ viewHMRFresults <- function(gobject,
 #' @param print_command see the python command
 #' @returns data.table with HMRF results for each b and the selected k
 #' @export
-writeHMRFresults <- function(gobject,
-    HMRFoutput,
-    k = NULL,
-    betas_to_view = NULL,
-    print_command = FALSE) {
+writeHMRFresults <- function(
+        gobject,
+        HMRFoutput,
+        k = NULL,
+        betas_to_view = NULL,
+        print_command = FALSE) {
     if (!"HMRFoutput" %in% class(HMRFoutput)) {
         stop("HMRFoutput needs to be output from doHMRFextend")
     }
@@ -487,7 +503,9 @@ writeHMRFresults <- function(gobject,
     ## reader.py and get_result.py paths
     # TODO: part of the package
     get_result_path <- system.file(
-        "python", "get_result2.py", package = "Giotto")
+        "python", "get_result2.py",
+        package = "Giotto"
+    )
 
     # paths and name
     name <- HMRFoutput$name
@@ -502,8 +520,10 @@ writeHMRFresults <- function(gobject,
 
     # betas
     betas <- HMRFoutput$betas
-    possible_betas <- seq(betas[1], to = betas[1] + (betas[2] * (betas[3] - 1)),
-                        by = betas[2])
+    possible_betas <- seq(betas[1],
+        to = betas[1] + (betas[2] * (betas[3] - 1)),
+        by = betas[2]
+    )
 
     betas_to_view_detected <- betas_to_view[betas_to_view %in% possible_betas]
 
@@ -533,7 +553,8 @@ writeHMRFresults <- function(gobject,
 
     result_DT <- data.table::as.data.table(do.call("cbind", result_list))
     result_DT <- cbind(data.table::data.table(
-        "cell_ID" = gobject@cell_ID), result_DT)
+        "cell_ID" = gobject@cell_ID
+    ), result_DT)
     return(result_DT)
 }
 
@@ -551,14 +572,42 @@ writeHMRFresults <- function(gobject,
 #' @param betas_to_add results from different betas that you want to add
 #' @param hmrf_name specify a custom name
 #' @returns giotto object
+#' @examples
+#' g <- GiottoData::loadGiottoMini("visium")
+#' spat_genes <- binSpect(g)
+#'
+#' output_folder <- file.path(tempdir(), "HMRF")
+#' if (!file.exists(output_folder)) dir.create(output_folder, recursive = TRUE)
+#'
+#' out <- doHMRF(
+#'     g,
+#'     spatial_genes = spat_genes[seq_len(20)]$feats,
+#'     expression_values = "scaled",
+#'     spatial_network_name = "Delaunay_network",
+#'     k = 6, betas = c(0, 10, 5),
+#'     output_folder = output_folder
+#' )
+#'
+#' g <- addHMRF(
+#'     gobject = g,
+#'     HMRFoutput = out,
+#'     k = 6,
+#'     betas_to_add = 20,
+#'     hmrf_name = "HMRF"
+#' )
+#'
+#' spatPlot(
+#'     gobject = g, cell_color = "HMRF_k6_b.20",
+#' )
 #' @export
-addHMRF <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    HMRFoutput,
-    k = NULL,
-    betas_to_add = NULL,
-    hmrf_name = NULL) {
+addHMRF <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        HMRFoutput,
+        k = NULL,
+        betas_to_add = NULL,
+        hmrf_name = NULL) {
     if (!"HMRFoutput" %in% class(HMRFoutput)) {
         stop("HMRFoutput needs to be output from doHMRFextend")
     }
@@ -580,7 +629,9 @@ addHMRF <- function(gobject,
     ## reader.py and get_result.py paths
     # TODO: part of the package
     get_result_path <- system.file(
-        "python", "get_result2.py", package = "Giotto")
+        "python", "get_result2.py",
+        package = "Giotto"
+    )
 
     # paths and name
     name <- HMRFoutput$name
@@ -595,8 +646,10 @@ addHMRF <- function(gobject,
 
     # betas
     betas <- HMRFoutput$betas
-    possible_betas <- seq(betas[1], to = betas[1] + (betas[2] * (betas[3] - 1)),
-                        by = betas[2])
+    possible_betas <- seq(betas[1],
+        to = betas[1] + (betas[2] * (betas[3] - 1)),
+        by = betas[2]
+    )
 
     betas_to_add_detected <- betas_to_add[betas_to_add %in% possible_betas]
 
@@ -665,6 +718,8 @@ addHMRF <- function(gobject,
 #' @name viewHMRFresults2D
 #' @description View results from doHMRF.
 #' @param gobject giotto object
+#' @param spat_unit spatial unit
+#' @param feat_type feature type
 #' @param HMRFoutput HMRF output from doHMRF
 #' @param k number of HMRF domains
 #' @param betas_to_view results from different betas that you want to view
@@ -672,11 +727,21 @@ addHMRF <- function(gobject,
 #' @returns spatial plots with HMRF domains
 #' @seealso \code{\link{spatPlot2D}}
 #' @export
-viewHMRFresults2D <- function(gobject,
-    HMRFoutput,
-    k = NULL,
-    betas_to_view = NULL,
-    ...) {
+viewHMRFresults2D <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        HMRFoutput,
+        k = NULL,
+        betas_to_view = NULL,
+        ...) {
+    spat_unit <- set_default_spat_unit(
+        gobject = gobject, spat_unit = spat_unit
+    )
+    feat_type <- set_default_feat_type(
+        gobject = gobject, spat_unit = spat_unit, feat_type = feat_type
+    )
+
     if (!"HMRFoutput" %in% class(HMRFoutput)) {
         stop("HMRFoutput needs to be output from doHMRFextend")
     }
@@ -684,7 +749,9 @@ viewHMRFresults2D <- function(gobject,
     ## reader.py and get_result.py paths
     # TODO: part of the package
     get_result_path <- system.file(
-        "python", "get_result2.py", package = "Giotto")
+        "python", "get_result2.py",
+        package = "Giotto"
+    )
 
     # paths and name
     name <- HMRFoutput$name
@@ -699,8 +766,10 @@ viewHMRFresults2D <- function(gobject,
 
     # betas
     betas <- HMRFoutput$betas
-    possible_betas <- seq(betas[1], to = betas[1] + (betas[2] * (betas[3] - 1)),
-                        by = betas[2])
+    possible_betas <- seq(betas[1],
+        to = betas[1] + (betas[2] * (betas[3] - 1)),
+        by = betas[2]
+    )
 
     betas_to_view_detected <- betas_to_view[betas_to_view %in% possible_betas]
 
@@ -716,19 +785,48 @@ viewHMRFresults2D <- function(gobject,
         )
 
         print(result_command)
-
         output <- system(command = result_command, intern = TRUE)
 
+        # get cell_IDs
+        cids <- gsub(basename(output_data), "", output_data) %>%
+            gsub(pattern = "\"", replacement = "") %>%
+            list.files(
+                pattern = "expression_matrix",
+                full.names = TRUE
+            ) %>%
+            data.table::fread(nrows = 0L, header = TRUE) %>%
+            colnames()
+        cids <- cids[-1] # gene colname is also included
+
+        # create unique name
+        annot_DT <- data.table::data.table(
+            cell_ID = cids,
+            temp_name = output
+        )
+
+        annot_name <- paste0("hmrf_k.", k, "_b.", b)
+        data.table::setnames(annot_DT, old = "temp_name", new = annot_name)
+
+
+        gobject <- addCellMetadata(
+            gobject = gobject,
+            spat_unit = spat_unit,
+            feat_type = feat_type,
+            column_cell_ID = "cell_ID",
+            new_metadata = annot_DT,
+            by_column = TRUE
+        )
 
         title_name <- paste0("k = ", k, " b = ", b)
 
         spatPlot2D(
-            gobject = gobject, 
-            cell_color = as.factor(output), 
-            show_plot = TRUE, 
-            save_plot = FALSE, 
-            title = title_name, 
-            ...)
+            gobject = gobject,
+            cell_color = annot_name,
+            show_plot = TRUE,
+            save_plot = FALSE,
+            title = title_name,
+            ...
+        )
     }
 }
 
@@ -737,6 +835,8 @@ viewHMRFresults2D <- function(gobject,
 #' @name viewHMRFresults3D
 #' @description View results from doHMRF.
 #' @param gobject giotto object
+#' @param spat_unit spatial unit
+#' @param feat_type feature type
 #' @param HMRFoutput HMRF output from doHMRF
 #' @param k number of HMRF domains
 #' @param betas_to_view results from different betas that you want to view
@@ -744,19 +844,31 @@ viewHMRFresults2D <- function(gobject,
 #' @returns spatial plots with HMRF domains
 #' @seealso \code{\link{spatPlot3D}}
 #' @export
-viewHMRFresults3D <- function(gobject,
-    HMRFoutput,
-    k = NULL,
-    betas_to_view = NULL,
-    ...) {
+viewHMRFresults3D <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        HMRFoutput,
+        k = NULL,
+        betas_to_view = NULL,
+        ...) {
     if (!"HMRFoutput" %in% class(HMRFoutput)) {
         stop("HMRFoutput needs to be output from doHMRFextend")
     }
 
+    spat_unit <- set_default_spat_unit(
+        gobject = gobject, spat_unit = spat_unit
+    )
+    feat_type <- set_default_feat_type(
+        gobject = gobject, spat_unit = spat_unit, feat_type = feat_type
+    )
+
     ## reader.py and get_result.py paths
     # TODO: part of the package
     get_result_path <- system.file(
-        "python", "get_result2.py", package = "Giotto")
+        "python", "get_result2.py",
+        package = "Giotto"
+    )
 
     # paths and name
     name <- HMRFoutput$name
@@ -771,8 +883,10 @@ viewHMRFresults3D <- function(gobject,
 
     # betas
     betas <- HMRFoutput$betas
-    possible_betas <- seq(betas[1], to = betas[1] + (betas[2] * (betas[3] - 1)),
-                        by = betas[2])
+    possible_betas <- seq(betas[1],
+        to = betas[1] + (betas[2] * (betas[3] - 1)),
+        by = betas[2]
+    )
 
     betas_to_view_detected <- betas_to_view[betas_to_view %in% possible_betas]
 
@@ -788,19 +902,48 @@ viewHMRFresults3D <- function(gobject,
         )
 
         print(result_command)
-
         output <- system(command = result_command, intern = TRUE)
 
+        # get cell_IDs
+        cids <- gsub(basename(output_data), "", output_data) %>%
+            gsub(pattern = "\"", replacement = "") %>%
+            list.files(
+                pattern = "expression_matrix",
+                full.names = TRUE
+            ) %>%
+            data.table::fread(nrows = 0L, header = TRUE) %>%
+            colnames()
+        cids <- cids[-1] # gene colname is also included
+
+        # create unique name
+        annot_DT <- data.table::data.table(
+            cell_ID = cids,
+            temp_name = output
+        )
+
+        annot_name <- paste0("hmrf_k.", k, "_b.", b)
+        data.table::setnames(annot_DT, old = "temp_name", new = annot_name)
+
+
+        gobject <- addCellMetadata(
+            gobject = gobject,
+            spat_unit = spat_unit,
+            feat_type = feat_type,
+            column_cell_ID = "cell_ID",
+            new_metadata = annot_DT,
+            by_column = TRUE
+        )
 
         title_name <- paste0("k = ", k, " b = ", b)
 
         spatPlot3D(
-            gobject = gobject, 
-            cell_color = output, 
-            show_plot = TRUE, 
-            save_plot = FALSE, 
-            title = title_name, 
-            ...)
+            gobject = gobject,
+            cell_color = annot_name,
+            show_plot = TRUE,
+            save_plot = FALSE,
+            title = title_name,
+            ...
+        )
     }
 }
 
@@ -818,17 +961,18 @@ viewHMRFresults3D <- function(gobject,
 #' @param seed random seed
 #' @returns list
 #' @details
-#' This function samples a subset of spatial genes among different clusters, 
+#' This function samples a subset of spatial genes among different clusters,
 #' with size n = target.
-#' Number of samples from each cluster denpends on the relative proportion of 
+#' Number of samples from each cluster denpends on the relative proportion of
 #' each cluster.
-#' Changing from equal size by setting sample_rate = 1 to with exact proportion 
+#' Changing from equal size by setting sample_rate = 1 to with exact proportion
 #' of each cluster by setting sample_rate = +Inf
 #' @keywords internal
-sampling_sp_genes <- function(clust,
-    sample_rate = 2,
-    target = 500,
-    seed = 10) {
+sampling_sp_genes <- function(
+        clust,
+        sample_rate = 2,
+        target = 500,
+        seed = 10) {
     tot <- 0
     num_cluster <- length(unique(clust))
     gene_list <- list()
@@ -862,28 +1006,30 @@ sampling_sp_genes <- function(clust,
     union_genes <- unique(union_genes)
 
     return(list(
-        union_genes = union_genes, num_sample = num_sample, 
-        num_gene = genes, gene_list = gene_list))
+        union_genes = union_genes, num_sample = num_sample,
+        num_gene = genes, gene_list = gene_list
+    ))
 }
 
 
 #' @title numPts_below_line
 #' @name numPts_below_line
-#' @description function to calculate the number of data points below a given 
+#' @description function to calculate the number of data points below a given
 #' line
-#' @param myVector input sequence of sorted positive values from smallest to 
+#' @param myVector input sequence of sorted positive values from smallest to
 #' greatest
 #' @param slope slope to compare
-#' @param x location point of the line to compare, integer from 1 to length of 
+#' @param x location point of the line to compare, integer from 1 to length of
 #' myVector
 #' @returns numeric
 #' @details
-#' This function calculates the number of data points in a sorted sequence 
+#' This function calculates the number of data points in a sorted sequence
 #' below a line with given slope through a certain point on this sequence.
 #' @keywords internal
-numPts_below_line <- function(myVector,
-    slope,
-    x) {
+numPts_below_line <- function(
+        myVector,
+        slope,
+        x) {
     yPt <- myVector[x]
     b <- yPt - (slope * x)
     xPts <- seq_along(myVector)
@@ -903,27 +1049,27 @@ numPts_below_line <- function(myVector,
 #' @param method method of spatial gene selection
 #' @returns list
 #' @details
-#' This function filters given gene list with the gene sets of selected 
+#' This function filters given gene list with the gene sets of selected
 #' spatial gene test in Giotto,
 #' also controls the total size of the gene set with given max number.
 #' @keywords external
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' filterSpatialGenes(g, spatial_genes = "Gm19935")
 #' @export
-filterSpatialGenes <- function(
-        gobject, spat_unit = NULL, feat_type = NULL, spatial_genes, max = 2500,
-    name = c("binSpect", "silhouetteRank", "silhouetteRankTest"), 
-    method = c("none", "elbow")) {
+filterSpatialGenes <- function(gobject, spat_unit = NULL, feat_type = NULL, spatial_genes, max = 2500,
+        name = c("binSpect", "silhouetteRank", "silhouetteRankTest"),
+        method = c("none", "elbow")) {
     name <- match.arg(
-        name, 
-        unique(c("binSpect", "silhouetteRank", "silhouetteRankTest", name)))
+        name,
+        unique(c("binSpect", "silhouetteRank", "silhouetteRankTest", name))
+    )
     method <- match.arg(method, unique(c("none", "elbow", method)))
 
 
     # NSE vars
-    binSpect.pval <- silhouetteRank.score <- silhouetteRankTest.pval <- 
+    binSpect.pval <- silhouetteRank.score <- silhouetteRankTest.pval <-
         feat_ID <- NULL
 
     # first determine how many spatial genes in this dataset
@@ -962,17 +1108,21 @@ filterSpatialGenes <- function(
         y0s <- sort(y0)
         y0s[y0s < 0] <- 0 # strictly positive
         # plot(x0, y0)
-        slope <- (max(y0s) - min(y0s)) / length(y0s) # This is the slope of the 
+        slope <- (max(y0s) - min(y0s)) / length(y0s) # This is the slope of the
         # line we want to slide. This is the diagonal.
         xPt <- floor(optimize(
-            numPts_below_line, lower = 1, upper = length(y0s), 
-            myVector = y0s, slope = slope)$minimum)
+            numPts_below_line,
+            lower = 1, upper = length(y0s),
+            myVector = y0s, slope = slope
+        )$minimum)
         xPt <- length(y0s) - xPt
         y_cutoff <- y0[xPt] # The y-value at this x point. This is our y_cutoff.
         gx_sorted <- head(gx_sorted, n = xPt)
         message("Elbow method chosen to determine number of spatial genes.")
-        cat(paste0("Elbow point determined to be at x=", xPt, " genes", 
-                " y=", y_cutoff))
+        cat(paste0(
+            "Elbow point determined to be at x=", xPt, " genes",
+            " y=", y_cutoff
+        ))
     }
 
     # filter user's gene list (spatial_genes)
@@ -981,28 +1131,28 @@ filterSpatialGenes <- function(
     num_genes_removed <- length(spatial_genes) - nrow(gx_sorted)
 
     return(list(
-        genes = gx_sorted$feat_ID, num_genes_removed = num_genes_removed))
+        genes = gx_sorted$feat_ID, num_genes_removed = num_genes_removed
+    ))
 }
 
 
 
 #' @title chooseAvailableSpatialGenes
 #' @name chooseAvailableSpatialGenes
-#' @description function to find the test name for existing spatial gene sets 
+#' @description function to find the test name for existing spatial gene sets
 #' in Giotto
 #' @param gobject Giotto object
 #' @param spat_unit spatial unit
 #' @param feat_type feature type
 #' @returns character
 #' @details
-#' This function outputs the available test name for existing spatial gene sets 
+#' This function outputs the available test name for existing spatial gene sets
 #' in Giotto,
 #' which could be used in parameter ‘name’ in `filterSpatialGenes`.
-#' Priorities for showing the spatial gene test names are ‘binSpect’ > 
+#' Priorities for showing the spatial gene test names are ‘binSpect’ >
 #' ‘silhouetteRankTest’ > ‘silhouetteRank’.
 #' @keywords internal
-chooseAvailableSpatialGenes <- function(
-        gobject, spat_unit = NULL, feat_type = NULL) {
+chooseAvailableSpatialGenes <- function(gobject, spat_unit = NULL, feat_type = NULL) {
     gx <- fDataDT(gobject, spat_unit = NULL, feat_type = NULL)
     eval1 <- "binSpect.pval" %in% names(gx)
     eval2 <- "silhouetteRankTest.pval" %in% names(gx)
@@ -1014,7 +1164,7 @@ chooseAvailableSpatialGenes <- function(
     } else if (eval3 == TRUE) {
         return("silhouetteRank")
     } else {
-        stop(paste0("No available spatial genes. Please run binSpect or 
+        stop(paste0("No available spatial genes. Please run binSpect or
                     silhouetteRank\n"), call. = FALSE)
     }
 }
@@ -1022,7 +1172,7 @@ chooseAvailableSpatialGenes <- function(
 
 #' @title checkAndFixSpatialGenes
 #' @name checkAndFixSpatialGenes
-#' @description function to check the selected test name for spatial gene set 
+#' @description function to check the selected test name for spatial gene set
 #' in Giotto object
 #' @param gobject Giotto object
 #' @param spat_unit spatial unit
@@ -1031,16 +1181,17 @@ chooseAvailableSpatialGenes <- function(
 #' @param use_score logical variable to select silhouetteRank score
 #' @returns character
 #' @details
-#' This function checks the user specified test name of spatial gene set in 
+#' This function checks the user specified test name of spatial gene set in
 #' Giotto object.
-#' SilhouetteRank works only with score, and SilhouetteRankTest works only 
+#' SilhouetteRank works only with score, and SilhouetteRankTest works only
 #' with pval. Use parameter use_score to specify.
 #' @keywords internal
-checkAndFixSpatialGenes <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    use_spatial_genes,
-    use_score = FALSE) {
+checkAndFixSpatialGenes <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        use_spatial_genes,
+        use_score = FALSE) {
     gx <- fDataDT(gobject, spat_unit = NULL, feat_type = NULL)
 
     if (use_spatial_genes == "silhouetteRank") {
@@ -1051,7 +1202,7 @@ checkAndFixSpatialGenes <- function(gobject,
             eval2 <- "silhouetteRankTest.pval" %in% names(gx)
             if (eval1 == TRUE && eval2 == TRUE) {
                 # if both evaluate to true, then decide by use_score.
-                # silhouetteRank works only with score, silhouetteRankTest 
+                # silhouetteRank works only with score, silhouetteRankTest
                 # works only with pval
                 if (use_score == TRUE) {
                     use_spatial_genes <- "silhouetteRank"
@@ -1063,8 +1214,8 @@ checkAndFixSpatialGenes <- function(gobject,
             } else if (eval2 == TRUE) {
                 use_spatial_genes <- "silhouetteRankTest"
             } else {
-                stop(paste0("\n use_spatial_genes is set to silhouetteRank, 
-                            but it has not been run yet. Run silhouetteRank 
+                stop(paste0("\n use_spatial_genes is set to silhouetteRank,
+                            but it has not been run yet. Run silhouetteRank
                             first.\n"), call. = FALSE)
             }
         }
@@ -1072,14 +1223,16 @@ checkAndFixSpatialGenes <- function(gobject,
     } else if (use_spatial_genes == "binSpect") {
         eval1 <- "binSpect.pval" %in% names(gx)
         if (eval1 == FALSE) {
-            stop(paste0("use_spatial_genes is set to binSpect, but it has 
-                        not been run yet. Run binSpect first."), 
-                call. = FALSE)
+            stop(paste0("use_spatial_genes is set to binSpect, but it has
+                        not been run yet. Run binSpect first."),
+                call. = FALSE
+            )
         }
         return(use_spatial_genes)
     } else {
-        stop(paste0("use_spatial_genes is set to one that is not supported."), 
-            call. = FALSE)
+        stop(paste0("use_spatial_genes is set to one that is not supported."),
+            call. = FALSE
+        )
     }
 }
 
@@ -1088,120 +1241,126 @@ checkAndFixSpatialGenes <- function(gobject,
 
 #' @title initHMRF_V2
 #' @name initHMRF_V2
-#' @description Run initialzation for HMRF model
+#' @description Run initialization for HMRF model
 #' @param gobject giotto object
 #' @param spat_unit spatial unit
 #' @param feat_type feature type
 #' @param expression_values expression values to use
 #' @param spatial_network_name name of spatial network to use for HMRF
 #' @param use_spatial_genes which of Giotto's spatial genes to use
-#' @param use_score use score as gene selection criterion 
+#' @param use_score use score as gene selection criterion
 #' (applies when use_spatial_genes=silhouetteRank)
 #' @param gene_list_from_top total spatial genes before sampling
-#' @param filter_method filter genes by top or by elbow method, prior to 
+#' @param filter_method filter genes by top or by elbow method, prior to
 #' sampling
 #' @param user_gene_list user-specified genes (optional)
-#' @param use_pca if PCA is used on the spatial gene expression value for 
+#' @param use_pca if PCA is used on the spatial gene expression value for
 #' clustering
 #' @param use_pca_dim dimensions of the PCs of the selected expression
 #' @param gene_samples number of spatial gene subset to use for HMRF
-#' @param gene_sampling_rate parameter (1-50) controlling proportion of gene 
-#' samples from different module when sampling, 1 corresponding to equal gene 
-#' samples between different modules; 50 corresponding to gene samples 
+#' @param gene_sampling_rate parameter (1-50) controlling proportion of gene
+#' samples from different module when sampling, 1 corresponding to equal gene
+#' samples between different modules; 50 corresponding to gene samples
 #' proportional to module size.
 #' @param gene_sampling_seed random number seed to sample spatial genes
 #' @param use_metagene if metagene expression is used for clustering
 #' @param cluster_metagene number of metagenes to use
-#' @param top_metagene = number of genes in each cluster for the metagene 
+#' @param top_metagene = number of genes in each cluster for the metagene
 #' calculation
-#' @param existing_spatial_enrichm_to_use name of existing spatial enrichment 
+#' @param existing_spatial_enrichm_to_use name of existing spatial enrichment
 #' result to use
-#' @param use_neighborhood_composition if neighborhood composition is used for 
+#' @param use_neighborhood_composition if neighborhood composition is used for
 #' hmrf
-#' @param spatial_network_name_for_neighborhood spatial network used to 
+#' @param spatial_network_name_for_neighborhood spatial network used to
 #' calculate neighborhood composition
 #' @param metadata_to_use metadata used to calculate neighborhood composition
-#' @param hmrf_seed random number seed to generate initial mean vector of HMRF 
+#' @param hmrf_seed random number seed to generate initial mean vector of HMRF
 #' model
-#' @param cl.method clustering method to calculate the initial mean vector, 
+#' @param cl.method clustering method to calculate the initial mean vector,
 #' selecting from 'km', 'leiden', or 'louvain'
 #' @param resolution.cl resolution of Leiden or Louvain clustering
 #' @param k number of HMRF domains
 #' @param tolerance error tolerance threshold
 #' @param zscore type of zscore to use
-#' @param nstart number of Kmeans initializations from which to select the 
+#' @param nstart number of Kmeans initializations from which to select the
 #' best initialization
 #' @param factor_step dampened factor step
 #' @param python_path python_path
 #' @returns initialized HMRF
 #' @details
-#' This function is the initialization step of HMRF domain clustering. First, 
+#' This function is the initialization step of HMRF domain clustering. First,
 #' user specify which of Giotto's spatial genes to run,
-#' through use_spatial_genes. Spatial genes have been stored in the gene 
+#' through use_spatial_genes. Spatial genes have been stored in the gene
 #' metadata table. A first pass of genes will filter genes that
-#' are not significantly spatial, as determined by filter_method. If 
+#' are not significantly spatial, as determined by filter_method. If
 #' filter_method is none, then top 2500 (gene_list_from_top) genes
-#' ranked by pvalue are considered spatial. If filter_method is elbow, then the 
+#' ranked by pvalue are considered spatial. If filter_method is elbow, then the
 #' exact cutoff is determined by the elbow in
-#' the -log10 P-value vs. gene rank plot. Second, users have a few options to 
+#' the -log10 P-value vs. gene rank plot. Second, users have a few options to
 #' decrease the dimension of the spatial genes for
 #' clustering, listed with selection priority:
 #'    1. use PCA of the spatial gene expressions (selected by use_pca)
 #'    2. use metagene expressions (selected by use_metagene)
 #'    3. sampling to select 500 spatial genes (controlled by gene_samples).
-#' Third, once spatial genes are finalized, we are using clustering method to 
+#' Third, once spatial genes are finalized, we are using clustering method to
 #' initialize HMRF.
-#' Instead of select spatial genes for domain clustering, HMRF method could 
-#' also applied on unit neighbohood composition of any group
-#' membership(such as cell types), specified by parameter: 
+#' Instead of select spatial genes for domain clustering, HMRF method could
+#' also applied on unit neighborhood composition of any group
+#' membership(such as cell types), specified by parameter:
 #' use_neighborhood_composition,  spatial_network_name_for_neighborhood and
-#' metadata_to_use. Also HMRF provides the oppertunity for user to do 
+#' metadata_to_use. Also HMRF provides the opportunity for user to do
 #' clustering by any customized spatial enrichment matrix
 #' (existing_spatial_enrichm_to_use).
-#' There are 3 clustering algorithm: K-means, Leiden, and Louvain to determine 
+#' There are 3 clustering algorithm: K-means, Leiden, and Louvain to determine
 #' initial centroids of HMRF. The initialization is
-#' then finished. This function returns a list containing y (expression), 
+#' then finished. This function returns a list containing y (expression),
 #' nei (neighborhood structure), numnei (number of neighbors),
-#' blocks (graph colors), damp (dampened factor), mu (mean), 
+#' blocks (graph colors), damp (dampened factor), mu (mean),
 #' sigma (covariance), k, genes, edgelist, init.cl (initial clusters),
 #' spat_unit, feat_type. This information is needed for the second step, doHMRF.
+#' @examples
+#' g <- GiottoData::loadGiottoMini("visium")
+#' g <- binSpect(g, return_gobject = TRUE)
+#'
+#' initHMRF_V2(gobject = g, cl.method = "km")
 #' @export
 initHMRF_V2 <-
-    function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("scaled", "normalized", "custom"),
-    spatial_network_name = "Delaunay_network",
-    use_spatial_genes = c("binSpect", "silhouetteRank"),
-    use_score = FALSE,
-    gene_list_from_top = 2500,
-    filter_method = c("none", "elbow"),
-    user_gene_list = NULL,
-    use_pca = FALSE,
-    use_pca_dim = 1:20,
-    gene_samples = 500,
-    gene_sampling_rate = 2,
-    gene_sampling_seed = 10,
-    use_metagene = FALSE,
-    cluster_metagene = 50,
-    top_metagene = 20,
-    existing_spatial_enrichm_to_use = NULL,
-    use_neighborhood_composition = FALSE,
-    spatial_network_name_for_neighborhood = NULL,
-    metadata_to_use = NULL,
-    hmrf_seed = 100,
-    cl.method = c("km", "leiden", "louvain"),
-    resolution.cl = 1,
-    k = 10,
-    tolerance = 1e-05,
-    zscore = c("none", "rowcol", "colrow"),
-    nstart = 1000,
-    factor_step = 1.05,
-    python_path = NULL) {
+    function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("scaled", "normalized", "custom"),
+        spatial_network_name = "Delaunay_network",
+        use_spatial_genes = c("binSpect", "silhouetteRank"),
+        use_score = FALSE,
+        gene_list_from_top = 2500,
+        filter_method = c("none", "elbow"),
+        user_gene_list = NULL,
+        use_pca = FALSE,
+        use_pca_dim = 1:20,
+        gene_samples = 500,
+        gene_sampling_rate = 2,
+        gene_sampling_seed = 10,
+        use_metagene = FALSE,
+        cluster_metagene = 50,
+        top_metagene = 20,
+        existing_spatial_enrichm_to_use = NULL,
+        use_neighborhood_composition = FALSE,
+        spatial_network_name_for_neighborhood = NULL,
+        metadata_to_use = NULL,
+        hmrf_seed = 100,
+        cl.method = c("km", "leiden", "louvain"),
+        resolution.cl = 1,
+        k = 10,
+        tolerance = 1e-05,
+        zscore = c("none", "rowcol", "colrow"),
+        nstart = 1000,
+        factor_step = 1.05,
+        python_path = NULL) {
         wrap_msg(
-        "If used in published research, please cite:
+            "If used in published research, please cite:
         Q Zhu, S Shah, R Dries, L Cai, GC Yuan.
-        'Identification of spatially associated subpopulations by combining 
+        'Identification of spatially associated subpopulations by combining
         scRNAseq and sequential fluorescence in situ hybridization data'
         Nature biotechnology 36 (12), 1183-1190. 2018\n"
         )
@@ -1237,51 +1396,60 @@ initHMRF_V2 <-
         cx <- pDataDT(gobject, spat_unit = spat_unit, feat_type = feat_type)
 
         spatial_network <- getSpatialNetwork(
-            gobject, 
-            spat_unit = spat_unit, 
-            name = spatial_network_name, 
-            output = "networkDT", 
-            copy_obj = FALSE)
+            gobject,
+            spat_unit = spat_unit,
+            name = spatial_network_name,
+            output = "networkDT",
+            copy_obj = FALSE
+        )
         spatial_network <- spatial_network[, .(to, from)]
 
         if (use_neighborhood_composition) {
             if (is.null(spatial_network_name_for_neighborhood)) {
-                stop("spatial network is required to define neighborhood, 
+                stop("spatial network is required to define neighborhood,
                     set with \'spatial_network_name_for_neighborhood\' \n",
                     call. = FALSE
                 )
             } else if (is.null(metadata_to_use)) {
-                stop("please specify the cluster in meta data, set with 
+                stop("please specify the cluster in meta data, set with
                     \'metadata_to_use\' \n",
                     call. = FALSE
                 )
             } else if (is.null(cx[[metadata_to_use]])) {
-                stop("please provide a valid index in meta data, set with 
+                stop("please provide a valid index in meta data, set with
                     \'metadata_to_use\'",
                     call. = FALSE
                 )
             }
 
-            cat(paste0("use spatial network composition of \'", 
-                    metadata_to_use, "\' for domain clustering"))
+            cat(paste0(
+                "use spatial network composition of \'",
+                metadata_to_use, "\' for domain clustering"
+            ))
 
             name.cl <- as.character(sort(unique(cx[[metadata_to_use]])))
 
             spatial_network_for_neighborhood <- getSpatialNetwork(
                 gobject,
                 spat_unit = spat_unit,
-                name = spatial_network_name_for_neighborhood, 
-                output = "networkDT", 
+                name = spatial_network_name_for_neighborhood,
+                output = "networkDT",
                 copy_obj = FALSE
             )
 
-            from.all <- c(spatial_network_for_neighborhood$from, 
-                        spatial_network_for_neighborhood$to)
-            to.all <- c(spatial_network_for_neighborhood$to, 
-                        spatial_network_for_neighborhood$from)
+            from.all <- c(
+                spatial_network_for_neighborhood$from,
+                spatial_network_for_neighborhood$to
+            )
+            to.all <- c(
+                spatial_network_for_neighborhood$to,
+                spatial_network_for_neighborhood$from
+            )
 
-            ct.tab <- aggregate(cx[[metadata_to_use]][match(
-                to.all, cx[["cell_ID"]])],
+            ct.tab <- aggregate(
+                cx[[metadata_to_use]][match(
+                    to.all, cx[["cell_ID"]]
+                )],
                 by = list(cell_ID = from.all), function(y) {
                     table(y)[name.cl]
                 }
@@ -1291,20 +1459,21 @@ initHMRF_V2 <-
             y0[is.na(y0)] <- 0
             rownames(y0) <- ct.tab$cell_ID
             y0 <- y0 / rowSums(y0)
-
         } else if (!is.null(existing_spatial_enrichm_to_use)) {
             y0 <- getSpatialEnrichment(
                 gobject,
                 spat_unit = spat_unit, feat_type = feat_type,
-                name = existing_spatial_enrichm_to_use, 
+                name = existing_spatial_enrichm_to_use,
                 output = "data.table"
             )
             cell_ID_enrich <- y0$cell_ID
             y0 <- as.data.frame(y0[, -"cell_ID"])
             rownames(y0) <- cell_ID_enrich
 
-            cat(paste0("Spatial enrichment result: \'", 
-                    existing_spatial_enrichm_to_use, "\' is used."))
+            cat(paste0(
+                "Spatial enrichment result: \'",
+                existing_spatial_enrichm_to_use, "\' is used."
+            ))
 
             if (sum(!rownames(y0) %in% cx$cell_ID) > 0) {
                 stop("Rownames of selected spatial enrichment result do not
@@ -1330,18 +1499,18 @@ initHMRF_V2 <-
                 "normalized", "custom", expression_values
             )))
             expr_values <- get_expression_values(
-                gobject = gobject, 
-                spat_unit = spat_unit, 
+                gobject = gobject,
+                spat_unit = spat_unit,
                 feat_type = feat_type,
                 values = values, output = "matrix"
             )
             if (zscore != "none") {
                 zscore <- match.arg(zscore, c("none", "colrow", "rowcol"))
                 expr_values <- get_expression_values(
-                    gobject = gobject, 
-                    spat_unit = spat_unit, 
+                    gobject = gobject,
+                    spat_unit = spat_unit,
                     feat_type = feat_type,
-                    values = "normalized", 
+                    values = "normalized",
                     output = "matrix"
                 )
                 if (zscore == "colrow") {
@@ -1357,8 +1526,8 @@ initHMRF_V2 <-
             if (!"binSpect.pval" %in% names(gx) &&
                 !"silhouetteRank.score" %in% names(gx) &&
                 !"silhouetteRankTest.pval" %in% names(gx)) {
-                stop(paste0("Giotto spatial gene detection has not been run. 
-                    Please run spatial gene detection first: binSpect, 
+                stop(paste0("Giotto spatial gene detection has not been run.
+                    Please run spatial gene detection first: binSpect,
                     silhouetteRank."),
                     call. = FALSE
                 )
@@ -1367,30 +1536,32 @@ initHMRF_V2 <-
             if (!is.null(user_gene_list)) {
                 message("User supplied gene list detected.")
                 message("Checking user gene list is spatial...")
-                
+
                 use_spatial_genes <- chooseAvailableSpatialGenes(gobject)
                 filtered <- filterSpatialGenes(
                     gobject,
-                    spat_unit = spat_unit, 
-                    feat_type = feat_type, 
+                    spat_unit = spat_unit,
+                    feat_type = feat_type,
                     spatial_genes = user_gene_list,
-                    max = gene_list_from_top, 
+                    max = gene_list_from_top,
                     name = use_spatial_genes,
                     method = filter_method
                 )
                 if (filtered$num_genes_removed > 0) {
                     cat(paste0(
                         "Removed ", filtered$num_genes_removed,
-                        " from user's input gene list due to being absent or 
+                        " from user's input gene list due to being absent or
                         non-spatial genes."
                     ))
-                    cat(paste0("Kept ", length(filtered$genes), 
-                            " spatial genes for next step"))
+                    cat(paste0(
+                        "Kept ", length(filtered$genes),
+                        " spatial genes for next step"
+                    ))
                 }
                 spatial_genes <- filtered$genes
 
                 if (length(spatial_genes) == 0) {
-                    stop("No genes are remaining to do HMRF. Please give a 
+                    stop("No genes are remaining to do HMRF. Please give a
                         larger gene list.",
                         call. = FALSE
                     )
@@ -1402,41 +1573,45 @@ initHMRF_V2 <-
                 ))
                 use_spatial_genes <- checkAndFixSpatialGenes(
                     gobject,
-                    spat_unit = spat_unit, 
+                    spat_unit = spat_unit,
                     feat_type = feat_type,
-                    use_spatial_genes = use_spatial_genes, 
+                    use_spatial_genes = use_spatial_genes,
                     use_score = use_score
                 )
                 all_genes <- gx$feat_ID
                 filtered <- filterSpatialGenes(
                     gobject,
-                    spat_unit = spat_unit, 
+                    spat_unit = spat_unit,
                     feat_type = feat_type,
-                    spatial_genes = all_genes, 
+                    spatial_genes = all_genes,
                     max = gene_list_from_top,
                     name = use_spatial_genes,
                     method = filter_method
                 )
-                cat(paste0("Kept ", length(filtered$genes), 
-                        " top spatial genes for next step"))
+                cat(paste0(
+                    "Kept ", length(filtered$genes),
+                    " top spatial genes for next step"
+                ))
                 spatial_genes <- filtered$genes
             }
 
             if (use_pca == TRUE) {
                 expr_values <- expr_values[spatial_genes, ]
                 pc.expr <- prcomp(expr_values)[[2]]
-                use_pca_dim <- use_pca_dim[use_pca_dim %in% 1:ncol(pc.expr)]
+                use_pca_dim <- use_pca_dim[
+                    use_pca_dim %in% seq_len(ncol(pc.expr))
+                ]
                 y0 <- (pc.expr[, use_pca_dim])
             } else {
                 message("Computing spatial coexpression modules...")
                 spat_cor_netw_DT <- detectSpatialCorFeats(
                     gobject = gobject,
-                    feat_type = feat_type, 
-                    spat_unit = spat_unit, 
+                    feat_type = feat_type,
+                    spat_unit = spat_unit,
                     expression_values = values,
                     method = "network",
                     spatial_network_name = spatial_network_name,
-                    subset_feats = spatial_genes, 
+                    subset_feats = spatial_genes,
                     network_smoothing = 0
                 )
 
@@ -1445,15 +1620,15 @@ initHMRF_V2 <-
                     if (n < length(spatial_genes)) {
                         spat_cor_netw_DT <- clusterSpatialCorFeats(
                             spat_cor_netw_DT,
-                            name = "spat_netw_clus", 
+                            name = "spat_netw_clus",
                             k = 20
                         )
-                        message("Sampling spatial genes from coexpression 
+                        message("Sampling spatial genes from coexpression
                                 modules...")
                         sample_genes <- sampling_sp_genes(
                             spat_cor_netw_DT$cor_clusters$spat_netw_clus,
-                            sample_rate = gene_sampling_rate, 
-                            target = n, 
+                            sample_rate = gene_sampling_rate,
+                            target = n,
                             seed = gene_sampling_seed
                         )
                         spatial_genes_selected <- sample_genes$union_genes
@@ -1471,11 +1646,13 @@ initHMRF_V2 <-
                     expr_values <- expr_values[spatial_genes_selected, ]
                 } else {
                     k.sp <- min(
-                        ceiling(length(spatial_genes) / 20), cluster_metagene)
+                        ceiling(length(spatial_genes) / 20), cluster_metagene
+                    )
                     if (k.sp < cluster_metagene) {
                         cat(paste0(
-                        "construct ", k.sp, 
-                        " coexpression modules due to limited gene size..."))
+                            "construct ", k.sp,
+                            " coexpression modules due to limited gene size..."
+                        ))
                     }
                     spat_cor_netw_DT <- clusterSpatialCorFeats(spat_cor_netw_DT,
                         name = "spat_netw_clus", k = k.sp
@@ -1486,28 +1663,32 @@ initHMRF_V2 <-
                         show_top_feats = 1
                     )
 
-                    cat(paste0("Collecting top spatial genes and calculating 
+                    cat(paste0("Collecting top spatial genes and calculating
                         metagenes from ", k.sp, " coexpression modules..."))
 
                     top_per_module <- cluster_genes_DT[
-                        , head(.SD, top_metagene), by = clus]
+                        , head(.SD, top_metagene),
+                        by = clus
+                    ]
                     cluster_genes <- top_per_module$clus
                     names(cluster_genes) <- top_per_module$feat_ID
 
                     meta.genes <- createMetafeats(
                         gobject,
-                        spat_unit = spat_unit, 
-                        feat_type = feat_type, 
+                        spat_unit = spat_unit,
+                        feat_type = feat_type,
                         expression_values = values,
-                        feat_clusters = cluster_genes, 
+                        feat_clusters = cluster_genes,
                         return_gobject = FALSE
                     )
 
-                    expr_values <- t(meta.genes@enrichDT[, 1:k.sp])
+                    expr_values <- t(meta.genes@enrichDT[, seq_len(k.sp)])
                     colnames(expr_values) <- unlist(
-                        meta.genes@enrichDT[, "cell_ID"])
+                        meta.genes@enrichDT[, "cell_ID"]
+                    )
                     rownames(expr_values) <- paste0(
-                        "metagene_", rownames(expr_values))
+                        "metagene_", rownames(expr_values)
+                    )
                 }
 
                 y0 <- t(as.matrix(expr_values))
@@ -1532,13 +1713,13 @@ initHMRF_V2 <-
             y <- y0
         }
 
-        
+
         numcell <- dim(y)[1]
         m <- dim(y)[2]
         ncol.nei <- max(table(c(spatial_network$to, spatial_network$from)))
         nei <- matrix(-1, ncol = ncol.nei, nrow = numcell)
         rownames(nei) <- rownames(y)
-        for (i in 1:numcell) {
+        for (i in seq_len(numcell)) {
             nei.i <- c(spatial_network$from[spatial_network$to ==
                 rownames(nei)[i]], spatial_network$to[spatial_network$from ==
                 rownames(nei)[i]])
@@ -1549,12 +1730,12 @@ initHMRF_V2 <-
         numnei <- as.integer(rowSums(nei != (-1)))
         nn <- nei
         numedge <- 0
-        for (i in 1:numcell) {
+        for (i in seq_len(numcell)) {
             numedge <- numedge + length(nn[i, nn[i, ] != -1])
         }
         edgelist <- matrix(0, nrow = numedge, ncol = 2)
         edge_ind <- 1
-        for (i in 1:numcell) {
+        for (i in seq_len(numcell)) {
             neighbors <- nn[i, nn[i, ] != -1]
             for (j in seq_along(neighbors)) {
                 edgelist[edge_ind, ] <- c(i, neighbors[j])
@@ -1563,9 +1744,11 @@ initHMRF_V2 <-
         }
         message("Parsing neighborhood graph...")
         pp <- tidygraph::tbl_graph(
-            edges = as.data.frame(edgelist), directed = FALSE)
+            edges = as.data.frame(edgelist), directed = FALSE
+        )
         yy <- pp %>% dplyr::mutate(
-            color = as.factor(graphcoloring::color_dsatur()))
+            color = as.factor(graphcoloring::color_dsatur())
+        )
         colors <- as.list(yy)$nodes$color
         cl_color <- sort(unique(colors))
         blocks <- lapply(cl_color, function(cl) {
@@ -1585,7 +1768,7 @@ initHMRF_V2 <-
                 nstart = nstart
             )
             mu <- t(kk$centers)
-            lclust <- lapply(1:k, function(x) which(kk$cluster == x))
+            lclust <- lapply(seq_len(k), function(x) which(kk$cluster == x))
         } else {
             ##### need to double check leiden and louvain cluster functions
             gobject@dimension_reduction$cells$spatial <- NULL
@@ -1596,8 +1779,8 @@ initHMRF_V2 <-
 
             gobject <- createNearestNetwork(
                 gobject = gobject,
-                dim_reduction_to_use = "spatial", 
-                dim_reduction_name = "spatial_feat", 
+                dim_reduction_to_use = "spatial",
+                dim_reduction_name = "spatial_feat",
                 dimensions_to_use = seq_len(ncol(y)),
                 name = "sNN.initHMRF"
             )
@@ -1605,30 +1788,32 @@ initHMRF_V2 <-
             if (cl.method == "leiden") {
                 message("Leiden clustering initialization...")
                 leiden.cl <- doLeidenCluster(
-                    gobject = gobject, 
-                    nn_network_to_use = "sNN", 
-                    network_name = "sNN.initHMRF", 
-                    set_seed = hmrf_seed, 
+                    gobject = gobject,
+                    nn_network_to_use = "sNN",
+                    network_name = "sNN.initHMRF",
+                    set_seed = hmrf_seed,
                     return_gobject = FALSE,
-                    python_path = python_path, 
+                    python_path = python_path,
                     resolution = resolution.cl
                 )
                 cl.match <- leiden.cl$leiden_clus[
-                    match(rownames(y), leiden.cl$cell_ID)]
+                    match(rownames(y), leiden.cl$cell_ID)
+                ]
                 mu <- aggregate(y, by = list(cl.match), FUN = mean)
             } else if (cl.method == "louvain") {
                 message("Louvain clustering initialization...")
                 louvain.cl <- doLouvainCluster(
-                    gobject = gobject, 
-                    nn_network_to_use = "sNN", 
-                    network_name = "sNN.initHMRF", 
-                    set_seed = hmrf_seed, 
+                    gobject = gobject,
+                    nn_network_to_use = "sNN",
+                    network_name = "sNN.initHMRF",
+                    set_seed = hmrf_seed,
                     return_gobject = FALSE,
                     python_path = python_path,
                     resolution = resolution.cl
                 )
                 cl.match <- louvain.cl$louvain_clus[
-                    match(rownames(y), louvain.cl$cell_ID)]
+                    match(rownames(y), louvain.cl$cell_ID)
+                ]
                 mu <- aggregate(y, by = list(cl.match), FUN = mean)
             }
 
@@ -1642,7 +1827,7 @@ initHMRF_V2 <-
 
         damp <- array(0, c(k))
         sigma <- array(0, c(m, m, k))
-        for (i in 1:k) {
+        for (i in seq_len(k)) {
             sigma[, , i] <- cov(y[lclust[[i]], ])
             di <- smfishHmrf::findDampFactor(
                 sigma[, , i],
@@ -1655,7 +1840,7 @@ initHMRF_V2 <-
         list(
             y = y, nei = nei, numnei = numnei, blocks = blocks,
             damp = damp, mu = mu, sigma = sigma, k = k, genes = colnames(y),
-            edgelist = edgelist, init.cl = lclust, spat_unit = spat_unit, 
+            edgelist = edgelist, init.cl = lclust, spat_unit = spat_unit,
             feat_type = feat_type
         )
     }
@@ -1667,41 +1852,47 @@ initHMRF_V2 <-
 #' @title doHMRF_V2
 #' @name doHMRF_V2
 #' @description function to run HMRF model
-#' @param HMRF_init_obj initialization object list returned from initHMRF() 
+#' @param HMRF_init_obj initialization object list returned from initHMRF()
 #' function
-#' @param betas beta value of the HMRF model, controlling the smoothness of 
-#' clustering. NULL value of beta will provide default values based on feature 
-#' numbers, otherwise, a vector of three values: initial beta, beta increment, 
+#' @param betas beta value of the HMRF model, controlling the smoothness of
+#' clustering. NULL value of beta will provide default values based on feature
+#' numbers, otherwise, a vector of three values: initial beta, beta increment,
 #' and number of betas
 #' @returns HMRF model
 #' @details
-#' This function will run a HMRF model after initialization of HMRF. Of note 
+#' This function will run a HMRF model after initialization of HMRF. Of note
 #' is the beta parameter, the smoothing parameter.
-#' If the users are interested in selecting results from different smoothness, 
+#' If the users are interested in selecting results from different smoothness,
 #' we recommend running a range of betas,
-#' hence betas specify what this range is. For example, betas=c(0,10,5) will 
+#' hence betas specify what this range is. For example, betas=c(0,10,5) will
 #' run for the following betas: 0, 10, 20, 30, 40.
-#' betas=c(0,5,2) will run for betas: 0, 5, 10. Setting the beta can use the 
+#' betas=c(0,5,2) will run for betas: 0, 5, 10. Setting the beta can use the
 #' following guideline:
 #' If number of features N is 10<N<50, set betas=c(0, 1, 20)
 #' For 50<N<100, set betas=c(0, 2, 25)
 #' For 100<N<500, set betas=c(0, 5, 20)
-#' If user didn't specify the number or sequence of beta, the function will 
+#' If user didn't specify the number or sequence of beta, the function will
 #' provide a default value based on the number of features.
-#' Returns a list of results for betas, spat_unit and feat_type. Result for 
-#' each beta is a list with probability(normalized or non-normalized), class, 
+#' Returns a list of results for betas, spat_unit and feat_type. Result for
+#' each beta is a list with probability(normalized or non-normalized), class,
 #' and model log-likelihood value.
+#' @examples
+#' g <- GiottoData::loadGiottoMini("visium")
+#' g <- binSpect(g, return_gobject = TRUE)
+#' HMRF_init_obj <- initHMRF_V2(gobject = g, cl.method = "km")
+#'
+#' doHMRF_V2(HMRF_init_obj = HMRF_init_obj, betas = c(0, 5, 2))
 #' @export
 doHMRF_V2 <- function(HMRF_init_obj, betas = NULL) {
     message(
-    "If used in published research, please cite:
+        "If used in published research, please cite:
     Q Zhu, S Shah, R Dries, L Cai, GC Yuan.
-    'Identification of spatially associated subpopulations by combining 
+    'Identification of spatially associated subpopulations by combining
     scRNAseq and sequential fluorescence in situ hybridization data'
     Nature biotechnology 36 (12), 1183-1190. 2018"
     )
 
-    message("Please find more explanation and instruction of the HMRF function 
+    message("Please find more explanation and instruction of the HMRF function
     on \n https://bitbucket.org/qzhudfci/smfishhmrf-r/src/master/TRANSITION.md")
     if (!"y" %in% names(HMRF_init_obj)) {
         stop("expression matrix 'y' not in the intialization object")
@@ -1722,7 +1913,7 @@ doHMRF_V2 <- function(HMRF_init_obj, betas = NULL) {
         stop("initial mean vector 'mu' not in the intialization object")
     }
     if (!"sigma" %in% names(HMRF_init_obj)) {
-        stop("initial covariance matrix 'sigma' not in the intialization 
+        stop("initial covariance matrix 'sigma' not in the intialization
             object")
     }
     if (!"k" %in% names(HMRF_init_obj)) {
@@ -1749,14 +1940,17 @@ doHMRF_V2 <- function(HMRF_init_obj, betas = NULL) {
     if (is.null(betas)) {
         beta_seq <- max(ceiling(ncol(y) / 10), 2)
         cat(paste0("Default value beta = ", beta_seq, " is used..."))
-    } else if (length(betas) != 3 || (sum(betas[1:3] < 0) > 0)) {
-        stop("please provide betas as a vector of 3 non-negative numbers 
-            (initial value, nicrement, total iteration number)")
+    } else if (length(betas) != 3 || (sum(betas[seq_len(3)] < 0) > 0)) {
+        stop(wrap_txt(
+            "please provide betas as a vector of 3 non-negative numbers
+            (initial value, increment, total iteration number)",
+            errWidth = TRUE
+        ))
     } else {
         beta_init <- betas[1]
         beta_increment <- betas[2]
         beta_num_iter <- betas[3]
-        beta_seq <- (1:beta_num_iter - 1) * beta_increment + beta_init
+        beta_seq <- (seq_len(beta_num_iter) - 1) * beta_increment + beta_init
         beta_seq <- sort(unique(c(0, beta_seq)))
     }
 
@@ -1765,23 +1959,23 @@ doHMRF_V2 <- function(HMRF_init_obj, betas = NULL) {
         print(sprintf("Doing beta=%.3f", beta_current))
         tc.hmrfem <- smfishHmrf::smfishHmrf.hmrfem.multi(
             y = y, neighbors = nei,
-            beta = beta_current, 
-            numnei = numnei, 
+            beta = beta_current,
+            numnei = numnei,
             blocks = blocks,
-            mu = mu, 
-            sigma = sigma, 
-            verbose = TRUE, 
+            mu = mu,
+            sigma = sigma,
+            verbose = TRUE,
             err = 1e-07,
-            maxit = 50, 
+            maxit = 50,
             dampFactor = damp
         )
-        
+
         t_key <- sprintf("k=%d b=%.2f", k, beta_current)
         tc.hmrfem$sigma <- NULL
         tc.hmrfem$mu <- NULL
         rownames(tc.hmrfem$prob) <- rownames(y)
         rownames(tc.hmrfem$unnormprob) <- rownames(y)
-        names(tc.hmrfem$class) <- rownames(y)
+        # names(tc.hmrfem$class) <- rownames(y)
         res[[t_key]] <- tc.hmrfem
     }
     result.hmrf <- res
@@ -1800,10 +1994,17 @@ doHMRF_V2 <- function(HMRF_init_obj, betas = NULL) {
 #' @param name name of HMRF models
 #' @returns giotto object
 #' @details
-#' This function appends HMRF domain clusters to corresponding cell meta data 
-#' for all the beta values, with the given HMRF model names. For example, if 
-#' name = ‘hmrf1’ and name of result in HMRFoutput is ‘k=8 b=0.00’, the 
+#' This function appends HMRF domain clusters to corresponding cell meta data
+#' for all the beta values, with the given HMRF model names. For example, if
+#' name = ‘hmrf1’ and name of result in HMRFoutput is ‘k=8 b=0.00’, the
 #' appended cell meta data column will be named with ‘hmrf1 k=8 b=0.00’
+#' @examples
+#' g <- GiottoData::loadGiottoMini("visium")
+#' g <- binSpect(g, return_gobject = TRUE)
+#' HMRF_init_obj <- initHMRF_V2(gobject = g, cl.method = "km")
+#' HMRFoutput <- doHMRF_V2(HMRF_init_obj = HMRF_init_obj, betas = c(0, 5, 2))
+#'
+#' addHMRF_V2(gobject = g, HMRFoutput = HMRFoutput)
 #' @export
 addHMRF_V2 <- function(gobject, HMRFoutput, name = "hmrf") {
     if (!"HMRFoutput" %in% class(HMRFoutput)) {
@@ -1833,10 +2034,11 @@ addHMRF_V2 <- function(gobject, HMRFoutput, name = "hmrf") {
             spat_unit = spat_unit,
             feat_type = feat_type,
             column_cell_ID = "cell_ID",
-            new_metadata = HMRFoutput[[i]]$class[match(
-                ordered_cell_IDs, names(HMRFoutput[[i]]$class))],
-            vector_name = paste(name, names(HMRFoutput)[i]),
-            by_column = TRUE
+            # new_metadata = HMRFoutput[[i]]$class[match(
+            #     ordered_cell_IDs, names(HMRFoutput[[i]]$class))],
+            new_metadata = HMRFoutput[[i]]$prob[ordered_cell_IDs, ],
+            vector_name = paste(name, names(HMRFoutput)[i])
+            # by_column = TRUE
         )
     }
     return(gobject)
@@ -1866,40 +2068,45 @@ addHMRF_V2 <- function(gobject, HMRFoutput, name = "hmrf") {
 #' @param \dots additional params to pass to plotting
 #' @returns spatial plots with HMRF domains
 #' @details
-#' This function plots spatial map of HMRF domain clusters for multiple beta 
+#' This function plots spatial map of HMRF domain clusters for multiple beta
 #' with the name (hmrf_name),
-#' matching the first part of the cell meta column names with HMRF clusters 
+#' matching the first part of the cell meta column names with HMRF clusters
 #' (for example name of ‘hmrf1 k=8 b=0.00’ is ‘hmrf1’)
 #' @export
 viewHMRFresults_V2 <-
-    function(gobject, k, betas,
-    hmrf_name,
-    spat_unit = NULL,
-    feat_type = NULL,
-    third_dim = FALSE,
-    cow_n_col = 2,
-    cow_rel_h = 1,
-    cow_rel_w = 1,
-    cow_align = "h",
-    show_plot = TRUE,
-    save_plot = TRUE,
-    return_plot = TRUE,
-    default_save_name = "HMRF_result",
-    save_param = list(),
-    ...) {
+    function(
+        gobject, k, betas,
+        hmrf_name,
+        spat_unit = NULL,
+        feat_type = NULL,
+        third_dim = FALSE,
+        cow_n_col = 2,
+        cow_rel_h = 1,
+        cow_rel_w = 1,
+        cow_align = "h",
+        show_plot = TRUE,
+        save_plot = TRUE,
+        return_plot = TRUE,
+        default_save_name = "HMRF_result",
+        save_param = list(),
+        ...) {
         # beta_seq = round(betas,digits = 2)
         # t_key = paste0(hmrf_name,'_k', k, '_b.',beta_seq)
         t_key <- paste(hmrf_name, sprintf("k=%d b=%.2f", k, betas))
 
         meta_names <- colnames(combineMetadata(
-            gobject = gobject, spat_unit = spat_unit, feat_type = feat_type))
+            gobject = gobject, spat_unit = spat_unit, feat_type = feat_type
+        ))
 
         if (length(setdiff(t_key, meta_names)) > 0) {
-            beta_null <- paste(betas[which(!t_key %in% meta_names)], 
-                            collapse = ",")
-            stop(paste0('\n HMRF result "', hmrf_name, '" of k = ', k, 
-                        ", beta = ", beta_null, 
-                        " was not found in the Giotto object."))
+            beta_null <- paste(betas[which(!t_key %in% meta_names)],
+                collapse = ","
+            )
+            stop(paste0(
+                '\n HMRF result "', hmrf_name, '" of k = ', k,
+                ", beta = ", beta_null,
+                " was not found in the Giotto object."
+            ))
         }
 
         savelist <- list()
@@ -1907,32 +2114,32 @@ viewHMRFresults_V2 <-
         {
             if (third_dim == TRUE) {
                 pl <- spatPlot3D(
-                    gobject = gobject, 
-                    spat_unit = spat_unit, 
+                    gobject = gobject,
+                    spat_unit = spat_unit,
                     feat_type = feat_type,
-                    cell_color = t_key[kk], 
-                    show_plot = FALSE, 
-                    save_plot = FALSE, 
+                    cell_color = t_key[kk],
+                    show_plot = FALSE,
+                    save_plot = FALSE,
                     title = t_key[kk],
-                    default_save_name = "HMRF_result", 
-                    return_plot = TRUE, 
+                    default_save_name = "HMRF_result",
+                    return_plot = TRUE,
                     ...
                 )
             } else {
                 pl <- spatPlot2D(
-                    gobject = gobject, 
-                    spat_unit = spat_unit, 
+                    gobject = gobject,
+                    spat_unit = spat_unit,
                     feat_type = feat_type,
-                    cell_color = t_key[kk], 
-                    show_plot = FALSE, 
-                    save_plot = FALSE, 
+                    cell_color = t_key[kk],
+                    show_plot = FALSE,
+                    save_plot = FALSE,
                     title = t_key[kk],
-                    cow_n_col = 1, 
-                    cow_rel_h = 1, 
-                    cow_rel_w = 1, 
+                    cow_n_col = 1,
+                    cow_rel_h = 1,
+                    cow_rel_w = 1,
                     cow_align = "h",
-                    default_save_name = "HMRF_result", 
-                    return_plot = TRUE, 
+                    default_save_name = "HMRF_result",
+                    return_plot = TRUE,
                     ...
                 )
             }
diff --git a/R/python_scrublet.R b/R/python_scrublet.R
index 200305ec1..0f422ceae 100644
--- a/R/python_scrublet.R
+++ b/R/python_scrublet.R
@@ -1,6 +1,7 @@
 #' @title doScrubletDetect
 #' @name doScrubletDetect
-#' @description run *scrublet* doublet detection for raw expression.
+#' @description Run *scrublet* doublet detection for raw expression. Intended
+#' for single cell data
 #' @param gobject giotto object containing expression data
 #' @param feat_type feature type
 #' @param spat_unit spatial unit
@@ -21,26 +22,32 @@
 #' @seealso This function wraps the python package scrublet
 #' \doi{10.1016/j.cels.2018.11.005}
 #' @returns if `return_gobject = FALSE`, a `data.table` cell_ID, doublet scores,
-#' and classifications are returned. If `TRUE`, that information is appended 
-#' into the input `giotto` object's metadata and the `giotto` object is 
+#' and classifications are returned. If `TRUE`, that information is appended
+#' into the input `giotto` object's metadata and the `giotto` object is
 #' returned.
 #' @md
 #' @examples
+#' # Should only be done with single cell data, but this is just a
+#' # convenient example.
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' doScrubletDetect(g)
+#'
+#' g <- doScrubletDetect(g)
+#'
+#' pDataDT(g) # doublet_scores and doublet cols are added
+#' dimPlot2D(g, cell_color = "doublet_scores", color_as_factor = FALSE)
 #' @export
-doScrubletDetect <- function(gobject,
-    feat_type = NULL,
-    spat_unit = "cell",
-    expression_values = "raw",
-    expected_doublet_rate = 0.06,
-    min_counts = 1,
-    min_cells = 1,
-    min_gene_variability_pctl = 85,
-    n_prin_comps = 30,
-    return_gobject = TRUE,
-    seed = 1234) {
+doScrubletDetect <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = "cell",
+        expression_values = "raw",
+        expected_doublet_rate = 0.06,
+        min_counts = 1,
+        min_cells = 1,
+        min_gene_variability_pctl = 85,
+        n_prin_comps = 30,
+        return_gobject = TRUE,
+        seed = 1234) {
     # verify if optional package is installed
     package_check(
         pkg_name = "scrublet",
@@ -48,10 +55,10 @@ doScrubletDetect <- function(gobject,
     )
 
     # print message with information #
-    message("using 'scrublet' to detect doublets. If used in published 
+    message("using 'scrublet' to detect doublets. If used in published
     research, please cite: \n
     Wolock, S. L., Lopez, R. & Klein, A. M.
-    Scrublet: Computational Identification of Cell Doublets in Single-Cell 
+    Scrublet: Computational Identification of Cell Doublets in Single-Cell
     Transcriptomic Data. Cell Syst. 8, 281-291.e9 (2019).
     https://doi.org/10.1016/j.cels.2018.11.005")
 
@@ -59,7 +66,9 @@ doScrubletDetect <- function(gobject,
     python_path <- readGiottoInstructions(gobject, param = "python_path")
     reticulate::use_python(required = TRUE, python = python_path)
     python_scrublet_function <- system.file(
-        "python", "python_scrublet.py", package = "Giotto")
+        "python", "python_scrublet.py",
+        package = "Giotto"
+    )
     reticulate::source_python(file = python_scrublet_function, convert = TRUE)
 
     # set seed
diff --git a/R/spatial_clusters.R b/R/spatial_clusters.R
index b9fef3f44..ca7fbd6cb 100644
--- a/R/spatial_clusters.R
+++ b/R/spatial_clusters.R
@@ -77,12 +77,13 @@
 #' # don't show legend since there are too many categories generated
 #' spatPlot2D(g, cell_color = "new", show_legend = FALSE)
 #' @export
-spatialSplitCluster <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    spatial_network_name = "Delaunay_network",
-    cluster_col,
-    split_clus_name = paste0(cluster_col, "_split")) {
+spatialSplitCluster <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        spatial_network_name = "Delaunay_network",
+        cluster_col,
+        split_clus_name = paste0(cluster_col, "_split")) {
     # NSE vars
     cell_ID <- NULL
 
@@ -113,14 +114,15 @@ spatialSplitCluster <- function(gobject,
         verbose = FALSE,
     )
 
-    clus_info <- cell_meta[, c("cell_ID", cluster_col), with = FALSE] 
+    clus_info <- cell_meta[, c("cell_ID", cluster_col), with = FALSE]
     # subset to needed cols
-    g <- GiottoClass::spat_net_to_igraph(sn) 
+    g <- GiottoClass::spat_net_to_igraph(sn)
     # convert spatialNetworkObject to igraph
 
     # assign cluster info to igraph nodes
     clus_values <- clus_info[
-        match(igraph::V(g)$name, cell_ID), get(cluster_col)]
+        match(igraph::V(g)$name, cell_ID), get(cluster_col)
+    ]
     igraph::V(g)$cluster <- clus_values
 
     # split cluster by spatial igraph
diff --git a/R/spatial_enrichment.R b/R/spatial_enrichment.R
index f3082a796..1f95d9a53 100644
--- a/R/spatial_enrichment.R
+++ b/R/spatial_enrichment.R
@@ -14,18 +14,29 @@
 #' @returns matrix
 #' @seealso \code{\link{PAGEEnrich}}
 #' @examples
-#' sign_list <- list(cell_type1 = c("Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c",
-#' "Rims2", "Gfap", "Gjc3", "Chrna4", "Prkcd"),
-#' cell_type2 = c("Prr18", "Grb14", "Tprn", "Clic1", "Olig2", "Hrh3", "Tmbim1",
-#' "Carhsp1", "Tmem88b", "Ugt8a"),
-#' cell_type2 = c("Arpp19", "Lamp5", "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4",
-#' "Cygb", "Ttc9b","Ipcef1"))
+#' sign_list <- list(
+#'     cell_type1 = c(
+#'         "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c",
+#'         "Rims2", "Gfap", "Gjc3", "Chrna4", "Prkcd"
+#'     ),
+#'     cell_type2 = c(
+#'         "Prr18", "Grb14", "Tprn", "Clic1", "Olig2", "Hrh3", "Tmbim1",
+#'         "Carhsp1", "Tmem88b", "Ugt8a"
+#'     ),
+#'     cell_type2 = c(
+#'         "Arpp19", "Lamp5", "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4",
+#'         "Cygb", "Ttc9b", "Ipcef1"
+#'     )
+#' )
 #'
-#' makeSignMatrixPAGE(sign_names = c("cell_type1", "cell_type2", "cell_type3"),
-#' sign_list = sign_list)
+#' makeSignMatrixPAGE(
+#'     sign_names = c("cell_type1", "cell_type2", "cell_type3"),
+#'     sign_list = sign_list
+#' )
 #' @export
-makeSignMatrixPAGE <- function(sign_names,
-    sign_list) {
+makeSignMatrixPAGE <- function(
+        sign_names,
+        sign_list) {
     ## check input
     if (!inherits(sign_list, "list")) {
         stop("sign_list needs to be a list of signatures for each cell type /
@@ -45,11 +56,14 @@ makeSignMatrixPAGE <- function(sign_names,
         res <- rep(x = name_subset, length(subset))
     })
     mydt <- data.table::data.table(
-        genes = genes, types = unlist(types), value = 1)
+        genes = genes, types = unlist(types), value = 1
+    )
 
     # convert data.table to signature matrix
     dtmatrix <- data.table::dcast.data.table(
-        mydt, formula = genes ~ types, value.var = "value", fill = 0)
+        mydt,
+        formula = genes ~ types, value.var = "value", fill = 0
+    )
     final_sig_matrix <- Matrix::as.matrix(dtmatrix[, -1])
     rownames(final_sig_matrix) <- dtmatrix$genes
 
@@ -70,21 +84,26 @@ makeSignMatrixPAGE <- function(sign_names,
 #' @returns matrix
 #' @seealso \code{\link{runDWLSDeconv}}
 #' @examples
-#' sign_gene <- c("Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
-#' "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
-#' "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
-#' "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b","Ipcef1")
+#' sign_gene <- c(
+#'     "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
+#'     "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
+#'     "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
+#'     "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1"
+#' )
 #'
-#' sign_matrix <- matrix(rnorm(length(sign_gene)*3), nrow = length(sign_gene))
+#' sign_matrix <- matrix(rnorm(length(sign_gene) * 3), nrow = length(sign_gene))
 #' rownames(sign_matrix) <- sign_gene
 #' colnames(sign_matrix) <- c("cell_type1", "cell_type2", "cell_type3")
 #'
-#' makeSignMatrixDWLSfromMatrix(matrix = sign_matrix, sign_gene = sign_gene,
-#' cell_type_vector = c("cell_type1", "cell_type2", "cell_type3"))
+#' makeSignMatrixDWLSfromMatrix(
+#'     matrix = sign_matrix, sign_gene = sign_gene,
+#'     cell_type_vector = c("cell_type1", "cell_type2", "cell_type3")
+#' )
 #' @export
-makeSignMatrixDWLSfromMatrix <- function(matrix,
-    sign_gene,
-    cell_type_vector) {
+makeSignMatrixDWLSfromMatrix <- function(
+        matrix,
+        sign_gene,
+        cell_type_vector) {
     # 1. check if cell_type_vector and matrix are compatible
     if (ncol(matrix) != length(cell_type_vector)) {
         stop("ncol(matrix) needs to be the same as length(cell_type_vector)")
@@ -114,7 +133,8 @@ makeSignMatrixDWLSfromMatrix <- function(matrix,
         cell_type <- unique(cell_type_vector)[cell_type_i]
         selected_cells <- colnames(matrix_subset)[cell_type_vector == cell_type]
         mean_expr_in_selected_cells <- rowMeans_flex(matrix_subset[
-            , selected_cells])
+            , selected_cells
+        ])
 
         signMatrix[, cell_type_i] <- mean_expr_in_selected_cells
     }
@@ -145,23 +165,28 @@ makeSignMatrixDWLSfromMatrix <- function(matrix,
 #' @seealso \code{\link{runDWLSDeconv}}
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' sign_gene <- c("Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
-#' "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
-#' "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
-#' "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b","Ipcef1")
+#' sign_gene <- c(
+#'     "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
+#'     "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
+#'     "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
+#'     "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1"
+#' )
 #'
-#' makeSignMatrixDWLS(gobject = g, sign_gene = sign_gene,
-#' cell_type_vector = pDataDT(g)[["leiden_clus"]])
+#' makeSignMatrixDWLS(
+#'     gobject = g, sign_gene = sign_gene,
+#'     cell_type_vector = pDataDT(g)[["leiden_clus"]]
+#' )
 #' @export
-makeSignMatrixDWLS <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    reverse_log = TRUE,
-    log_base = 2,
-    sign_gene,
-    cell_type_vector,
-    cell_type = NULL) {
+makeSignMatrixDWLS <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        reverse_log = TRUE,
+        log_base = 2,
+        sign_gene,
+        cell_type_vector,
+        cell_type = NULL) {
     ## deprecated arguments
     if (!is.null(cell_type)) {
         warning("cell_type is deprecated, use cell_type_vector in the future")
@@ -182,8 +207,9 @@ makeSignMatrixDWLS <- function(gobject,
 
     ## 1. expression matrix
     values <- match.arg(
-      expression_values,
-      unique(c("normalized", "scaled", "custom", expression_values)))
+        expression_values,
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_values <- get_expression_values(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -223,22 +249,27 @@ makeSignMatrixDWLS <- function(gobject,
 #' @returns matrix
 #' @seealso \code{\link{rankEnrich}}
 #' @examples
-#' sign_gene <- c("Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
-#' "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
-#' "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
-#' "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b","Ipcef1")
+#' sign_gene <- c(
+#'     "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
+#'     "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
+#'     "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
+#'     "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1"
+#' )
 #'
-#' sign_matrix <- matrix(rnorm(length(sign_gene)*3), nrow = length(sign_gene))
+#' sign_matrix <- matrix(rnorm(length(sign_gene) * 3), nrow = length(sign_gene))
 #' rownames(sign_matrix) <- sign_gene
 #' colnames(sign_matrix) <- c("cell_type1", "cell_type2", "cell_type3")
 #'
-#' makeSignMatrixRank(sc_matrix = sign_matrix,
-#' sc_cluster_ids = c("cell_type1", "cell_type2", "cell_type3"))
+#' makeSignMatrixRank(
+#'     sc_matrix = sign_matrix,
+#'     sc_cluster_ids = c("cell_type1", "cell_type2", "cell_type3")
+#' )
 #' @export
-makeSignMatrixRank <- function(sc_matrix,
-    sc_cluster_ids,
-    ties_method = c("random", "max"),
-    gobject = NULL) {
+makeSignMatrixRank <- function(
+        sc_matrix,
+        sc_cluster_ids,
+        ties_method = c("random", "max"),
+        gobject = NULL) {
     if (inherits(sc_matrix, "exprObj")) {
         sc_matrix <- sc_matrix[]
     }
@@ -293,14 +324,18 @@ makeSignMatrixRank <- function(sc_matrix,
     # calculate fold change and rank of fold-change
     comb_dt[, fold := log2(mean_expr + 1) - log2(av_expr + 1)]
     comb_dt[, rankFold := data.table::frank(
-        -fold, ties.method = ties_method), by = clusters]
+        -fold,
+        ties.method = ties_method
+    ), by = clusters]
 
     # create matrix
     comb_rank_mat <- data.table::dcast.data.table(
-        data = comb_dt, genes ~ clusters, value.var = "rankFold")
+        data = comb_dt, genes ~ clusters, value.var = "rankFold"
+    )
     comb_rank_matrix <- dt_to_matrix(comb_rank_mat)
     comb_rank_matrix <- comb_rank_matrix[
-        rownames(sc_matrix), unique(sc_cluster_ids)]
+        rownames(sc_matrix), unique(sc_cluster_ids)
+    ]
 
     return(comb_rank_matrix)
 }
@@ -315,19 +350,22 @@ makeSignMatrixRank <- function(sc_matrix,
 #' @description creates permutation for the PAGEEnrich test
 #' @returns PAGEEnrich test
 #' @keywords internal
-.do_page_permutation <- function(gobject,
-    sig_gene,
-    ntimes) {
+.do_page_permutation <- function(
+        gobject,
+        sig_gene,
+        ntimes) {
     # check available gene
     available_ct <- c()
     for (i in colnames(sig_gene)) {
         gene_i <- rownames(sig_gene)[which(sig_gene[, i] == 1)]
         overlap_i <- intersect(
-            gene_i, rownames(gobject@expression$rna$normalized))
+            gene_i, rownames(gobject@expression$rna$normalized)
+        )
         if (length(overlap_i) <= 5) {
             output <- paste0(
                 "Warning, ", i, " only has ", length(overlap_i),
-                " overlapped genes. Will remove it.")
+                " overlapped genes. Will remove it."
+            )
         } else {
             available_ct <- c(available_ct, i)
         }
@@ -337,34 +375,38 @@ makeSignMatrixRank <- function(sc_matrix,
     }
     # only continue with genes present in both datasets
     interGene <- intersect(
-        rownames(sig_gene), rownames(gobject@expression$rna$normalized))
+        rownames(sig_gene), rownames(gobject@expression$rna$normalized)
+    )
     sign_matrix <- sig_gene[interGene, available_ct]
 
     ct_gene_counts <- NULL
-    for (i in 1:dim(sign_matrix)[2]) {
+    for (i in seq_len(dim(sign_matrix)[2])) {
         a <- length(which(sign_matrix[, i] == 1))
         ct_gene_counts <- c(ct_gene_counts, a)
     }
     uniq_ct_gene_counts <- unique(ct_gene_counts)
     background_mean_sd <- matrix(
-        data = NA, nrow = length(uniq_ct_gene_counts) + 1, ncol = 3)
+        data = NA, nrow = length(uniq_ct_gene_counts) + 1, ncol = 3
+    )
     for (i in seq_along(uniq_ct_gene_counts)) {
         gene_num <- uniq_ct_gene_counts[i]
         all_sample_names <- NULL
         all_sample_list <- NULL
-        for (j in 1:ntimes) {
+        for (j in seq_len(ntimes)) {
             set.seed(j)
             random_gene <- sample(rownames(
-                gobject@expression$rna$normalized), gene_num, replace = FALSE)
+                gobject@expression$rna$normalized
+            ), gene_num, replace = FALSE)
             ct_name <- paste("ct", j, sep = "")
             all_sample_names <- c(all_sample_names, ct_name)
             all_sample_list <- c(all_sample_list, list(random_gene))
         }
         random_sig <- makeSignMatrixPAGE(all_sample_names, all_sample_list)
         random_DT <- runPAGEEnrich(
-          gobject,
-          sign_matrix = random_sig,
-          p_value = FALSE)
+            gobject,
+            sign_matrix = random_sig,
+            p_value = FALSE
+        )
         background <- unlist(random_DT[, 2:dim(random_DT)[2]])
         df_row_name <- paste("gene_num_", uniq_ct_gene_counts[i], sep = "")
         list_back_i <- c(df_row_name, mean(background), stats::sd(background))
@@ -407,16 +449,17 @@ makeSignMatrixRank <- function(sc_matrix,
 #' gene set.
 #' @seealso \code{\link{makeSignMatrixPAGE}}
 #' @export
-runPAGEEnrich_OLD <- function(gobject,
-    sign_matrix,
-    expression_values = c("normalized", "scaled", "custom"),
-    reverse_log_scale = TRUE,
-    logbase = 2,
-    output_enrichment = c("original", "zscore"),
-    p_value = FALSE,
-    n_times = 1000,
-    name = NULL,
-    return_gobject = TRUE) {
+runPAGEEnrich_OLD <- function(
+        gobject,
+        sign_matrix,
+        expression_values = c("normalized", "scaled", "custom"),
+        reverse_log_scale = TRUE,
+        logbase = 2,
+        output_enrichment = c("original", "zscore"),
+        p_value = FALSE,
+        n_times = 1000,
+        name = NULL,
+        return_gobject = TRUE) {
     # expression values to be used
     values <- match.arg(expression_values, c("normalized", "scaled", "custom"))
     expr_values <- get_expression_values(gobject = gobject, values = values)
@@ -432,7 +475,8 @@ runPAGEEnrich_OLD <- function(gobject,
         if (length(overlap_i) <= 5) {
             output <- paste0(
                 "Warning, ", i, " only has ", length(overlap_i),
-                " overlapped genes. Will remove it.")
+                " overlapped genes. Will remove it."
+            )
         } else {
             available_ct <- c(available_ct, i)
         }
@@ -444,7 +488,9 @@ runPAGEEnrich_OLD <- function(gobject,
 
     # output enrichment
     output_enrichment <- match.arg(
-        output_enrichment, choices = c("original", "zscore"))
+        output_enrichment,
+        choices = c("original", "zscore")
+    )
 
     # only continue with genes present in both datasets
     interGene <- intersect(rownames(sign_matrix), rownames(expr_values))
@@ -465,8 +511,9 @@ runPAGEEnrich_OLD <- function(gobject,
 
     # get enrichment scores
     enrichment <- matrix(
-        data = NA, nrow = dim(filterSig)[2], ncol = length(cellColMean))
-    for (i in (1:dim(filterSig)[2])) {
+        data = NA, nrow = dim(filterSig)[2], ncol = length(cellColMean)
+    )
+    for (i in seq_len(dim(filterSig)[2])) {
         signames <- rownames(filterSig)[which(filterSig[, i] == 1)]
         sigColMean <- apply(geneFold[signames, ], 2, mean)
         m <- length(signames)
@@ -501,12 +548,14 @@ runPAGEEnrich_OLD <- function(gobject,
         for (i in colnames(sign_matrix)) {
             gene_i <- rownames(sign_matrix)[which(sign_matrix[, i] == 1)]
             overlap_i <- intersect(
-                gene_i, rownames(gobject@expression$rna$normalized))
+                gene_i, rownames(gobject@expression$rna$normalized)
+            )
 
             if (length(overlap_i) <= 5) {
                 output <- paste0(
                     "Warning, ", i, " only has ", length(overlap_i),
-                    " overlapped genes. It will be removed.")
+                    " overlapped genes. It will be removed."
+                )
             } else {
                 available_ct <- c(available_ct, i)
             }
@@ -518,7 +567,8 @@ runPAGEEnrich_OLD <- function(gobject,
 
         # only continue with genes present in both datasets
         interGene <- intersect(
-            rownames(sign_matrix), rownames(gobject@expression$rna$normalized))
+            rownames(sign_matrix), rownames(gobject@expression$rna$normalized)
+        )
         filter_sign_matrix <- sign_matrix[interGene, available_ct]
 
         background_mean_sd <- .do_page_permutation(
@@ -527,17 +577,21 @@ runPAGEEnrich_OLD <- function(gobject,
             ntimes = n_times
         )
 
-        for (i in 1:dim(filter_sign_matrix)[2]) {
+        for (i in seq_len(dim(filter_sign_matrix)[2])) {
             length_gene <- length(which(filter_sign_matrix[, i] == 1))
             join_gene_with_length <- paste("gene_num_", length_gene, sep = "")
             mean_i <- as.numeric(as.character(
-                background_mean_sd[join_gene_with_length, ][[1]]))
+                background_mean_sd[join_gene_with_length, ][[1]]
+            ))
             sd_i <- as.numeric(as.character(
-                background_mean_sd[join_gene_with_length, ][[2]]))
+                background_mean_sd[join_gene_with_length, ][[2]]
+            ))
             j <- i + 1
             enrichmentDT[[j]] <- stats::pnorm(
-                enrichmentDT[[j]], mean = mean_i, sd = sd_i,
-                lower.tail = FALSE, log.p = FALSE)
+                enrichmentDT[[j]],
+                mean = mean_i, sd = sd_i,
+                lower.tail = FALSE, log.p = FALSE
+            )
         }
     }
 
@@ -584,17 +638,18 @@ runPAGEEnrich_OLD <- function(gobject,
 #' @param expr_values matrix of expression values
 #' @returns data.table
 #' @keywords internal
-.page_dt_method <- function(sign_matrix,
-    expr_values,
-    min_overlap_genes = 5,
-    logbase = 2,
-    reverse_log_scale = TRUE,
-    output_enrichment = c("original", "zscore"),
-    p_value = FALSE,
-    include_depletion = FALSE,
-    n_times = 1000,
-    max_block = 20e6,
-    verbose = TRUE) {
+.page_dt_method <- function(
+        sign_matrix,
+        expr_values,
+        min_overlap_genes = 5,
+        logbase = 2,
+        reverse_log_scale = TRUE,
+        output_enrichment = c("original", "zscore"),
+        p_value = FALSE,
+        include_depletion = FALSE,
+        n_times = 1000,
+        max_block = 20e6,
+        verbose = TRUE) {
     # data.table variables
     Var1 <- value <- Var2 <- V1 <- marker <- nr_markers <- fc <- cell_ID <-
         zscore <- colmean <- colSd <- pval <- NULL
@@ -602,7 +657,9 @@ runPAGEEnrich_OLD <- function(gobject,
 
     # output enrichment
     output_enrichment <- match.arg(
-      output_enrichment, choices = c("original", "zscore"))
+        output_enrichment,
+        choices = c("original", "zscore")
+    )
 
     ## identify available cell types
     all_genes <- rownames(expr_values)
@@ -613,11 +670,12 @@ runPAGEEnrich_OLD <- function(gobject,
 
     lost_cell_types_DT <- detected_DT[V1 <= min_overlap_genes]
     if (nrow(lost_cell_types_DT) > 0) {
-        for (row in 1:nrow(lost_cell_types_DT)) {
+        for (row in seq_len(nrow(lost_cell_types_DT))) {
             output <- paste0(
-              "Warning, ", lost_cell_types_DT[row][["Var2"]], " only has ",
-              lost_cell_types_DT[row][["V1"]],
-              " overlapping genes. Will be removed.")
+                "Warning, ", lost_cell_types_DT[row][["Var2"]], " only has ",
+                lost_cell_types_DT[row][["V1"]],
+                " overlapping genes. Will be removed."
+            )
             if (verbose) print(output)
         }
     }
@@ -659,13 +717,17 @@ runPAGEEnrich_OLD <- function(gobject,
     colnames(geneFold_DT) <- c("gene", "cell_ID", "fc")
 
     mergetest <- data.table::merge.data.table(
-      sub_ct_DT, geneFold_DT, by = "gene")
+        sub_ct_DT, geneFold_DT,
+        by = "gene"
+    )
     mergetest <- mergetest[, mean(fc), by = .(cell_type, cell_ID, nr_markers)]
     if (is.integer(mergetest$cell_ID) && is.character(cellColMeanSd$cell_ID)) {
         mergetest$cell_ID <- as.character(mergetest$cell_ID)
     }
     mergetest <- data.table::merge.data.table(
-      mergetest, cellColMeanSd, by = "cell_ID")
+        mergetest, cellColMeanSd,
+        by = "cell_ID"
+    )
     mergetest[, zscore := ((V1 - colmean) * nr_markers^(1 / 2)) / colSd]
 
     if (output_enrichment == "zscore") {
@@ -684,13 +746,13 @@ runPAGEEnrich_OLD <- function(gobject,
         ## 2. first create the random samples all together ##
         cell_type_list <- list()
         perm_type_list <- list()
-        for (row in 1:nrow(sample_intrs)) {
+        for (row in seq_len(nrow(sample_intrs))) {
             cell_type <- sample_intrs[row][["cell_type"]]
             nr_genes <- as.numeric(sample_intrs[row][["nr_markers"]])
 
             gene_list <- list()
             perm_list <- list()
-            for (i in 1:n_times) {
+            for (i in seq_len(n_times)) {
                 sampled_genes <- sample(rownames(expr_values), size = nr_genes)
                 gene_list[[i]] <- sampled_genes
                 perm_list[[i]] <- rep(paste0("p_", i), nr_genes)
@@ -728,9 +790,11 @@ runPAGEEnrich_OLD <- function(gobject,
         all_perms <- unique(perm_round)
         all_perms_num <- seq_along(all_perms)
         names(all_perms_num) <- all_perms
-        group_labels <- paste0("group_", 1:nr_groups)
+        group_labels <- paste0("group_", seq_len(nr_groups))
         groups_vec <- cut(
-            all_perms_num, breaks = nr_groups, labels = group_labels)
+            all_perms_num,
+            breaks = nr_groups, labels = group_labels
+        )
         names(all_perms) <- groups_vec
 
 
@@ -742,16 +806,24 @@ runPAGEEnrich_OLD <- function(gobject,
             cell_type_perm_DT_sub <- cell_type_perm_DT[round %in% sub_perms]
 
             mergetest_perm_sub <- data.table::merge.data.table(
-                cell_type_perm_DT_sub, geneFold_DT, allow.cartesian = TRUE)
+                cell_type_perm_DT_sub, geneFold_DT,
+                allow.cartesian = TRUE
+            )
             mergetest_perm_sub <- mergetest_perm_sub[
-                , mean(fc), by = .(cell_type, cell_ID, nr_markers, round)]
+                , mean(fc),
+                by = .(cell_type, cell_ID, nr_markers, round)
+            ]
             if (is.integer(mergetest_perm_sub$cell_ID) && is.character(
-                cellColMeanSd$cell_ID)) {
+                cellColMeanSd$cell_ID
+            )) {
                 mergetest_perm_sub$cell_ID <- as.character(
-                    mergetest_perm_sub$cell_ID)
+                    mergetest_perm_sub$cell_ID
+                )
             }
             mergetest_perm_sub <- data.table::merge.data.table(
-                mergetest_perm_sub, cellColMeanSd, by = "cell_ID")
+                mergetest_perm_sub, cellColMeanSd,
+                by = "cell_ID"
+            )
             mergetest_perm_sub[, zscore := ((
                 V1 - colmean) * nr_markers^(1 / 2)) / colSd]
 
@@ -761,19 +833,26 @@ runPAGEEnrich_OLD <- function(gobject,
         res_list_comb <- do.call("rbind", res_list)
         res_list_comb_average <- res_list_comb[
             , .(mean_zscore = mean(zscore), sd_zscore = stats::sd(zscore)),
-            by = c("cell_ID", "cell_type")]
+            by = c("cell_ID", "cell_type")
+        ]
         mergetest_final <- data.table::merge.data.table(
-            mergetest, res_list_comb_average, by = c("cell_ID", "cell_type"))
+            mergetest, res_list_comb_average,
+            by = c("cell_ID", "cell_type")
+        )
 
         ## calculate p.values based on normal distribution
         if (include_depletion == TRUE) {
             mergetest_final[, pval := stats::pnorm(
-                abs(zscore), mean = mean_zscore, sd = sd_zscore,
-                lower.tail = FALSE, log.p = FALSE)]
+                abs(zscore),
+                mean = mean_zscore, sd = sd_zscore,
+                lower.tail = FALSE, log.p = FALSE
+            )]
         } else {
             mergetest_final[, pval := stats::pnorm(
-                zscore, mean = mean_zscore, sd = sd_zscore,
-                lower.tail = FALSE, log.p = FALSE)]
+                zscore,
+                mean = mean_zscore, sd = sd_zscore,
+                lower.tail = FALSE, log.p = FALSE
+            )]
         }
 
         data.table::setorder(mergetest_final, pval)
@@ -787,12 +866,16 @@ runPAGEEnrich_OLD <- function(gobject,
 
 
         resultmatrix <- data.table::dcast(
-            mergetest_final, formula = cell_ID ~ cell_type,
-            value.var = "pval_score")
+            mergetest_final,
+            formula = cell_ID ~ cell_type,
+            value.var = "pval_score"
+        )
         return(list(DT = mergetest_final, matrix = resultmatrix))
     } else {
         resultmatrix <- data.table::dcast(
-            mergetest, formula = cell_ID ~ cell_type, value.var = "zscore")
+            mergetest,
+            formula = cell_ID ~ cell_type, value.var = "zscore"
+        )
         return(list(DT = mergetest, matrix = resultmatrix))
     }
 }
@@ -836,34 +919,38 @@ runPAGEEnrich_OLD <- function(gobject,
 #' @seealso \code{\link{makeSignMatrixPAGE}}
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' sign_gene <- c("Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap", 
-#' "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2", 
-#' "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5", 
-#' "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b","Ipcef1")
-#' 
-#' sign_matrix <- matrix(rnorm(length(sign_gene)*3, mean  = 10), 
-#' nrow = length(sign_gene))
+#' sign_gene <- c(
+#'     "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
+#'     "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
+#'     "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
+#'     "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1"
+#' )
+#'
+#' sign_matrix <- matrix(rnorm(length(sign_gene) * 3, mean = 10),
+#'     nrow = length(sign_gene)
+#' )
 #' rownames(sign_matrix) <- sign_gene
 #' colnames(sign_matrix) <- c("cell_type1", "cell_type2", "cell_type3")
-#' 
+#'
 #' runPAGEEnrich(gobject = g, sign_matrix = sign_matrix)
 #' @export
-runPAGEEnrich <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    sign_matrix,
-    expression_values = c("normalized", "scaled", "custom"),
-    min_overlap_genes = 5,
-    reverse_log_scale = TRUE,
-    logbase = 2,
-    output_enrichment = c("original", "zscore"),
-    p_value = FALSE,
-    include_depletion = FALSE,
-    n_times = 1000,
-    max_block = 20e6,
-    name = NULL,
-    verbose = TRUE,
-    return_gobject = TRUE) {
+runPAGEEnrich <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        sign_matrix,
+        expression_values = c("normalized", "scaled", "custom"),
+        min_overlap_genes = 5,
+        reverse_log_scale = TRUE,
+        logbase = 2,
+        output_enrichment = c("original", "zscore"),
+        p_value = FALSE,
+        include_depletion = FALSE,
+        n_times = 1000,
+        max_block = 20e6,
+        name = NULL,
+        verbose = TRUE,
+        return_gobject = TRUE) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -878,7 +965,8 @@ runPAGEEnrich <- function(gobject,
     # expression values to be used
     values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom"), expression_values))
+        unique(c("normalized", "scaled", "custom"), expression_values)
+    )
     expr_values <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -999,10 +1087,12 @@ PAGEEnrich <- function(...) {
 #' @keywords internal
 .do_rank_permutation <- function(sc_gene, n) {
     random_df <- data.frame(matrix(ncol = n, nrow = length(sc_gene)))
-    for (i in 1:n) {
+    for (i in seq_len(n)) {
         set.seed(i)
         random_rank <- sample(
-            seq_along(sc_gene), length(sc_gene), replace = FALSE)
+            seq_along(sc_gene), length(sc_gene),
+            replace = FALSE
+        )
         random_df[, i] <- random_rank
     }
     rownames(random_df) <- sc_gene
@@ -1044,33 +1134,38 @@ PAGEEnrich <- function(...) {
 #' @seealso \code{\link{makeSignMatrixRank}}
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' x <- findMarkers_one_vs_all(g, 
-#' cluster_column = "leiden_clus", min_feats = 20)
+#' x <- findMarkers_one_vs_all(g,
+#'     cluster_column = "leiden_clus", min_feats = 20
+#' )
 #' sign_gene <- x$feats
-#' 
-#' sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10), 
-#' nrow = length(sign_gene))
+#'
+#' sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+#'     nrow = length(sign_gene)
+#' )
 #' rownames(sign_matrix) <- sign_gene
-#' colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
-#' 
-#' runRankEnrich(gobject = g, sign_matrix = sign_matrix, 
-#' expression_values = "normalized")
+#' colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
+#'
+#' runRankEnrich(
+#'     gobject = g, sign_matrix = sign_matrix,
+#'     expression_values = "normalized"
+#' )
 #' @export
-runRankEnrich <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    sign_matrix,
-    expression_values = c("normalized", "raw", "scaled", "custom"),
-    reverse_log_scale = TRUE,
-    logbase = 2,
-    output_enrichment = c("original", "zscore"),
-    ties_method = c("average", "max"),
-    p_value = FALSE,
-    n_times = 1000,
-    rbp_p = 0.99,
-    num_agg = 100,
-    name = NULL,
-    return_gobject = TRUE) {
+runRankEnrich <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        sign_matrix,
+        expression_values = c("normalized", "raw", "scaled", "custom"),
+        reverse_log_scale = TRUE,
+        logbase = 2,
+        output_enrichment = c("original", "zscore"),
+        ties_method = c("average", "max"),
+        p_value = FALSE,
+        n_times = 1000,
+        rbp_p = 0.99,
+        num_agg = 100,
+        name = NULL,
+        return_gobject = TRUE) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -1088,7 +1183,8 @@ runRankEnrich <- function(gobject,
     # expression values to be used
     values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_values <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -1113,7 +1209,9 @@ runRankEnrich <- function(gobject,
 
     # output enrichment
     output_enrichment <- match.arg(
-        output_enrichment, choices = c("original", "zscore"))
+        output_enrichment,
+        choices = c("original", "zscore")
+    )
 
     enrichment <- matrix(
         data = NA,
@@ -1124,7 +1222,9 @@ runRankEnrich <- function(gobject,
     # calculate mean gene expression
     if (reverse_log_scale == TRUE) {
         mean_gene_expr <- log(Matrix::rowMeans(
-            logbase^expr_values[] - 1, dims = 1) + 1)
+            logbase^expr_values[] - 1,
+            dims = 1
+        ) + 1)
     } else {
         mean_gene_expr <- Matrix::rowMeans(expr_values[])
     }
@@ -1145,7 +1245,7 @@ runRankEnrich <- function(gobject,
     rownames(rankFold) <- rownames(expr_values[])
     colnames(rankFold) <- colnames(expr_values[])
 
-    for (i in (1:dim(sign_matrix)[2])) {
+    for (i in seq_len(dim(sign_matrix)[2])) {
         signames <- rownames(sign_matrix)[which(sign_matrix[, i] > 0)]
         interGene <- intersect(signames, rownames(rankFold))
         filterSig <- sign_matrix[interGene, ]
@@ -1156,9 +1256,9 @@ runRankEnrich <- function(gobject,
 
         vectorX <- rep(NA, dim(filterRankFold)[2])
 
-        for (j in (1:dim(filterRankFold)[2])) {
+        for (j in seq_len(dim(filterRankFold)[2])) {
             toprpb <- sort(rpb[, j], decreasing = TRUE)
-            zscore <- sum(toprpb[1:num_agg])
+            zscore <- sum(toprpb[seq_len(num_agg)])
             vectorX[j] <- zscore
         }
         enrichment[i, ] <- vectorX
@@ -1199,14 +1299,19 @@ runRankEnrich <- function(gobject,
 
         background <- unlist(random_DT[, 2:dim(random_DT)[2]])
         fit.gamma <- fitdistrplus::fitdist(
-            background, distr = "gamma", method = "mle")
+            background,
+            distr = "gamma", method = "mle"
+        )
         pvalue_DT <- enrichmentDT
         enrichmentDT[, 2:dim(enrichmentDT)[2]] <- lapply(
             enrichmentDT[, 2:dim(enrichmentDT)[2]], function(x) {
-            stats::pgamma(
-                x, fit.gamma$estimate[1], rate = fit.gamma$estimate[2],
-                lower.tail = FALSE, log.p = FALSE)
-        })
+                stats::pgamma(
+                    x, fit.gamma$estimate[1],
+                    rate = fit.gamma$estimate[2],
+                    lower.tail = FALSE, log.p = FALSE
+                )
+            }
+        )
     }
 
     # create spatial enrichment object
@@ -1230,7 +1335,8 @@ runRankEnrich <- function(gobject,
     ## return object or results ##
     if (return_gobject == TRUE) {
         spenr_names <- list_spatial_enrichments_names(
-          gobject = gobject, spat_unit = spat_unit, feat_type = feat_type)
+            gobject = gobject, spat_unit = spat_unit, feat_type = feat_type
+        )
 
 
         if (name %in% spenr_names) {
@@ -1308,28 +1414,31 @@ rankEnrich <- function(...) {
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #' x <- findMarkers_one_vs_all(g,
-#' cluster_column = "leiden_clus", min_feats = 20)
+#'     cluster_column = "leiden_clus", min_feats = 20
+#' )
 #' sign_gene <- x$feats
 #'
-#' sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10),
-#' nrow = length(sign_gene))
+#' sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+#'     nrow = length(sign_gene)
+#' )
 #' rownames(sign_matrix) <- sign_gene
-#' colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
+#' colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
 #'
 #' runHyperGeometricEnrich(gobject = g, sign_matrix = sign_matrix)
 #' @export
-runHyperGeometricEnrich <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    sign_matrix,
-    expression_values = c("normalized", "scaled", "custom"),
-    reverse_log_scale = TRUE,
-    logbase = 2,
-    top_percentage = 5,
-    output_enrichment = c("original", "zscore"),
-    p_value = FALSE,
-    name = NULL,
-    return_gobject = TRUE) {
+runHyperGeometricEnrich <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        sign_matrix,
+        expression_values = c("normalized", "scaled", "custom"),
+        reverse_log_scale = TRUE,
+        logbase = 2,
+        top_percentage = 5,
+        output_enrichment = c("original", "zscore"),
+        p_value = FALSE,
+        name = NULL,
+        return_gobject = TRUE) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -1343,7 +1452,8 @@ runHyperGeometricEnrich <- function(gobject,
 
     values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_values <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -1358,7 +1468,9 @@ runHyperGeometricEnrich <- function(gobject,
 
     # output enrichment
     output_enrichment <- match.arg(
-        output_enrichment, choices = c("original", "zscore"))
+        output_enrichment,
+        choices = c("original", "zscore")
+    )
 
     # calculate mean gene expression
     if (reverse_log_scale == TRUE) {
@@ -1375,7 +1487,9 @@ runHyperGeometricEnrich <- function(gobject,
 
     top_q <- 1 - top_percentage / 100
     quantilecut <- apply(
-        foldChange, 2, stats::quantile, probs = top_q, na.rm = TRUE)
+        foldChange, 2, stats::quantile,
+        probs = top_q, na.rm = TRUE
+    )
     expbinary <- t_flex(1 * t_flex(foldChange > quantilecut))
 
     markerGenes <- rownames(inter_sign_matrix)
@@ -1387,21 +1501,26 @@ runHyperGeometricEnrich <- function(gobject,
         ncol = dim(expbinaryOverlap)[2]
     )
 
-    for (i in (1:dim(inter_sign_matrix)[2])) {
+    for (i in seq_len(dim(inter_sign_matrix)[2])) {
         signames <- rownames(inter_sign_matrix)[
-            which(inter_sign_matrix[, i] == 1)]
+            which(inter_sign_matrix[, i] == 1)
+        ]
         vectorX <- NULL
 
-        for (j in (1:dim(expbinaryOverlap)[2])) {
+        for (j in seq_len(dim(expbinaryOverlap)[2])) {
             cellsiggene <- names(expbinaryOverlap[
-                which(expbinaryOverlap[, j] == 1), j])
+                which(expbinaryOverlap[, j] == 1), j
+            ])
             x <- length(intersect(cellsiggene, signames))
             m <- length(rownames(inter_sign_matrix)[which(
-                inter_sign_matrix[, i] == 1)])
+                inter_sign_matrix[, i] == 1
+            )])
             n <- total - m
             k <- length(intersect(cellsiggene, markerGenes))
             enrich <- (0 - log10(stats::phyper(
-                x, m, n, k, log.p = FALSE, lower.tail = FALSE)))
+                x, m, n, k,
+                log.p = FALSE, lower.tail = FALSE
+            )))
             vectorX <- append(vectorX, enrich)
         }
         enrichment[i, ] <- vectorX
@@ -1424,8 +1543,9 @@ runHyperGeometricEnrich <- function(gobject,
     if (p_value == TRUE) {
         enrichmentDT[, 2:dim(enrichmentDT)[2]] <- lapply(
             enrichmentDT[, 2:dim(enrichmentDT)[2]], function(x) {
-            10^(-x)
-        })
+                10^(-x)
+            }
+        )
     }
 
     # create spatial enrichment object
@@ -1449,7 +1569,8 @@ runHyperGeometricEnrich <- function(gobject,
     ## return object or results ##
     if (return_gobject == TRUE) {
         spenr_names <- list_spatial_enrichments_names(
-          gobject = gobject, spat_unit = spat_unit, feat_type = feat_type)
+            gobject = gobject, spat_unit = spat_unit, feat_type = feat_type
+        )
 
         if (name %in% spenr_names) {
             cat(name, " has already been used, will be overwritten")
@@ -1537,41 +1658,48 @@ hyperGeometricEnrich <- function(...) {
 #' @md
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' x <- findMarkers_one_vs_all(g, 
-#' cluster_column = "leiden_clus", min_feats = 20)
+#' x <- findMarkers_one_vs_all(g,
+#'     cluster_column = "leiden_clus", min_feats = 20
+#' )
 #' sign_gene <- x$feats
-#' 
-#' sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10), 
-#' nrow = length(sign_gene))
+#'
+#' sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+#'     nrow = length(sign_gene)
+#' )
 #' rownames(sign_matrix) <- sign_gene
-#' colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
-#' 
+#' colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
+#'
 #' runSpatialEnrich(gobject = g, sign_matrix = sign_matrix)
 #' @export
-runSpatialEnrich <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    enrich_method = c("PAGE", "rank", "hypergeometric"),
-    sign_matrix,
-    expression_values = c("normalized", "scaled", "custom"),
-    min_overlap_genes = 5,
-    reverse_log_scale = TRUE,
-    logbase = 2,
-    p_value = FALSE,
-    n_times = 1000,
-    rbp_p = 0.99,
-    num_agg = 100,
-    max_block = 20e6,
-    top_percentage = 5,
-    output_enrichment = c("original", "zscore"),
-    name = NULL,
-    verbose = TRUE,
-    return_gobject = TRUE) {
+runSpatialEnrich <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        enrich_method = c("PAGE", "rank", "hypergeometric"),
+        sign_matrix,
+        expression_values = c("normalized", "scaled", "custom"),
+        min_overlap_genes = 5,
+        reverse_log_scale = TRUE,
+        logbase = 2,
+        p_value = FALSE,
+        n_times = 1000,
+        rbp_p = 0.99,
+        num_agg = 100,
+        max_block = 20e6,
+        top_percentage = 5,
+        output_enrichment = c("original", "zscore"),
+        name = NULL,
+        verbose = TRUE,
+        return_gobject = TRUE) {
     enrich_method <- match.arg(
-        enrich_method, choices = c("PAGE", "rank", "hypergeometric"))
+        enrich_method,
+        choices = c("PAGE", "rank", "hypergeometric")
+    )
 
     output_enrichment <- match.arg(
-        output_enrichment, choices = c("original", "zscore"))
+        output_enrichment,
+        choices = c("original", "zscore")
+    )
 
 
     if (enrich_method == "PAGE") {
@@ -1710,29 +1838,32 @@ NULL
 #'   \item{\emph{Geary's C} 'geary'}
 #' }
 #' @export
-spatialAutoCorGlobal <- function(gobject = NULL,
-    spat_unit = NULL,
-    feat_type = NULL,
-    feats = NULL,
-    method = c("moran", "geary"),
-    data_to_use = c("expression", "cell_meta"),
-    expression_values = c("normalized", "scaled", "custom"),
-    meta_cols = NULL,
-    spatial_network_to_use = "kNN_network",
-    wm_method = c("distance", "adjacency"),
-    wm_name = "spat_weights",
-    node_values = NULL,
-    weight_matrix = NULL,
-    test_method = c("none", "monte_carlo"),
-    mc_nsim = 99,
-    cor_name = NULL,
-    return_gobject = FALSE,
-    verbose = TRUE) {
+spatialAutoCorGlobal <- function(
+        gobject = NULL,
+        spat_unit = NULL,
+        feat_type = NULL,
+        feats = NULL,
+        method = c("moran", "geary"),
+        data_to_use = c("expression", "cell_meta"),
+        expression_values = c("normalized", "scaled", "custom"),
+        meta_cols = NULL,
+        spatial_network_to_use = "kNN_network",
+        wm_method = c("distance", "adjacency"),
+        wm_name = "spat_weights",
+        node_values = NULL,
+        weight_matrix = NULL,
+        test_method = c("none", "monte_carlo"),
+        mc_nsim = 99,
+        cor_name = NULL,
+        return_gobject = FALSE,
+        verbose = TRUE) {
     # 0. determine inputs
     method <- match.arg(method, choices = c("moran", "geary"))
     test_method <- match.arg(test_method, choices = c("none", "monte_carlo"))
     data_to_use <- match.arg(
-        data_to_use, choices = c("expression", "cell_meta"))
+        data_to_use,
+        choices = c("expression", "cell_meta")
+    )
     if (is.null(cor_name)) cor_name <- method
     if (!is.null(node_values)) {
         if (is.numeric(node_values)) {
@@ -1743,9 +1874,13 @@ spatialAutoCorGlobal <- function(gobject = NULL,
     }
 
     use_ext_vals <- data.table::fifelse(
-        !is.null(node_values), yes = TRUE, no = FALSE)
+        !is.null(node_values),
+        yes = TRUE, no = FALSE
+    )
     use_sn <- data.table::fifelse(
-        !is.null(weight_matrix), yes = FALSE, no = TRUE)
+        !is.null(weight_matrix),
+        yes = FALSE, no = TRUE
+    )
 
     use_expr <- data.table::fcase(
         isTRUE(use_ext_vals), FALSE,
@@ -1837,9 +1972,12 @@ spatialAutoCorGlobal <- function(gobject = NULL,
 
     # return info
     if (isTRUE(return_gobject)) {
-        if (isTRUE(verbose))
-            wrap_msg("Appending", method,
-                    "results to feature metadata: fDataDT()")
+        if (isTRUE(verbose)) {
+            wrap_msg(
+                "Appending", method,
+                "results to feature metadata: fDataDT()"
+            )
+        }
         gobject <- addFeatMetadata(
             gobject = gobject,
             spat_unit = spat_unit,
@@ -1873,30 +2011,35 @@ spatialAutoCorGlobal <- function(gobject = NULL,
 #'   \item{\emph{Local mean} 'mean'}
 #' }
 #' @export
-spatialAutoCorLocal <- function(gobject = NULL,
-    spat_unit = NULL,
-    feat_type = NULL,
-    feats = NULL,
-    method = c("moran", "gi", "gi*", "mean"),
-    data_to_use = c("expression", "cell_meta"),
-    expression_values = c("normalized", "scaled", "custom"),
-    meta_cols = NULL,
-    spatial_network_to_use = "kNN_network",
-    wm_method = c("distance", "adjacency"),
-    wm_name = "spat_weights",
-    node_values = NULL,
-    weight_matrix = NULL,
-    test_method = c("none"),
-    # cor_name = NULL,
-    enrich_name = NULL,
-    return_gobject = TRUE,
-    output = c("spatEnrObj", "data.table"),
-    verbose = TRUE) {
+spatialAutoCorLocal <- function(
+        gobject = NULL,
+        spat_unit = NULL,
+        feat_type = NULL,
+        feats = NULL,
+        method = c("moran", "gi", "gi*", "mean"),
+        data_to_use = c("expression", "cell_meta"),
+        expression_values = c("normalized", "scaled", "custom"),
+        meta_cols = NULL,
+        spatial_network_to_use = "kNN_network",
+        wm_method = c("distance", "adjacency"),
+        wm_name = "spat_weights",
+        node_values = NULL,
+        weight_matrix = NULL,
+        test_method = c("none"),
+        # cor_name = NULL,
+        enrich_name = NULL,
+        return_gobject = TRUE,
+        output = c("spatEnrObj", "data.table"),
+        verbose = TRUE) {
     # 0. determine inputs
     method_select <- match.arg(
-        method, choices = c("moran", "gi", "gi*", "mean"))
+        method,
+        choices = c("moran", "gi", "gi*", "mean")
+    )
     data_to_use <- match.arg(
-        data_to_use, choices = c("expression", "cell_meta"))
+        data_to_use,
+        choices = c("expression", "cell_meta")
+    )
     output <- match.arg(output, choices = c("spatEnrObj", "data.table"))
     # if(is.null(cor_name)) cor_name = method
 
@@ -1916,9 +2059,13 @@ spatialAutoCorLocal <- function(gobject = NULL,
 
 
     use_ext_vals <- data.table::fifelse(
-        !is.null(node_values), yes = TRUE, no = FALSE)
+        !is.null(node_values),
+        yes = TRUE, no = FALSE
+    )
     use_sn <- data.table::fifelse(
-        !is.null(weight_matrix), yes = FALSE, no = TRUE)
+        !is.null(weight_matrix),
+        yes = FALSE, no = TRUE
+    )
 
     use_expr <- data.table::fcase(
         isTRUE(use_ext_vals), FALSE,
@@ -2021,7 +2168,7 @@ spatialAutoCorLocal <- function(gobject = NULL,
     if (isTRUE(return_gobject)) {
         if (isTRUE(verbose)) {
             wrap_msg("Attaching ", method_select,
-                    ' results as spatial enrichment: "',
+                ' results as spatial enrichment: "',
                 enrich_name, '"',
                 sep = ""
             )
@@ -2053,13 +2200,14 @@ spatialAutoCorLocal <- function(gobject = NULL,
 #' .run_spat_autocor_global
 #' @returns data.table
 #' @keywords internal
-.run_spat_autocor_global <- function(use_values,
-    feats,
-    weight_matrix,
-    method,
-    test_method,
-    mc_nsim,
-    cor_name) {
+.run_spat_autocor_global <- function(
+        use_values,
+        feats,
+        weight_matrix,
+        method,
+        test_method,
+        mc_nsim,
+        cor_name) {
     # data.table vars
     cell_ID <- nsim <- NULL
 
@@ -2071,8 +2219,11 @@ spatialAutoCorLocal <- function(gobject = NULL,
     }
 
     progressr::with_progress({
-        if (step_size > 1) pb <- progressr::progressor(
-            steps = nfeats / step_size)
+        if (step_size > 1) {
+            pb <- progressr::progressor(
+                steps = nfeats / step_size
+            )
+        }
         res_list <- lapply_flex(
             seq_along(feats),
             # future.packages = c('terra', 'data.table'),
@@ -2125,7 +2276,8 @@ spatialAutoCorLocal <- function(gobject = NULL,
         colnames(res_dt) <- c("feat_ID", cor_name)
     } else {
         colnames(res_dt) <- c("feat_ID", cor_name, paste0(
-            cor_name, "_", test_method))
+            cor_name, "_", test_method
+        ))
     }
     return(res_dt)
 }
@@ -2133,12 +2285,13 @@ spatialAutoCorLocal <- function(gobject = NULL,
 #' .run_spat_autocor_local
 #' @returns data.table
 #' @keywords internal
-.run_spat_autocor_local <- function(use_values,
-    feats,
-    weight_matrix,
-    method,
-    test_method,
-    IDs) {
+.run_spat_autocor_local <- function(
+        use_values,
+        feats,
+        weight_matrix,
+        method,
+        test_method,
+        IDs) {
     cell_ID <- NULL
 
     nfeats <- length(feats)
@@ -2149,8 +2302,11 @@ spatialAutoCorLocal <- function(gobject = NULL,
     }
 
     progressr::with_progress({
-        if (step_size > 1) pb <- progressr::progressor(
-            steps = nfeats / step_size)
+        if (step_size > 1) {
+            pb <- progressr::progressor(
+                steps = nfeats / step_size
+            )
+        }
         res_list <- lapply_flex(
             seq_along(feats),
             # future.packages = c('terra', 'data.table'),
@@ -2216,23 +2372,24 @@ spatialAutoCorLocal <- function(gobject = NULL,
 # 4, IDs - cell_IDs if available
 # Some additional information about information used in specific workflows are
 # also returned
-.evaluate_autocor_input <- function(gobject,
-    use_ext_vals,
-    use_sn,
-    use_expr,
-    use_meta,
-    spat_unit,
-    feat_type,
-    feats,
-    data_to_use,
-    expression_values,
-    meta_cols,
-    spatial_network_to_use,
-    wm_method,
-    wm_name,
-    node_values,
-    weight_matrix,
-    verbose = TRUE) {
+.evaluate_autocor_input <- function(
+        gobject,
+        use_ext_vals,
+        use_sn,
+        use_expr,
+        use_meta,
+        spat_unit,
+        feat_type,
+        feats,
+        data_to_use,
+        expression_values,
+        meta_cols,
+        spatial_network_to_use,
+        wm_method,
+        wm_name,
+        node_values,
+        weight_matrix,
+        verbose = TRUE) {
     cell_ID <- NULL
 
     # 1. Get spatial network to either get or generate a spatial weight matrix
@@ -2250,7 +2407,9 @@ spatialAutoCorLocal <- function(gobject = NULL,
         # if no weight_matrix already generated...
         if (is.null(weight_matrix)) {
             wm_method <- match.arg(
-                wm_method, choices = c("distance", "adjacency"))
+                wm_method,
+                choices = c("distance", "adjacency")
+            )
             if (isTRUE(verbose)) {
                 wrap_msg(
                     "No spatial weight matrix found in selected spatial network
@@ -2294,7 +2453,8 @@ spatialAutoCorLocal <- function(gobject = NULL,
         # EXPR=================================================================#
         values <- match.arg(
             expression_values,
-            unique(c("normalized", "scaled", "custom", expression_values)))
+            unique(c("normalized", "scaled", "custom", expression_values))
+        )
         use_values <- getExpression(
             gobject = gobject,
             spat_unit = spat_unit,
@@ -2358,7 +2518,8 @@ spatialAutoCorLocal <- function(gobject = NULL,
         (nrow(use_values) != nrow(weight_matrix))) {
         stop(wrap_txt("Number of values to correlate do not match number of
                       weight matrix entries",
-                      errWidth = TRUE))
+            errWidth = TRUE
+        ))
     }
 
 
@@ -2391,11 +2552,12 @@ spatialAutoCorLocal <- function(gobject = NULL,
 #' @description Rui to fill in
 #' @returns matrix
 #' @keywords internal
-enrich_deconvolution <- function(expr,
-    log_expr,
-    cluster_info,
-    ct_exp,
-    cutoff) {
+enrich_deconvolution <- function(
+        expr,
+        log_expr,
+        cluster_info,
+        ct_exp,
+        cutoff) {
     ##### generate enrich 0/1 matrix based on expression matrix
     ct_exp <- ct_exp[rowSums(ct_exp) > 0, ]
     enrich_matrix <- matrix(0, nrow = dim(ct_exp)[1], ncol = dim(ct_exp)[2])
@@ -2423,7 +2585,8 @@ enrich_deconvolution <- function(expr,
     cluster_info <- cluster_info
     for (i in seq_along(cluster_sort)) {
         cluster_i_enrich <- enrich_result[
-          , which(cluster_info == cluster_sort[i])]
+            , which(cluster_info == cluster_sort[i])
+        ]
         row_i_max <- Rfast::rowMaxs(cluster_i_enrich, value = TRUE)
         ct <- rownames(enrich_result)[which(row_i_max > cutoff)]
         if (length(ct) < 2) {
@@ -2433,7 +2596,8 @@ enrich_deconvolution <- function(expr,
         ct_gene <- c()
         for (j in seq_along(ct)) {
             sig_gene_j <- rownames(enrich_matrix)[
-              which(enrich_matrix[, ct[j]] == 1)]
+                which(enrich_matrix[, ct[j]] == 1)
+            ]
             ct_gene <- c(ct_gene, sig_gene_j)
         }
         uniq_ct_gene <- intersect(rownames(expr), unique(ct_gene))
@@ -2442,7 +2606,8 @@ enrich_deconvolution <- function(expr,
         cluster_cell_exp <- expr[uniq_ct_gene, cluster_i_cell]
 
         cluster_i_dwls <- optimize_deconvolute_dwls(
-            cluster_cell_exp, select_sig_exp)
+            cluster_cell_exp, select_sig_exp
+        )
         dwls_results[ct, cluster_i_cell] <- cluster_i_dwls
     }
     ##### remove negative values
@@ -2459,10 +2624,11 @@ enrich_deconvolution <- function(expr,
 #' @description Rui to fill in
 #' @returns matrix
 #' @keywords internal
-spot_deconvolution <- function(expr,
-    cluster_info,
-    ct_exp,
-    binary_matrix) {
+spot_deconvolution <- function(
+        expr,
+        cluster_info,
+        ct_exp,
+        binary_matrix) {
     ##### generate enrich 0/1 matrix based on expression matrix
     enrich_matrix <- matrix(0, nrow = dim(ct_exp)[1], ncol = dim(ct_exp)[2])
     rowmax_col <- Rfast::rowMaxs(ct_exp)
@@ -2480,7 +2646,8 @@ spot_deconvolution <- function(expr,
 
     for (i in seq_along(cluster_sort)) {
         cluster_i_matrix <- binary_matrix[
-            , which(cluster_info == cluster_sort[i])]
+            , which(cluster_info == cluster_sort[i])
+        ]
         row_i_max <- Rfast::rowMaxs(cluster_i_matrix, value = TRUE)
         ct_i <- rownames(cluster_i_matrix)[which(row_i_max == 1)]
         ######## calculate proportion based on binarized deconvolution
@@ -2491,7 +2658,8 @@ spot_deconvolution <- function(expr,
             ct_gene <- c()
             for (j in seq_along(ct_i)) {
                 sig_gene_j <- rownames(enrich_matrix)[
-                    which(enrich_matrix[, ct_i[j]] == 1)]
+                    which(enrich_matrix[, ct_i[j]] == 1)
+                ]
                 ct_gene <- c(ct_gene, sig_gene_j)
             }
             uniq_ct_gene <- intersect(rownames(expr), unique(ct_gene))
@@ -2503,12 +2671,14 @@ spot_deconvolution <- function(expr,
             all_exp <- Matrix::rowMeans(cluster_cell_exp)
             solution_all_exp <- solve_OLS_internal(select_sig_exp, all_exp)
             constant_J <- find_dampening_constant(
-                select_sig_exp, all_exp, solution_all_exp)
+                select_sig_exp, all_exp, solution_all_exp
+            )
             ###### deconvolution for each spot
-            for (k in 1:(dim(cluster_cell_exp)[2])) {
+            for (k in seq_len(dim(cluster_cell_exp)[2])) {
                 B <- Matrix::as.matrix(cluster_cell_exp[, k])
                 ct_spot_k <- rownames(cluster_i_matrix)[
-                    which(cluster_i_matrix[, k] == 1)]
+                    which(cluster_i_matrix[, k] == 1)
+                ]
                 if (sum(B) == 0 || length(ct_spot_k) == 0) {
                     ####* must include the case where all genes are 0
                     dwls_results[, colnames(cluster_cell_exp)[k]] <- NA
@@ -2517,16 +2687,19 @@ spot_deconvolution <- function(expr,
                 }
                 if (length(ct_spot_k) == 1) {
                     dwls_results[
-                        ct_spot_k[1], colnames(cluster_cell_exp)[k]] <- 1
+                        ct_spot_k[1], colnames(cluster_cell_exp)[k]
+                    ] <- 1
                 } else {
                     ct_k_gene <- c()
                     for (m in seq_along(ct_spot_k)) {
                         sig_gene_k <- rownames(enrich_matrix)[which(
-                            enrich_matrix[, ct_spot_k[m]] == 1)]
+                            enrich_matrix[, ct_spot_k[m]] == 1
+                        )]
                         ct_k_gene <- c(ct_k_gene, sig_gene_k)
                     }
                     uniq_ct_k_gene <- intersect(
-                        rownames(ct_exp), unique(ct_k_gene))
+                        rownames(ct_exp), unique(ct_k_gene)
+                    )
                     S_k <- Matrix::as.matrix(ct_exp[uniq_ct_k_gene, ct_spot_k])
                     if (sum(B[uniq_ct_k_gene, ]) == 0) {
                         ####* must include the case all genes are 0
@@ -2534,7 +2707,8 @@ spot_deconvolution <- function(expr,
                         ####* will produce NAs for some spots in the output
                     } else {
                         solDWLS <- optimize_solveDampenedWLS(S_k, B[
-                            uniq_ct_k_gene, ], constant_J)
+                            uniq_ct_k_gene,
+                        ], constant_J)
                         dwls_results[names(solDWLS), colnames(cluster_cell_exp)[k]] <- solDWLS
                     }
                 }
@@ -2555,9 +2729,10 @@ spot_deconvolution <- function(expr,
 #' @description Rui to fill in
 #' @returns enrichment values
 #' @keywords internal
-cluster_enrich_analysis <- function(exp_matrix,
-    cluster_info,
-    enrich_sig_matrix) {
+cluster_enrich_analysis <- function(
+        exp_matrix,
+        cluster_info,
+        enrich_sig_matrix) {
     uniq_cluster <- mixedsort(unique(cluster_info))
     if (length(uniq_cluster) == 1) {
         stop("Only one cluster identified, need at least two.")
@@ -2566,8 +2741,11 @@ cluster_enrich_analysis <- function(exp_matrix,
     for (i in uniq_cluster) {
         cluster_exp <- cbind(
             cluster_exp,
-            (apply(exp_matrix, 1,
-                    function(y) mean(y[which(cluster_info == i)]))))
+            (apply(
+                exp_matrix, 1,
+                function(y) mean(y[which(cluster_info == i)])
+            ))
+        )
     }
     log_cluster_exp <- log2(cluster_exp + 1)
     colnames(log_cluster_exp) <- uniq_cluster
@@ -2580,8 +2758,9 @@ cluster_enrich_analysis <- function(exp_matrix,
 #' @description Rui to fill in
 #' @returns enrichment matrix
 #' @keywords internal
-enrich_analysis <- function(expr_values,
-    sign_matrix) {
+enrich_analysis <- function(
+        expr_values,
+        sign_matrix) {
     # output enrichment
     # only continue with genes present in both datasets
     interGene <- intersect(rownames(sign_matrix), rownames(expr_values))
@@ -2597,8 +2776,9 @@ enrich_analysis <- function(expr_values,
 
     # get enrichment scores
     enrichment <- matrix(
-        data = NA, nrow = dim(filterSig)[2], ncol = length(cellColMean))
-    for (i in (1:dim(filterSig)[2])) {
+        data = NA, nrow = dim(filterSig)[2], ncol = length(cellColMean)
+    )
+    for (i in seq_len(dim(filterSig)[2])) {
         signames <- rownames(filterSig)[which(filterSig[, i] == 1)]
         sigColMean <- apply(geneFold[signames, ], 2, mean)
         m <- length(signames)
@@ -2623,8 +2803,9 @@ enrich_analysis <- function(expr_values,
 #' @description Rui to fill in
 #' @returns matrix
 #' @keywords internal
-optimize_deconvolute_dwls <- function(exp,
-    Signature) {
+optimize_deconvolute_dwls <- function(
+        exp,
+        Signature) {
     ###### overlap signature with spatial genes
     Genes <- intersect(rownames(Signature), rownames(exp))
     S <- Signature[Genes, ]
@@ -2637,7 +2818,7 @@ optimize_deconvolute_dwls <- function(exp,
     solution_all_exp <- solve_OLS_internal(S, all_exp[Genes])
 
     constant_J <- find_dampening_constant(S, all_exp[Genes], solution_all_exp)
-    for (j in 1:(dim(subBulk)[2])) {
+    for (j in seq_len(dim(subBulk)[2])) {
         B <- subBulk[, j]
         if (sum(B) > 0) {
             solDWLS <- optimize_solveDampenedWLS(S, B, constant_J)
@@ -2658,9 +2839,10 @@ optimize_deconvolute_dwls <- function(exp,
 #' @title optimize_solveDampenedWLS
 #' @returns numeric
 #' @keywords internal
-optimize_solveDampenedWLS <- function(S,
-    B,
-    constant_J) {
+optimize_solveDampenedWLS <- function(
+        S,
+        B,
+        constant_J) {
     # first solve OLS, use this solution to find a starting point for the
     # weights
     solution <- solve_OLS_internal(S, B)
@@ -2696,9 +2878,10 @@ optimize_solveDampenedWLS <- function(S,
 #' @description find a dampening constant for the weights using cross-validation
 #' @returns numeric
 #' @keywords internal
-find_dampening_constant <- function(S,
-    B,
-    goldStandard) {
+find_dampening_constant <- function(
+        S,
+        B,
+        goldStandard) {
     solutionsSd <- NULL
 
     # goldStandard is used to define the weights
@@ -2715,20 +2898,22 @@ find_dampening_constant <- function(S,
     # try multiple values of the dampening constant (multiplier)
     # for each, calculate the variance of the dampened weighted solution for
     # a subset of genes
-    for (j in 1:ceiling(log2(max(wsScaledMinusInf)))) {
+    for (j in seq_len(ceiling(log2(max(wsScaledMinusInf))))) {
         multiplier <- 1 * 2^(j - 1)
         wsDampened <- wsScaled
         wsDampened[which(wsScaled > multiplier)] <- multiplier
         solutions <- NULL
-        seeds <- c(1:100)
-        for (i in 1:100) {
+        seeds <- seq_len(100)
+        for (i in seq_len(100)) {
             set.seed(seeds[i]) # make nondeterministic
             subset <- sample(length(ws), size = length(ws) * 0.5)
             # randomly select half of gene set
 
             # solve dampened weighted least squares for subset
             fit <- stats::lm(
-                B[subset] ~ -1 + S[subset, ], weights = wsDampened[subset])
+                B[subset] ~ -1 + S[subset, , drop = FALSE],
+                weights = wsDampened[subset]
+            )
             sol <- fit$coef * sum(goldStandard) / sum(fit$coef)
             solutions <- cbind(solutions, sol)
         }
@@ -2745,8 +2930,9 @@ find_dampening_constant <- function(S,
 #' @description basic functions for dwls
 #' @returns numeric
 #' @keywords internal
-solve_OLS_internal <- function(S,
-    B) {
+solve_OLS_internal <- function(
+        S,
+        B) {
     D <- t(S) %*% S
     d <- t(S) %*% B
     A <- cbind(diag(dim(S)[2]))
@@ -2811,10 +2997,11 @@ solve_OLS_internal <- function(S,
 #' @description solve WLS given a dampening constant
 #' @returns matrix
 #' @keywords internal
-solve_dampened_WLSj <- function(S,
-    B,
-    goldStandard,
-    j) {
+solve_dampened_WLSj <- function(
+        S,
+        B,
+        goldStandard,
+        j) {
     multiplier <- 1 * 2^(j - 1)
     sol <- goldStandard
     ws <- as.vector((1 / (S %*% sol))^2)
@@ -2863,27 +3050,30 @@ solve_dampened_WLSj <- function(S,
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #' x <- findMarkers_one_vs_all(g,
-#' cluster_column = "leiden_clus", min_feats = 20)
+#'     cluster_column = "leiden_clus", min_feats = 20
+#' )
 #' sign_gene <- x$feats
 #'
-#' sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10),
-#' nrow = length(sign_gene))
+#' sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+#'     nrow = length(sign_gene)
+#' )
 #' rownames(sign_matrix) <- sign_gene
-#' colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
+#' colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
 #'
 #' runDWLSDeconv(gobject = g, sign_matrix = sign_matrix)
 #' @export
-runDWLSDeconv <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized"),
-    logbase = 2,
-    cluster_column = "leiden_clus",
-    sign_matrix,
-    n_cell = 50,
-    cutoff = 2,
-    name = NULL,
-    return_gobject = TRUE) {
+runDWLSDeconv <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized"),
+        logbase = 2,
+        cluster_column = "leiden_clus",
+        sign_matrix,
+        n_cell = 50,
+        cutoff = 2,
+        name = NULL,
+        return_gobject = TRUE) {
     # verify if optional package is installed
     package_check(pkg_name = "quadprog", repository = "CRAN")
     package_check(pkg_name = "Rfast", repository = "CRAN")
@@ -2905,7 +3095,8 @@ runDWLSDeconv <- function(gobject,
 
     values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_values <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -2954,9 +3145,11 @@ runDWLSDeconv <- function(gobject,
         binary_matrix = binarize_proportion
     )
     deconvolutionDT <- data.table::data.table(
-        cell_ID = colnames(spot_proportion))
+        cell_ID = colnames(spot_proportion)
+    )
     deconvolutionDT <- cbind(
-        deconvolutionDT, data.table::as.data.table(t(spot_proportion)))
+        deconvolutionDT, data.table::as.data.table(t(spot_proportion))
+    )
 
     # create spatial enrichment object
     enrObj <- create_spat_enr_obj(
@@ -3039,29 +3232,32 @@ runDWLSDeconv <- function(gobject,
 #' @seealso \code{\link{runDWLSDeconv}}
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' x <- findMarkers_one_vs_all(g, 
-#' cluster_column = "leiden_clus", min_feats = 20)
+#' x <- findMarkers_one_vs_all(g,
+#'     cluster_column = "leiden_clus", min_feats = 20
+#' )
 #' sign_gene <- x$feats
-#' 
-#' sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10), 
-#' nrow = length(sign_gene))
+#'
+#' sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+#'     nrow = length(sign_gene)
+#' )
 #' rownames(sign_matrix) <- sign_gene
-#' colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
-#' 
+#' colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
+#'
 #' runSpatialDeconv(gobject = g, sign_matrix = sign_matrix)
 #' @export
-runSpatialDeconv <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    deconv_method = c("DWLS"),
-    expression_values = c("normalized"),
-    logbase = 2,
-    cluster_column = "leiden_clus",
-    sign_matrix,
-    n_cell = 50,
-    cutoff = 2,
-    name = NULL,
-    return_gobject = TRUE) {
+runSpatialDeconv <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        deconv_method = c("DWLS"),
+        expression_values = c("normalized"),
+        logbase = 2,
+        cluster_column = "leiden_clus",
+        sign_matrix,
+        n_cell = 50,
+        cutoff = 2,
+        name = NULL,
+        return_gobject = TRUE) {
     deconv_method <- match.arg(deconv_method, choices = c("DWLS"))
 
 
diff --git a/R/spatial_enrichment_visuals.R b/R/spatial_enrichment_visuals.R
index e898eb5fc..fec02713c 100644
--- a/R/spatial_enrichment_visuals.R
+++ b/R/spatial_enrichment_visuals.R
@@ -2,7 +2,7 @@
 #' @name findCellTypesFromEnrichment
 #' @param gobject Giotto Object
 #' @param spat_unit spatial unit in which the enrichment information is stored
-#' @param feat_type feature type for which the enrichment information was 
+#' @param feat_type feature type for which the enrichment information was
 #' calculated
 #' @param enrichment_name name of the spatial enrichment
 #'  i.e. output from GiottoClass::list_spatial_enrichment_names()
@@ -24,15 +24,17 @@
 #' the associated cell types from the enrichment.
 #'
 #' @export
-findCellTypesFromEnrichment <- function(gobject = NULL,
-    spat_unit = NULL,
-    feat_type = NULL,
-    enrichment_name = "PAGE_z_score",
-    return_frequency_table = FALSE) {
+findCellTypesFromEnrichment <- function(
+        gobject = NULL,
+        spat_unit = NULL,
+        feat_type = NULL,
+        enrichment_name = "PAGE_z_score",
+        return_frequency_table = FALSE) {
     # guard clauses
 
-    if (!inherits(gobject, "giotto")) 
+    if (!inherits(gobject, "giotto")) {
         stop("gobject needs to be a giotto object")
+    }
 
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -69,17 +71,20 @@ findCellTypesFromEnrichment <- function(gobject = NULL,
     # new column, mapping a cell to it's most likely type
     if (enrich_is_p_value) {
         pz_enrich[, probable_cell_type := names(
-            .SD)[max.col(-.SD)], .SDcols = 2:n_c]
+            .SD
+        )[max.col(-.SD)], .SDcols = 2:n_c]
     } else {
         pz_enrich[, probable_cell_type := names(
-            .SD)[max.col(.SD)], .SDcols = 2:n_c]
+            .SD
+        )[max.col(.SD)], .SDcols = 2:n_c]
     }
 
     cell_ID_and_types_pz_enrich <- pz_enrich[, .(cell_ID, probable_cell_type)]
 
     if (return_frequency_table) {
         pz_enrich_cell_type_frequencies <- table(
-            cell_ID_and_types_pz_enrich$probable_cell_type)
+            cell_ID_and_types_pz_enrich$probable_cell_type
+        )
         return(pz_enrich_cell_type_frequencies)
     }
 
@@ -90,7 +95,7 @@ findCellTypesFromEnrichment <- function(gobject = NULL,
 #' @name plotCellTypesFromEnrichment
 #' @param gobject Giotto Object
 #' @param spat_unit spatial unit in which the enrichment information is stored
-#' @param feat_type feature type for which the enrichment information was 
+#' @param feat_type feature type for which the enrichment information was
 #' calculated
 #' @param enrichment_name name of the spatial enrichment
 #'  i.e. output from GiottoClass::list_spatial_enrichment_names()
@@ -104,20 +109,21 @@ findCellTypesFromEnrichment <- function(gobject = NULL,
 #' This function generates a bar plot of cell types vs the frequency
 #' of that cell type in the data. These cell type results are
 #' based on the provided `enrichment_name`, and will be determined
-#' by the maximum value of the z-score or p-value for a given cell or 
+#' by the maximum value of the z-score or p-value for a given cell or
 #' annotation.
 #'
 #' @export
-plotCellTypesFromEnrichment <- function(gobject = NULL,
-    spat_unit = NULL,
-    feat_type = NULL,
-    enrichment_name = "PAGE_z_score",
-    title = NULL,
-    save_param = list(),
-    default_save_name = "cell_types_from_enrichment",
-    save_plot = NULL,
-    show_plot = NULL,
-    return_plot = NULL) {
+plotCellTypesFromEnrichment <- function(
+        gobject = NULL,
+        spat_unit = NULL,
+        feat_type = NULL,
+        enrichment_name = "PAGE_z_score",
+        title = NULL,
+        save_param = list(),
+        default_save_name = "cell_types_from_enrichment",
+        save_plot = NULL,
+        show_plot = NULL,
+        return_plot = NULL) {
     # guard clauses handled at first step downstream
     # therefore, omitting here.
     id_and_types <- findCellTypesFromEnrichment(
@@ -131,8 +137,11 @@ plotCellTypesFromEnrichment <- function(gobject = NULL,
     # data.table column
     probable_cell_type <- NULL
 
-    if (is.null(title)) title <- paste0(
-        spat_unit, "cell types (maximum", enrichment_name, ")")
+    if (is.null(title)) {
+        title <- paste0(
+            spat_unit, "cell types (maximum", enrichment_name, ")"
+        )
+    }
 
     pl <- ggplot2::ggplot(id_and_types, aes(x = probable_cell_type)) +
         ggplot2::geom_bar() +
@@ -163,7 +172,7 @@ plotCellTypesFromEnrichment <- function(gobject = NULL,
 #' @name pieCellTypesFromEnrichment
 #' @param gobject Giotto Object
 #' @param spat_unit spatial unit in which the enrichment information is stored
-#' @param feat_type feature type for which the enrichment information was 
+#' @param feat_type feature type for which the enrichment information was
 #' calculated
 #' @param enrichment_name name of the spatial enrichment
 #'  i.e. output from GiottoClass::list_spatial_enrichment_names()
@@ -179,16 +188,17 @@ plotCellTypesFromEnrichment <- function(gobject = NULL,
 #' and will be determined by the maximum value of the z-score
 #' or p-value for a given cell or annotation.
 #' @export
-pieCellTypesFromEnrichment <- function(gobject = NULL,
-    spat_unit = NULL,
-    feat_type = NULL,
-    enrichment_name = "PAGE_z_score",
-    title = NULL,
-    save_param = list(),
-    default_save_name = "cell_types_from_enrichment_pie",
-    save_plot = NULL,
-    show_plot = NULL,
-    return_plot = NULL) {
+pieCellTypesFromEnrichment <- function(
+        gobject = NULL,
+        spat_unit = NULL,
+        feat_type = NULL,
+        enrichment_name = "PAGE_z_score",
+        title = NULL,
+        save_param = list(),
+        default_save_name = "cell_types_from_enrichment_pie",
+        save_plot = NULL,
+        show_plot = NULL,
+        return_plot = NULL) {
     # guard clauses handled one step downstream
 
     freq_table <- findCellTypesFromEnrichment(
@@ -211,7 +221,8 @@ pieCellTypesFromEnrichment <- function(gobject = NULL,
     for (i in cell_types) {
         # hackish, admittedly
         nullvar <- freq_dt[cell_type == i, perc := num_cells / sum(
-            freq_dt$num_cells) * 100]
+            freq_dt$num_cells
+        ) * 100]
     }
     rm(nullvar) # saves memory
 
diff --git a/R/spatial_genes.R b/R/spatial_genes.R
index 68fd2d3fa..53bc927c5 100644
--- a/R/spatial_genes.R
+++ b/R/spatial_genes.R
@@ -8,11 +8,12 @@ NULL
 
 #' @rdname spat_fisher_exact
 #' @keywords internal
-.spat_fish_func <- function(feat,
-    bin_matrix,
-    spat_mat,
-    calc_hub = FALSE,
-    hub_min_int = 3) {
+.spat_fish_func <- function(
+        feat,
+        bin_matrix,
+        spat_mat,
+        calc_hub = FALSE,
+        hub_min_int = 3) {
     feat_vector <- bin_matrix[rownames(bin_matrix) == feat, ]
 
     feat_vectorA <- feat_vector[names(feat_vector) %in% rownames(spat_mat)]
@@ -47,19 +48,22 @@ NULL
         high_cells <- names(feat_vector[feat_vector == 1])
         subset_spat_mat <- spat_mat[
             rownames(spat_mat) %in% high_cells, colnames(spat_mat) %in%
-                high_cells]
+                high_cells
+        ]
 
         if (length(subset_spat_mat) == 1) {
             hub_nr <- 0
         } else {
             subset_spat_mat <- spat_mat[
                 rownames(spat_mat) %in% high_cells, colnames(spat_mat) %in%
-                    high_cells]
+                    high_cells
+            ]
             rowhubs <- rowSums_flex(subset_spat_mat)
             colhubs <- colSums_flex(subset_spat_mat)
             hub_nr <- length(unique(c(
                 names(colhubs[colhubs > hub_min_int]),
-                names(rowhubs[colhubs > hub_min_int]))))
+                names(rowhubs[colhubs > hub_min_int])
+            )))
         }
 
         fish_res <- stats::fisher.test(table_matrix)[c("p.value", "estimate")]
@@ -72,11 +76,12 @@ NULL
 
 #' @describeIn spat_fisher_exact data.table implementation
 #' @keywords internal
-.spat_fish_func_dt <- function(bin_matrix_DTm,
-    spat_netw_min,
-    calc_hub = FALSE,
-    hub_min_int = 3,
-    cores = NA) {
+.spat_fish_func_dt <- function(
+        bin_matrix_DTm,
+        spat_netw_min,
+        calc_hub = FALSE,
+        hub_min_int = 3,
+        cores = NA) {
     # set number of cores automatically, but with limit of 10
     cores <- determine_cores(cores)
     data.table::setDTthreads(threads = cores)
@@ -91,12 +96,15 @@ NULL
         bin_matrix_DTm,
         by.x = "from",
         by.y = "variable",
-        allow.cartesian = TRUE)
+        allow.cartesian = TRUE
+    )
     data.table::setnames(spatial_network_min_ext, "value", "from_value")
 
     spatial_network_min_ext <- data.table::merge.data.table(
-        spatial_network_min_ext, by.x = c("to", "feat_ID"),
-        bin_matrix_DTm, by.y = c("variable", "feat_ID"))
+        spatial_network_min_ext,
+        by.x = c("to", "feat_ID"),
+        bin_matrix_DTm, by.y = c("variable", "feat_ID")
+    )
     data.table::setnames(spatial_network_min_ext, "value", "to_value")
 
 
@@ -121,7 +129,8 @@ NULL
     # sort the combinations and run fisher test
     data.table::setorder(freq_summary2, feat_ID, combn, -N)
     fish_results <- freq_summary2[, stats::fisher.test(
-        matrix(N, nrow = 2))[c(1, 3)], by = feat_ID]
+        matrix(N, nrow = 2)
+    )[c(1, 3)], by = feat_ID]
 
 
     ## hubs ##
@@ -140,14 +149,17 @@ NULL
         # get hubs and add 0's
         hub_DT <- double_pos_both[V1 > hub_min_int, .N, by = feat_ID]
         hub_DT_zeroes <- data.table::data.table(feat_ID = unique(
-            spatial_network_min_ext$feat_ID), N = 0)
+            spatial_network_min_ext$feat_ID
+        ), N = 0)
         hub_DT2 <- rbind(hub_DT, hub_DT_zeroes)
 
         hub_DT2 <- hub_DT2[, sum(N), by = feat_ID]
         data.table::setnames(hub_DT2, "V1", "hub_nr")
 
         fish_results <- data.table::merge.data.table(
-            fish_results, hub_DT2, by = "feat_ID")
+            fish_results, hub_DT2,
+            by = "feat_ID"
+        )
     }
 
     return(fish_results)
@@ -164,11 +176,12 @@ NULL
 
 #' @rdname spat_odds_ratio
 #' @keywords internal
-.spat_or_func <- function(feat,
-    bin_matrix,
-    spat_mat,
-    calc_hub = FALSE,
-    hub_min_int = 3) {
+.spat_or_func <- function(
+        feat,
+        bin_matrix,
+        spat_mat,
+        calc_hub = FALSE,
+        hub_min_int = 3) {
     feat_vector <- bin_matrix[rownames(bin_matrix) == feat, ]
 
     feat_vectorA <- feat_vector[names(feat_vector) %in% rownames(spat_mat)]
@@ -204,7 +217,8 @@ NULL
         high_cells <- names(feat_vector[feat_vector == 1])
         subset_spat_mat <- spat_mat[
             rownames(spat_mat) %in% high_cells, colnames(spat_mat) %in%
-                high_cells]
+                high_cells
+        ]
 
         if (length(subset_spat_mat) == 1) {
             hub_nr <- 0
@@ -212,14 +226,16 @@ NULL
             rowhubs <- rowSums_flex(subset_spat_mat)
             colhubs <- colSums_flex(subset_spat_mat)
             hub_nr <- length(unique(c(names(
-                colhubs[colhubs > hub_min_int]), names(
-                    rowhubs[colhubs > hub_min_int]))))
+                colhubs[colhubs > hub_min_int]
+            ), names(
+                rowhubs[colhubs > hub_min_int]
+            ))))
         }
 
         fish_matrix <- table_matrix
         fish_matrix <- fish_matrix / 1000
         OR <- ((fish_matrix[1] * fish_matrix[4]) /
-                (fish_matrix[2] * fish_matrix[3]))
+            (fish_matrix[2] * fish_matrix[3]))
 
         return(c(feats = list(feat), OR, hubs = list(hub_nr)))
     }
@@ -234,11 +250,12 @@ NULL
 
 #' @describeIn spat_odds_ratio data.table implementation
 #' @keywords internal
-.spat_or_func_dt <- function(bin_matrix_DTm,
-    spat_netw_min,
-    calc_hub = FALSE,
-    hub_min_int = 3,
-    cores = NA) {
+.spat_or_func_dt <- function(
+        bin_matrix_DTm,
+        spat_netw_min,
+        calc_hub = FALSE,
+        hub_min_int = 3,
+        cores = NA) {
     # set number of cores automatically, but with limit of 10
     cores <- determine_cores(cores)
     data.table::setDTthreads(threads = cores)
@@ -251,12 +268,15 @@ NULL
     spatial_network_min_ext <- data.table::merge.data.table(
         spat_netw_min, bin_matrix_DTm,
         by.x = "from", by.y = "variable",
-        allow.cartesian = TRUE)
+        allow.cartesian = TRUE
+    )
     data.table::setnames(spatial_network_min_ext, "value", "from_value")
 
     spatial_network_min_ext <- data.table::merge.data.table(
-        spatial_network_min_ext, by.x = c("to", "feat_ID"),
-        bin_matrix_DTm, by.y = c("variable", "feat_ID"))
+        spatial_network_min_ext,
+        by.x = c("to", "feat_ID"),
+        bin_matrix_DTm, by.y = c("variable", "feat_ID")
+    )
     data.table::setnames(spatial_network_min_ext, "value", "to_value")
 
 
@@ -281,7 +301,9 @@ NULL
     # sort the combinations and run fisher test
     setorder(freq_summary2, feat_ID, combn, -N)
     or_results <- freq_summary2[
-        , .or_test_func(matrix(N, nrow = 2)), by = feat_ID]
+        , .or_test_func(matrix(N, nrow = 2)),
+        by = feat_ID
+    ]
 
 
     ## hubs ##
@@ -300,14 +322,17 @@ NULL
         # get hubs and add 0's
         hub_DT <- double_pos_both[V1 > hub_min_int, .N, by = feat_ID]
         hub_DT_zeroes <- data.table::data.table(
-            feat_ID = unique(spatial_network_min_ext$feat_ID), N = 0)
+            feat_ID = unique(spatial_network_min_ext$feat_ID), N = 0
+        )
         hub_DT2 <- rbind(hub_DT, hub_DT_zeroes)
 
         hub_DT2 <- hub_DT2[, sum(N), by = feat_ID]
         data.table::setnames(hub_DT2, "V1", "hub_nr")
 
         or_results <- data.table::merge.data.table(
-            or_results, hub_DT2, by = "feat_ID")
+            or_results, hub_DT2,
+            by = "feat_ID"
+        )
     }
 
     return(or_results)
@@ -336,17 +361,18 @@ NULL
 #' @describeIn calculate_spatial_enrichment calculate using a 'simple' and
 #' efficient for loop
 #' @keywords internal
-.calc_spatial_enrichment_minimum <- function(spatial_network,
-    bin_matrix,
-    adjust_method = "fdr",
-    do_fisher_test = TRUE) {
+.calc_spatial_enrichment_minimum <- function(
+        spatial_network,
+        bin_matrix,
+        adjust_method = "fdr",
+        do_fisher_test = TRUE) {
     # data.table variables
     from <- to <- feats <- variable <- value <- p.value <- adj.p.value <-
         score <- estimate <- NULL
 
     spatial_network_min <- spatial_network[, .(from, to)]
 
-    all_colindex <- 1:ncol(bin_matrix)
+    all_colindex <- seq_len(ncol(bin_matrix))
     names(all_colindex) <- colnames(bin_matrix)
 
     # code for possible combinations
@@ -355,15 +381,17 @@ NULL
 
     # preallocate final matrix for results
     matrix_res <- matrix(
-        data = NA, nrow = nrow(bin_matrix), ncol = nrow(spatial_network_min))
+        data = NA, nrow = nrow(bin_matrix), ncol = nrow(spatial_network_min)
+    )
 
     ## 1. summarize results for each edge in the network
-    for (row_i in 1:nrow(spatial_network_min)) {
+    for (row_i in seq_len(nrow(spatial_network_min))) {
         from_id <- spatial_network_min[row_i][["from"]]
         to_id <- spatial_network_min[row_i][["to"]]
 
         sumres <- data.table::as.data.table(bin_matrix[
-            , all_colindex[c(from_id, to_id)]])
+            , all_colindex[c(from_id, to_id)]
+        ])
         sumres[, combn := paste0(get(from_id), "-", get(to_id))]
 
         code_res <- convert_code[sumres$combn]
@@ -378,7 +406,7 @@ NULL
 
     ## 2. calculate the frequencies of possible combinations ##
     # '0-0' = 1, '0-1' = 2, '1-0' = 3 and '1-1' = 4
-    for (row_i in 1:nrow(matrix_res)) {
+    for (row_i in seq_len(nrow(matrix_res))) {
         x <- matrix_res[row_i, ]
         x <- factor(x, levels = c(1, 2, 3, 4))
         tabres <- as.vector(table(x))
@@ -387,7 +415,7 @@ NULL
     }
 
     rownames(table_res) <- rownames(matrix_res)
-    colnames(table_res) <- 1:4
+    colnames(table_res) <- seq_len(4)
 
     rable_resDT <- data.table::as.data.table(table_res)
     rable_resDT[, feats := rownames(table_res)]
@@ -398,20 +426,26 @@ NULL
     ## run fisher test ##
     if (do_fisher_test == TRUE) {
         results <- rable_resDTm[, stats::fisher.test(matrix(
-            value, nrow = 2))[c(1, 3)], by = feats]
+            value,
+            nrow = 2
+        ))[c(1, 3)], by = feats]
 
         # replace zero p-values with lowest p-value
         min_pvalue <- min(results$p.value[results$p.value > 0])
         results[, p.value := ifelse(p.value == 0, min_pvalue, p.value)]
         results[, adj.p.value := stats::p.adjust(
-            p.value, method = adjust_method)]
+            p.value,
+            method = adjust_method
+        )]
 
         # sort feats based on p-value and estimate
         results[, score := -log(p.value) * estimate]
         data.table::setorder(results, -score)
     } else {
         results <- rable_resDTm[, .or_test_func(matrix(
-            value, nrow = 2)), by = feats]
+            value,
+            nrow = 2
+        )), by = feats]
         data.table::setorder(results, -estimate)
     }
 
@@ -421,21 +455,24 @@ NULL
 #' @describeIn calculate_spatial_enrichment calculate using 'matrix'
 #' implementation
 #' @keywords internal
-.calc_spatial_enrichment_matrix <- function(spatial_network,
-    bin_matrix,
-    adjust_method = "fdr",
-    do_fisher_test = TRUE,
-    do_parallel = TRUE,
-    cores = NA,
-    calc_hub = FALSE,
-    hub_min_int = 3,
-    verbose = TRUE) {
+.calc_spatial_enrichment_matrix <- function(
+        spatial_network,
+        bin_matrix,
+        adjust_method = "fdr",
+        do_fisher_test = TRUE,
+        do_parallel = TRUE,
+        cores = NA,
+        calc_hub = FALSE,
+        hub_min_int = 3,
+        verbose = TRUE) {
     # data.table variables
     verbose <- feats <- p.value <- estimate <- adj.p.value <- score <- NULL
 
     # convert spatial network data.table to spatial matrix
     dc_spat_network <- data.table::dcast.data.table(
-        spatial_network, formula = to ~ from, value.var = "distance", fill = 0)
+        spatial_network,
+        formula = to ~ from, value.var = "distance", fill = 0
+    )
     spat_mat <- dt_to_matrix(dc_spat_network)
     spat_mat[spat_mat > 0] <- 1
 
@@ -486,13 +523,16 @@ NULL
 
     if (do_fisher_test == TRUE) {
         result[, c("p.value", "estimate") := list(
-            as.numeric(p.value), as.numeric(estimate))]
+            as.numeric(p.value), as.numeric(estimate)
+        )]
 
         # convert p.value = 0 to lowest p-value
         min_pvalue <- min(result$p.value[result$p.value > 0])
         result[, p.value := ifelse(p.value == 0, min_pvalue, p.value)]
         result[, adj.p.value := stats::p.adjust(
-            p.value, method = adjust_method)]
+            p.value,
+            method = adjust_method
+        )]
 
         result[, score := -log(p.value) * estimate]
         data.table::setorder(result, -score)
@@ -508,15 +548,14 @@ NULL
 #' @describeIn calculate_spatial_enrichment calculate using 'data.table'
 #' implementation
 #' @keywords internal
-.calc_spatial_enrichment_dt <- function(
-        bin_matrix,
-        spatial_network,
-        calc_hub = FALSE,
-        hub_min_int = 3,
-        group_size = "automatic",
-        do_fisher_test = TRUE,
-        adjust_method = "fdr",
-        cores = NA) {
+.calc_spatial_enrichment_dt <- function(bin_matrix,
+    spatial_network,
+    calc_hub = FALSE,
+    hub_min_int = 3,
+    group_size = "automatic",
+    do_fisher_test = TRUE,
+    adjust_method = "fdr",
+    cores = NA) {
     # set number of cores automatically, but with limit of 10
     cores <- determine_cores(cores)
     data.table::setDTthreads(threads = cores)
@@ -542,13 +581,18 @@ NULL
     }
 
     groups <- ceiling(nrow(bin_matrix) / group_size)
-    cut_groups <- cut(1:nrow(bin_matrix), breaks = groups, labels = 1:groups)
+    cut_groups <- cut(seq_len(nrow(bin_matrix)),
+        breaks = groups,
+        labels = seq_len(groups)
+    )
     if (any(table(cut_groups) == 1)) {
-        stop("With group size = ", group_size,
+        stop(
+            "With group size = ", group_size,
             " you have a single gene in a group. Manually pick another group
-            size")
+            size"
+        )
     }
-    indexes <- 1:nrow(bin_matrix)
+    indexes <- seq_len(nrow(bin_matrix))
     names(indexes) <- cut_groups
 
 
@@ -559,7 +603,9 @@ NULL
         bin_matrix_DT <- data.table::as.data.table(bin_matrix[sel_indices, ])
         bin_matrix_DT[, feat_ID := rownames(bin_matrix[sel_indices, ])]
         bin_matrix_DTm <- data.table::melt.data.table(
-            bin_matrix_DT, id.vars = "feat_ID")
+            bin_matrix_DT,
+            id.vars = "feat_ID"
+        )
 
         if (do_fisher_test == TRUE) {
             test <- .spat_fish_func_dt(
@@ -589,7 +635,9 @@ NULL
         min_pvalue <- min(result$p.value[result$p.value > 0])
         result[, p.value := ifelse(p.value == 0, min_pvalue, p.value)]
         result[, adj.p.value := stats::p.adjust(
-            p.value, method = adjust_method)]
+            p.value,
+            method = adjust_method
+        )]
 
         result[, score := -log(p.value) * estimate]
         data.table::setorder(result, -score)
@@ -622,7 +670,6 @@ NULL
 #' @param bin_method method to binarize gene expression
 #' @param expression_values expression values to use
 #' @param subset_feats only select a subset of features to test
-#' @param subset_genes deprecated, use subset_feats
 #' @param spatial_network_name name of spatial network to use
 #' (default = 'spatial_network')
 #' @param spatial_network_k different k's for a spatial kNN to evaluate
@@ -715,40 +762,38 @@ NULL
 
 #' @rdname binSpect
 #' @export
-binSpect <- function(
-        gobject,
-        spat_unit = NULL,
-        feat_type = NULL,
-        bin_method = c("kmeans", "rank"),
-        expression_values = c("normalized", "scaled", "custom"),
-        subset_feats = NULL,
-        spatial_network_name = "Delaunay_network",
-        spatial_network_k = NULL,
-        reduce_network = FALSE,
-        kmeans_algo = c("kmeans", "kmeans_arma", "kmeans_arma_subset"),
-        nstart = 3,
-        iter_max = 10,
-        extreme_nr = 50,
-        sample_nr = 50,
-        percentage_rank = 30,
-        do_fisher_test = TRUE,
-        adjust_method = "fdr",
-        calc_hub = FALSE,
-        hub_min_int = 3,
-        get_av_expr = TRUE,
-        get_high_expr = TRUE,
-        implementation = c("data.table", "simple", "matrix"),
-        group_size = "automatic",
-        do_parallel = TRUE,
-        cores = NA,
-        verbose = TRUE,
-        knn_params = NULL,
-        set.seed = deprecated(),
-        seed = 1234,
-        bin_matrix = NULL,
-        summarize = c("p.value", "adj.p.value"),
-        return_gobject = FALSE
-) {
+binSpect <- function(gobject,
+    spat_unit = NULL,
+    feat_type = NULL,
+    bin_method = c("kmeans", "rank"),
+    expression_values = c("normalized", "scaled", "custom"),
+    subset_feats = NULL,
+    spatial_network_name = "Delaunay_network",
+    spatial_network_k = NULL,
+    reduce_network = FALSE,
+    kmeans_algo = c("kmeans", "kmeans_arma", "kmeans_arma_subset"),
+    nstart = 3,
+    iter_max = 10,
+    extreme_nr = 50,
+    sample_nr = 50,
+    percentage_rank = 30,
+    do_fisher_test = TRUE,
+    adjust_method = "fdr",
+    calc_hub = FALSE,
+    hub_min_int = 3,
+    get_av_expr = TRUE,
+    get_high_expr = TRUE,
+    implementation = c("data.table", "simple", "matrix"),
+    group_size = "automatic",
+    do_parallel = TRUE,
+    cores = NA,
+    verbose = TRUE,
+    knn_params = NULL,
+    set.seed = deprecated(),
+    seed = 1234,
+    bin_matrix = NULL,
+    summarize = c("p.value", "adj.p.value"),
+    return_gobject = FALSE) {
     # TODO align set.seed, set_seed, seed_number naming and usage across
     # packages
     # use only param seed. If NULL, set no seed. If !NULL set value as seed
@@ -769,7 +814,7 @@ binSpect <- function(
         "subset_feats", "reduce_network", "kmeans_algo",
         "nstart", "iter_max", "extreme_nr", "sample_nr",
         "percentage_rank", "do_fisher_test", "adjust_method",
-        "calc_hub" , "hub_min_int", "get_av_expr", "get_high_expr",
+        "calc_hub", "hub_min_int", "get_av_expr", "get_high_expr",
         "implementation", "group_size", "do_parallel", "cores", "seed",
         "verbose"
     ))
@@ -779,20 +824,20 @@ binSpect <- function(
             gobject = gobject,
             spatial_network_k = spatial_network_k,
             knn_params = knn_params,
-            summarize = summarize,
+            summarize = summarize
         ))
     } else {
         output <- do.call(binSpectSingle, args = c(a,
             gobject = gobject,
             spatial_network_name = spatial_network_name,
-            bin_matrix = bin_matrix,
+            bin_matrix = bin_matrix
         ))
     }
 
     if (isTRUE(return_gobject)) {
-
         result_dt <- data.table::data.table(
-            feats = output$feats, pval = output$adj.p.value)
+            feats = output$feats, pval = output$adj.p.value
+        )
         data.table::setnames(result_dt, old = "pval", new = "binSpect.pval")
         gobject <- addFeatMetadata(
             gobject,
@@ -817,30 +862,31 @@ binSpect <- function(
 #' @param expression_matrix expression matrix
 #' @param spatial_network spatial network in data.table format
 #' @export
-binSpectSingleMatrix <- function(expression_matrix,
-    spatial_network = NULL,
-    bin_matrix = NULL,
-    bin_method = c("kmeans", "rank"),
-    subset_feats = NULL,
-    kmeans_algo = c("kmeans", "kmeans_arma", "kmeans_arma_subset"),
-    nstart = 3,
-    iter_max = 10,
-    extreme_nr = 50,
-    sample_nr = 50,
-    percentage_rank = 30,
-    do_fisher_test = TRUE,
-    adjust_method = "fdr",
-    calc_hub = FALSE,
-    hub_min_int = 3,
-    get_av_expr = TRUE,
-    get_high_expr = TRUE,
-    implementation = c("data.table", "simple", "matrix"),
-    group_size = "automatic",
-    do_parallel = TRUE,
-    cores = NA,
-    verbose = FALSE,
-    set.seed = deprecated(),
-    seed = 1234) {
+binSpectSingleMatrix <- function(
+        expression_matrix,
+        spatial_network = NULL,
+        bin_matrix = NULL,
+        bin_method = c("kmeans", "rank"),
+        subset_feats = NULL,
+        kmeans_algo = c("kmeans", "kmeans_arma", "kmeans_arma_subset"),
+        nstart = 3,
+        iter_max = 10,
+        extreme_nr = 50,
+        sample_nr = 50,
+        percentage_rank = 30,
+        do_fisher_test = TRUE,
+        adjust_method = "fdr",
+        calc_hub = FALSE,
+        hub_min_int = 3,
+        get_av_expr = TRUE,
+        get_high_expr = TRUE,
+        implementation = c("data.table", "simple", "matrix"),
+        group_size = "automatic",
+        do_parallel = TRUE,
+        cores = NA,
+        verbose = FALSE,
+        set.seed = deprecated(),
+        seed = 1234) {
     if (is_present(set.seed) && !is.function(set.seed)) {
         deprecate_warn(
             when = "4.0.3",
@@ -874,11 +920,14 @@ binSpectSingleMatrix <- function(expression_matrix,
     # kmeans algorithm
     kmeans_algo <- match.arg(
         kmeans_algo,
-        choices = c("kmeans", "kmeans_arma", "kmeans_arma_subset"))
+        choices = c("kmeans", "kmeans_arma", "kmeans_arma_subset")
+    )
 
     # implementation
     implementation <- match.arg(
-        implementation, choices = c("data.table", "simple", "matrix"))
+        implementation,
+        choices = c("data.table", "simple", "matrix")
+    )
 
 
     # spatial network
@@ -974,7 +1023,8 @@ binSpectSingleMatrix <- function(expression_matrix,
         # expression
         if (!is.null(subset_feats)) {
             expr_values <- expression_matrix[
-                rownames(expression_matrix) %in% subset_feats, ]
+                rownames(expression_matrix) %in% subset_feats,
+            ]
         } else {
             expr_values <- expression_matrix
         }
@@ -984,7 +1034,8 @@ binSpectSingleMatrix <- function(expression_matrix,
             mean(x[x > 0])
         })
         av_expr_DT <- data.table::data.table(
-            feats = names(av_expr), av_expr = av_expr)
+            feats = names(av_expr), av_expr = av_expr
+        )
         result <- merge(result, av_expr_DT, by = "feats")
 
         vmsg(.v = verbose, "\n 3. (optional) average expression of high
@@ -999,7 +1050,8 @@ binSpectSingleMatrix <- function(expression_matrix,
     if (get_high_expr) {
         high_expr <- rowSums(bin_matrix)
         high_expr_DT <- data.table::data.table(
-            feats = names(high_expr), high_expr = high_expr)
+            feats = names(high_expr), high_expr = high_expr
+        )
         result <- merge(result, high_expr_DT, by = "feats")
 
         vmsg(.v = verbose, "\n 4. (optional) number of high expressing cells
@@ -1021,34 +1073,35 @@ binSpectSingleMatrix <- function(expression_matrix,
 
 #' @describeIn binSpect binSpect for a single spatial network
 #' @export
-binSpectSingle <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    bin_method = c("kmeans", "rank"),
-    expression_values = c("normalized", "scaled", "custom"),
-    subset_feats = NULL,
-    spatial_network_name = "Delaunay_network",
-    reduce_network = FALSE,
-    kmeans_algo = c("kmeans", "kmeans_arma", "kmeans_arma_subset"),
-    nstart = 3,
-    iter_max = 10,
-    extreme_nr = 50,
-    sample_nr = 50,
-    percentage_rank = 30,
-    do_fisher_test = TRUE,
-    adjust_method = "fdr",
-    calc_hub = FALSE,
-    hub_min_int = 3,
-    get_av_expr = TRUE,
-    get_high_expr = TRUE,
-    implementation = c("data.table", "simple", "matrix"),
-    group_size = "automatic",
-    do_parallel = TRUE,
-    cores = NA,
-    verbose = TRUE,
-    set.seed = deprecated(),
-    seed = 1234,
-    bin_matrix = NULL) {
+binSpectSingle <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        bin_method = c("kmeans", "rank"),
+        expression_values = c("normalized", "scaled", "custom"),
+        subset_feats = NULL,
+        spatial_network_name = "Delaunay_network",
+        reduce_network = FALSE,
+        kmeans_algo = c("kmeans", "kmeans_arma", "kmeans_arma_subset"),
+        nstart = 3,
+        iter_max = 10,
+        extreme_nr = 50,
+        sample_nr = 50,
+        percentage_rank = 30,
+        do_fisher_test = TRUE,
+        adjust_method = "fdr",
+        calc_hub = FALSE,
+        hub_min_int = 3,
+        get_av_expr = TRUE,
+        get_high_expr = TRUE,
+        implementation = c("data.table", "simple", "matrix"),
+        group_size = "automatic",
+        do_parallel = TRUE,
+        cores = NA,
+        verbose = TRUE,
+        set.seed = deprecated(),
+        seed = 1234,
+        bin_matrix = NULL) {
     ## deprecated arguments
 
     if (is_present(set.seed) && !is.function(set.seed)) {
@@ -1076,7 +1129,8 @@ binSpectSingle <- function(gobject,
     ## 1. expression matrix
     values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_values <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -1094,15 +1148,18 @@ binSpectSingle <- function(gobject,
         output = "networkDT"
     )
     if (is.null(spatial_network)) {
-        stop("spatial_network_name: ", spatial_network_name,
-            " does not exist, create a spatial network first")
+        stop(
+            "spatial_network_name: ", spatial_network_name,
+            " does not exist, create a spatial network first"
+        )
     }
 
     # convert to full network
     if (reduce_network == FALSE) {
         spatial_network <- convert_to_full_spatial_network(spatial_network)
         data.table::setnames(
-            spatial_network, c("source", "target"), c("from", "to"))
+            spatial_network, c("source", "target"), c("from", "to")
+        )
     }
 
 
@@ -1139,35 +1196,36 @@ binSpectSingle <- function(gobject,
 
 #' @describeIn binSpect binSpect for multiple spatial kNN networks
 #' @export
-binSpectMulti <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    bin_method = c("kmeans", "rank"),
-    expression_values = c("normalized", "scaled", "custom"),
-    subset_feats = NULL,
-    spatial_network_k = c(5, 10, 20),
-    reduce_network = FALSE,
-    kmeans_algo = c("kmeans", "kmeans_arma", "kmeans_arma_subset"),
-    nstart = 3,
-    iter_max = 10,
-    extreme_nr = 50,
-    sample_nr = 50,
-    percentage_rank = c(10, 30),
-    do_fisher_test = TRUE,
-    adjust_method = "fdr",
-    calc_hub = FALSE,
-    hub_min_int = 3,
-    get_av_expr = TRUE,
-    get_high_expr = TRUE,
-    implementation = c("data.table", "simple", "matrix"),
-    group_size = "automatic",
-    do_parallel = TRUE,
-    cores = NA,
-    verbose = TRUE,
-    knn_params = NULL,
-    set.seed = deprecated(),
-    seed = 1234,
-    summarize = c("adj.p.value", "p.value")) {
+binSpectMulti <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        bin_method = c("kmeans", "rank"),
+        expression_values = c("normalized", "scaled", "custom"),
+        subset_feats = NULL,
+        spatial_network_k = c(5, 10, 20),
+        reduce_network = FALSE,
+        kmeans_algo = c("kmeans", "kmeans_arma", "kmeans_arma_subset"),
+        nstart = 3,
+        iter_max = 10,
+        extreme_nr = 50,
+        sample_nr = 50,
+        percentage_rank = c(10, 30),
+        do_fisher_test = TRUE,
+        adjust_method = "fdr",
+        calc_hub = FALSE,
+        hub_min_int = 3,
+        get_av_expr = TRUE,
+        get_high_expr = TRUE,
+        implementation = c("data.table", "simple", "matrix"),
+        group_size = "automatic",
+        do_parallel = TRUE,
+        cores = NA,
+        verbose = TRUE,
+        knn_params = NULL,
+        set.seed = deprecated(),
+        seed = 1234,
+        summarize = c("adj.p.value", "p.value")) {
     ## deprecated arguments
     if (is_present(set.seed) && !is.function(set.seed)) {
         deprecate_warn(
@@ -1191,8 +1249,9 @@ binSpectMulti <- function(gobject,
         feat_type = feat_type
     )
 
-    if (verbose == TRUE)
+    if (verbose == TRUE) {
         message("This is the multi parameter version of binSpect")
+    }
 
     # set number of cores automatically, but with limit of 10
     cores <- determine_cores(cores)
@@ -1223,8 +1282,9 @@ binSpectMulti <- function(gobject,
             ))
 
             for (rank_i in percentage_rank) {
-                if (verbose == TRUE)
+                if (verbose == TRUE) {
                     cat("Run for k = ", k, " and rank % = ", rank_i)
+                }
 
                 result <- binSpectSingle(
                     gobject = temp_gobject,
@@ -1266,7 +1326,8 @@ binSpectMulti <- function(gobject,
         ## expression matrix
         values <- match.arg(
             expression_values,
-            unique(c("normalized", "scaled", "custom", expression_values)))
+            unique(c("normalized", "scaled", "custom", expression_values))
+        )
         expr_values <- getExpression(
             gobject = gobject,
             spat_unit = spat_unit,
@@ -1347,10 +1408,12 @@ binSpectMulti <- function(gobject,
     simple_result <- combined_result[, sum(log(get(summarize))), by = feats]
     simple_result[, V1 := V1 * -2]
     simple_result[, p.val := stats::pchisq(
-        q = V1, df = total_trials, log.p = FALSE, lower.tail = FALSE)]
+        q = V1, df = total_trials, log.p = FALSE, lower.tail = FALSE
+    )]
 
     return(list(
-        combined = combined_result, simple = simple_result[, .(feats, p.val)]))
+        combined = combined_result, simple = simple_result[, .(feats, p.val)]
+    ))
 }
 
 
@@ -1392,31 +1455,32 @@ binSpectMulti <- function(gobject,
 #' is set.
 #' @param summarize summarize the p-values or adjusted p-values
 #' @returns data.table with results
-binSpectMultiMatrix <- function(expression_matrix,
-    spatial_networks,
-    bin_method = c("kmeans", "rank"),
-    subset_feats = NULL,
-    kmeans_algo = c("kmeans", "kmeans_arma", "kmeans_arma_subset"),
-    nstart = 3,
-    iter_max = 10,
-    extreme_nr = 50,
-    sample_nr = 50,
-    percentage_rank = c(10, 30),
-    do_fisher_test = TRUE,
-    adjust_method = "fdr",
-    calc_hub = FALSE,
-    hub_min_int = 3,
-    get_av_expr = TRUE,
-    get_high_expr = TRUE,
-    implementation = c("data.table", "simple", "matrix"),
-    group_size = "automatic",
-    do_parallel = TRUE,
-    cores = NA,
-    verbose = TRUE,
-    knn_params = NULL,
-    set.seed = deprecated(),
-    seed = 1234,
-    summarize = c("adj.p.value", "p.value")) {
+binSpectMultiMatrix <- function(
+        expression_matrix,
+        spatial_networks,
+        bin_method = c("kmeans", "rank"),
+        subset_feats = NULL,
+        kmeans_algo = c("kmeans", "kmeans_arma", "kmeans_arma_subset"),
+        nstart = 3,
+        iter_max = 10,
+        extreme_nr = 50,
+        sample_nr = 50,
+        percentage_rank = c(10, 30),
+        do_fisher_test = TRUE,
+        adjust_method = "fdr",
+        calc_hub = FALSE,
+        hub_min_int = 3,
+        get_av_expr = TRUE,
+        get_high_expr = TRUE,
+        implementation = c("data.table", "simple", "matrix"),
+        group_size = "automatic",
+        do_parallel = TRUE,
+        cores = NA,
+        verbose = TRUE,
+        knn_params = NULL,
+        set.seed = deprecated(),
+        seed = 1234,
+        summarize = c("adj.p.value", "p.value")) {
     if (is_present(set.seed) && !is.function(set.seed)) {
         deprecate_warn(
             when = "4.0.3",
@@ -1429,8 +1493,9 @@ binSpectMultiMatrix <- function(expression_matrix,
     }
 
 
-    if (verbose == TRUE)
+    if (verbose == TRUE) {
         message("This is the multi parameter version of binSpect")
+    }
 
     # set number of cores automatically, but with limit of 10
     cores <- determine_cores(cores)
@@ -1451,8 +1516,9 @@ binSpectMultiMatrix <- function(expression_matrix,
 
         for (k in seq_along(spatial_networks)) {
             for (rank_i in percentage_rank) {
-                if (verbose == TRUE)
+                if (verbose == TRUE) {
                     cat("Run for spatial network ", k, " and rank % = ", rank_i)
+                }
 
                 result <- binSpectSingleMatrix(
                     expression_matrix = expression_matrix,
@@ -1545,10 +1611,12 @@ binSpectMultiMatrix <- function(expression_matrix,
     simple_result <- combined_result[, sum(log(get(summarize))), by = feats]
     simple_result[, V1 := V1 * -2]
     simple_result[, p.val := stats::pchisq(
-        q = V1, df = total_trials, log.p = FALSE, lower.tail = FALSE)]
+        q = V1, df = total_trials, log.p = FALSE, lower.tail = FALSE
+    )]
 
     return(list(
-        combined = combined_result, simple = simple_result[, .(feats, p.val)]))
+        combined = combined_result, simple = simple_result[, .(feats, p.val)]
+    ))
 }
 
 
@@ -1576,16 +1644,17 @@ binSpectMultiMatrix <- function(expression_matrix,
 #' @returns data.table with spatial scores
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' silhouetteRank(g)
 #' @export
-silhouetteRank <- function(gobject,
-    expression_values = c("normalized", "scaled", "custom"),
-    metric = "euclidean",
-    subset_genes = NULL,
-    rbp_p = 0.95,
-    examine_top = 0.3,
-    python_path = NULL) {
+silhouetteRank <- function(
+        gobject,
+        expression_values = c("normalized", "scaled", "custom"),
+        metric = "euclidean",
+        subset_genes = NULL,
+        rbp_p = 0.95,
+        examine_top = 0.3,
+        python_path = NULL) {
     # expression values
     values <- match.arg(expression_values, c("normalized", "scaled", "custom"))
     expr_values <- getExpression(
@@ -1621,7 +1690,9 @@ silhouetteRank <- function(gobject,
     ## prepare python path and louvain script
     reticulate::use_python(required = TRUE, python = python_path)
     python_silh_function <- system.file(
-        "python", "python_spatial_genes.py", package = "Giotto")
+        "python", "python_spatial_genes.py",
+        package = "Giotto"
+    )
     reticulate::source_python(file = python_silh_function)
 
     output_python <- python_spatial_genes(
@@ -1667,21 +1738,22 @@ silhouetteRank <- function(gobject,
 #' @returns data.table with spatial scores
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' silhouetteRankTest(g)
 #' @export
-silhouetteRankTest <- function(gobject,
-    expression_values = c("normalized", "scaled", "custom"),
-    subset_genes = NULL,
-    overwrite_input_bin = TRUE,
-    rbp_ps = c(0.95, 0.99),
-    examine_tops = c(0.005, 0.010, 0.050, 0.100, 0.300),
-    matrix_type = "dissim",
-    num_core = 4,
-    parallel_path = "/usr/bin",
-    output = NULL,
-    query_sizes = 10L,
-    verbose = FALSE) {
+silhouetteRankTest <- function(
+        gobject,
+        expression_values = c("normalized", "scaled", "custom"),
+        subset_genes = NULL,
+        overwrite_input_bin = TRUE,
+        rbp_ps = c(0.95, 0.99),
+        examine_tops = c(0.005, 0.010, 0.050, 0.100, 0.300),
+        matrix_type = "dissim",
+        num_core = 4,
+        parallel_path = "/usr/bin",
+        output = NULL,
+        query_sizes = 10L,
+        verbose = FALSE) {
     # data.table variables
     cell_ID <- sdimx <- sdimy <- sdimz <- NULL
 
@@ -1696,7 +1768,6 @@ silhouetteRankTest <- function(gobject,
             "To install: \n",
             "install.packages('eva')"
         )
-
     }
 
     ## test if python package is installed
@@ -1747,16 +1818,28 @@ silhouetteRankTest <- function(gobject,
     if (is.null(output)) {
         save_dir <- readGiottoInstructions(gobject, param = "save_dir")
         silh_output_dir <- paste0(save_dir, "/", "silhouetteRank_output/")
-        if (!file.exists(silh_output_dir)) dir.create(
-            silh_output_dir, recursive = TRUE)
+        if (!file.exists(silh_output_dir)) {
+            dir.create(
+                silh_output_dir,
+                recursive = TRUE
+            )
+        }
     } else if (file.exists(output)) {
         silh_output_dir <- paste0(output, "/", "silhouetteRank_output/")
-        if (!file.exists(silh_output_dir)) dir.create(
-            silh_output_dir, recursive = TRUE)
+        if (!file.exists(silh_output_dir)) {
+            dir.create(
+                silh_output_dir,
+                recursive = TRUE
+            )
+        }
     } else {
         silh_output_dir <- paste0(output, "/", "silhouetteRank_output/")
-        if (!file.exists(silh_output_dir)) dir.create(
-            silh_output_dir, recursive = TRUE)
+        if (!file.exists(silh_output_dir)) {
+            dir.create(
+                silh_output_dir,
+                recursive = TRUE
+            )
+        }
     }
 
     # log directory
@@ -1785,8 +1868,11 @@ silhouetteRankTest <- function(gobject,
     silh_output_dir_norm <- normalizePath(silh_output_dir)
     expr_values_path_norm <- paste0(silh_output_dir_norm, "/", "expression.txt")
 
-    data.table::fwrite(data.table::as.data.table(
-        expr_values, keep.rownames = "gene"),
+    data.table::fwrite(
+        data.table::as.data.table(
+            expr_values,
+            keep.rownames = "gene"
+        ),
         file = expr_values_path_norm,
         quote = FALSE,
         sep = "\t",
@@ -1800,7 +1886,9 @@ silhouetteRankTest <- function(gobject,
     python_path <- readGiottoInstructions(gobject, param = "python_path")
     reticulate::use_python(required = TRUE, python = python_path)
     python_silh_function <- system.file(
-        "python", "silhouette_rank_wrapper.py", package = "Giotto")
+        "python", "silhouette_rank_wrapper.py",
+        package = "Giotto"
+    )
     reticulate::source_python(file = python_silh_function)
 
 
@@ -1847,29 +1935,30 @@ silhouetteRankTest <- function(gobject,
 #' @param default_save_name default save name for saving, don't change,
 #' change save_name in save_param
 #' @returns a list of data.frames with results and plot (optional)
-#' @details This function is a wrapper for the SpatialDE method originally 
+#' @details This function is a wrapper for the SpatialDE method originally
 #' implemented
 #' in python. See publication \doi{10.1038/nmeth.4636}
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' spatialDE(g)
 #' @export
-spatialDE <- function(gobject = NULL,
-    feat_type = NULL,
-    spat_unit = NULL,
-    spat_loc_name = "raw",
-    expression_values = c("raw", "normalized", "scaled", "custom"),
-    size = c(4, 2, 1),
-    color = c("blue", "green", "red"),
-    sig_alpha = 0.5,
-    unsig_alpha = 0.5,
-    python_path = NULL,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "SpatialDE") {
+spatialDE <- function(
+        gobject = NULL,
+        feat_type = NULL,
+        spat_unit = NULL,
+        spat_loc_name = "raw",
+        expression_values = c("raw", "normalized", "scaled", "custom"),
+        size = c(4, 2, 1),
+        color = c("blue", "green", "red"),
+        sig_alpha = 0.5,
+        unsig_alpha = 0.5,
+        python_path = NULL,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "SpatialDE") {
     # test if SPARK is installed ##
 
     module_test <- reticulate::py_module_available("SpatialDE")
@@ -1919,7 +2008,8 @@ spatialDE <- function(gobject = NULL,
 
     # expression
     values <- match.arg(
-        expression_values, c("raw", "normalized", "scaled", "custom"))
+        expression_values, c("raw", "normalized", "scaled", "custom")
+    )
     expr_values <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -1936,7 +2026,9 @@ spatialDE <- function(gobject = NULL,
     ## source python file
     reticulate::use_python(required = TRUE, python = python_path)
     reader_path <- system.file(
-        "python", "SpatialDE_wrapper.py", package = "Giotto")
+        "python", "SpatialDE_wrapper.py",
+        package = "Giotto"
+    )
     reticulate::source_python(file = reader_path)
 
     ## get spatial locations
@@ -1951,13 +2043,15 @@ spatialDE <- function(gobject = NULL,
 
     ## run spatialDE
     Spatial_DE_results <- Spatial_DE(
-        as.data.frame(t(as.matrix(expr_values))), spatial_locs)
+        as.data.frame(t(as.matrix(expr_values))), spatial_locs
+    )
 
     results <- as.data.frame(reticulate::py_to_r(Spatial_DE_results[[1]]))
 
     if (length(Spatial_DE_results) == 2) {
         ms_results <- as.data.frame(
-            reticulate::py_to_r(Spatial_DE_results[[2]]))
+            reticulate::py_to_r(Spatial_DE_results[[2]])
+        )
         spatial_genes_results <- list(results, ms_results)
         names(spatial_genes_results) <- c("results", "ms_results")
     } else {
@@ -1968,11 +2062,17 @@ spatialDE <- function(gobject = NULL,
 
     # print, return and save parameters
     show_plot <- ifelse(is.na(show_plot), readGiottoInstructions(
-        gobject, param = "show_plot"), show_plot)
+        gobject,
+        param = "show_plot"
+    ), show_plot)
     save_plot <- ifelse(is.na(save_plot), readGiottoInstructions(
-        gobject, param = "save_plot"), save_plot)
+        gobject,
+        param = "save_plot"
+    ), save_plot)
     return_plot <- ifelse(is.na(return_plot), readGiottoInstructions(
-        gobject, param = "return_plot"), return_plot)
+        gobject,
+        param = "return_plot"
+    ), return_plot)
 
     ## create plot
     if (isTRUE(show_plot) ||
@@ -1997,8 +2097,11 @@ spatialDE <- function(gobject = NULL,
     if (save_plot == TRUE) {
         do.call(
             "all_plots_save_function",
-            c(list(gobject = gobject, plot_object = FSV_plot,
-                default_save_name = default_save_name), save_param))
+            c(list(
+                gobject = gobject, plot_object = FSV_plot,
+                default_save_name = default_save_name
+            ), save_param)
+        )
     }
 
     ## return results and plot (optional)
@@ -2025,24 +2128,25 @@ spatialDE <- function(gobject = NULL,
 #' @param python_path specify specific path to python if required
 #' @param return_gobject show plot
 #' @returns An updated giotto object
-#' @details This function is a wrapper for the SpatialAEH method 
+#' @details This function is a wrapper for the SpatialAEH method
 #' implemented in the ...
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' spatialAEH(g)
 #' @export
-spatialAEH <- function(gobject = NULL,
-    feat_type = NULL,
-    spat_unit = NULL,
-    spat_loc_name = "raw",
-    SpatialDE_results = NULL,
-    name_pattern = "AEH_patterns",
-    expression_values = c("raw", "normalized", "scaled", "custom"),
-    pattern_num = 6,
-    l = 1.05,
-    python_path = NULL,
-    return_gobject = TRUE) {
+spatialAEH <- function(
+        gobject = NULL,
+        feat_type = NULL,
+        spat_unit = NULL,
+        spat_loc_name = "raw",
+        SpatialDE_results = NULL,
+        name_pattern = "AEH_patterns",
+        expression_values = c("raw", "normalized", "scaled", "custom"),
+        pattern_num = 6,
+        l = 1.05,
+        python_path = NULL,
+        return_gobject = TRUE) {
     # data.table variables
     cell_ID <- NULL
 
@@ -2059,7 +2163,8 @@ spatialAEH <- function(gobject = NULL,
 
     # expression
     values <- match.arg(
-        expression_values, c("raw", "normalized", "scaled", "custom"))
+        expression_values, c("raw", "normalized", "scaled", "custom")
+    )
     expr_values <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -2075,7 +2180,9 @@ spatialAEH <- function(gobject = NULL,
     ## source python file
     reticulate::use_python(required = TRUE, python = python_path)
     reader_path <- system.file(
-        "python", "SpatialDE_wrapper.py", package = "Giotto")
+        "python", "SpatialDE_wrapper.py",
+        package = "Giotto"
+    )
     reticulate::source_python(file = reader_path)
 
 
@@ -2104,14 +2211,17 @@ spatialAEH <- function(gobject = NULL,
 
     spatial_pattern_results <- list(histology_results, cell_pattern_score)
     names(spatial_pattern_results) <- c(
-        "histology_results", "cell_pattern_score")
+        "histology_results", "cell_pattern_score"
+    )
 
 
     if (return_gobject == TRUE) {
         dt_res <- data.table::as.data.table(
-            spatial_pattern_results[["cell_pattern_score"]])
+            spatial_pattern_results[["cell_pattern_score"]]
+        )
         dt_res[["cell_ID"]] <- rownames(
-            spatial_pattern_results[["cell_pattern_score"]])
+            spatial_pattern_results[["cell_pattern_score"]]
+        )
         gobject@spatial_enrichment[[name_pattern]] <- dt_res
         return(gobject)
     } else {
@@ -2131,15 +2241,18 @@ spatialAEH <- function(gobject = NULL,
 #' @param unsig_alpha transparency of unsignificant genes
 #' @returns ggplot object
 #' @keywords internal
-FSV_show <- function(results,
-    ms_results = NULL,
-    size = c(4, 2, 1),
-    color = c("blue", "green", "red"),
-    sig_alpha = 0.5,
-    unsig_alpha = 0.5) {
+FSV_show <- function(
+        results,
+        ms_results = NULL,
+        size = c(4, 2, 1),
+        color = c("blue", "green", "red"),
+        sig_alpha = 0.5,
+        unsig_alpha = 0.5) {
     results$FSV95conf <- 2 * sqrt(results$s2_FSV)
     results$intervals <- cut(
-        results$FSV95conf, c(0, 1e-1, 1e0, Inf), label = FALSE)
+        results$FSV95conf, c(0, 1e-1, 1e0, Inf),
+        label = FALSE
+    )
     results$log_pval <- log10(results$pval)
 
     if (is.null(ms_results)) {
@@ -2160,7 +2273,8 @@ FSV_show <- function(results,
     pl <- pl + ggplot2::geom_point(
         data = results[results$qval < 0.05, ],
         ggplot2::aes_string(
-            x = "FSV", y = "log_pval", fill = "model_bic", size = "intervals"),
+            x = "FSV", y = "log_pval", fill = "model_bic", size = "intervals"
+        ),
         show.legend = TRUE, shape = 21, alpha = sig_alpha,
         stroke = 0.1, color = "black"
     ) +
@@ -2179,10 +2293,12 @@ FSV_show <- function(results,
             labels = c("linear", "periodical", "general")
         ) +
         ggplot2::geom_hline(yintercept = max(results[
-            results$qval < 0.05, ]$log_pval), linetype = "dashed") +
+            results$qval < 0.05,
+        ]$log_pval), linetype = "dashed") +
         ggplot2::geom_text(ggplot2::aes(0.9, max(results[
-            results$qval < 0.05, ]$log_pval),
-            label = "FDR = 0.05", vjust = -1
+            results$qval < 0.05,
+        ]$log_pval),
+        label = "FDR = 0.05", vjust = -1
         )) +
         ggplot2::scale_y_reverse()
 
@@ -2208,23 +2324,24 @@ FSV_show <- function(results,
 #' @param \dots Additional parameters to the
 #' \code{\link[trendsceek]{trendsceek_test}} function
 #' @returns data.frame with trendsceek spatial genes results
-#' @details This function is a wrapper for the trendsceek_test method 
+#' @details This function is a wrapper for the trendsceek_test method
 #' implemented in the trendsceek package
 #' Publication: \doi{10.1038/nmeth.4634}
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' trendSceek(g)
 #' @export
-trendSceek <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    spat_loc_name = "raw",
-    expression_values = c("normalized", "raw"),
-    subset_genes = NULL,
-    nrand = 100,
-    ncores = 8,
-    ...) {
+trendSceek <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        spat_loc_name = "raw",
+        expression_values = c("normalized", "raw"),
+        subset_genes = NULL,
+        nrand = 100,
+        ncores = 8,
+        ...) {
     # verify if optional package is installed
     package_check(
         pkg_name = "trendsceek",
@@ -2294,7 +2411,9 @@ trendSceek <- function(gobject,
 
     ## run trendsceek
     trendsceektest <- trendsceek::trendsceek_test(
-        pp, nrand = nrand, ncores = ncores, ...)
+        pp,
+        nrand = nrand, ncores = ncores, ...
+    )
 
     ## get final results
     trendsceektest <- trendsceektest$supstats_wide
@@ -2322,7 +2441,7 @@ trendSceek <- function(gobject,
 #' @param \dots Additional parameters to the \code{\link[SPARK]{spark.vc}}
 #' function
 #' @returns data.table with SPARK spatial genes results or the SPARK object
-#' @details This function is a wrapper for the method implemented in the 
+#' @details This function is a wrapper for the method implemented in the
 #' SPARK package:
 #' \pkg{SPARK} package:
 #'   1. **CreateSPARKObject** create a SPARK object from a giotto object
@@ -2334,20 +2453,21 @@ trendSceek <- function(gobject,
 #' @md
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' spark(g)
 #' @export
-spark <- function(gobject,
-    spat_loc_name = "raw",
-    feat_type = NULL,
-    spat_unit = NULL,
-    percentage = 0.1,
-    min_count = 10,
-    expression_values = "raw",
-    num_core = 5,
-    covariates = NULL,
-    return_object = c("data.table", "spark"),
-    ...) {
+spark <- function(
+        gobject,
+        spat_loc_name = "raw",
+        feat_type = NULL,
+        spat_unit = NULL,
+        percentage = 0.1,
+        min_count = 10,
+        expression_values = "raw",
+        num_core = 5,
+        covariates = NULL,
+        return_object = c("data.table", "spark"),
+        ...) {
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -2400,7 +2520,7 @@ spark <- function(gobject,
     ## create SPARK object for analysis and filter out lowly expressed genes
     sobject <- SPARK::CreateSPARKObject(
         counts = expr,
-        location = locs[, 1:2],
+        location = locs[, seq_len(2)],
         percentage = percentage,
         min_total_counts = min_count
     )
@@ -2483,26 +2603,28 @@ spark <- function(gobject,
 #' @details
 #' Steps to identify spatial patterns:
 #' \itemize{
-#'   \item{1. average gene expression for cells within a grid, see createSpatialGrid}
-#'   \item{2. perform PCA on the average grid expression profiles}
-#'   \item{3. convert variance of principlal components (PCs) to z-scores and select PCs based on a z-score threshold}
+#'   * 1. average gene expression for cells within a grid, see createSpatialGrid
+#'   * 2. perform PCA on the average grid expression profiles
+#'   * 3. convert variance of principal components (PCs) to z-scores and
+#'   select PCs based on a z-score threshold
 #' }
 #' @export
-detectSpatialPatterns <- function(gobject,
-    expression_values = c("normalized", "scaled", "custom"),
-    spatial_grid_name = "spatial_grid",
-    min_cells_per_grid = 4,
-    scale_unit = FALSE,
-    ncp = 100,
-    show_plot = TRUE,
-    PC_zscore = 1.5) {
+detectSpatialPatterns <- function(
+        gobject,
+        expression_values = c("normalized", "scaled", "custom"),
+        spatial_grid_name = "spatial_grid",
+        min_cells_per_grid = 4,
+        scale_unit = FALSE,
+        ncp = 100,
+        show_plot = TRUE,
+        PC_zscore = 1.5) {
     ############################################################################
     stop(wrap_txt(
-    "This function has not been updated for use with the current version
+        "This function has not been updated for use with the current version
     of Giotto.
     See details:
     https://github.com/drieslab/Giotto/issues/666#issuecomment-1540447537",
-    errWidth = TRUE
+        errWidth = TRUE
     ))
     ############################################################################
     # expression values to be used
@@ -2535,10 +2657,12 @@ detectSpatialPatterns <- function(gobject,
 
     if (all(c("sdimx", "sdimy", "sdimz") %in% colnames(spatial_locs))) {
         spatial_locs <- annotate_spatlocs_with_spatgrid_3D(
-            spatloc = spatial_locs, spatgrid = spatial_grid)
+            spatloc = spatial_locs, spatgrid = spatial_grid
+        )
     } else if (all(c("sdimx", "sdimy") %in% colnames(spatial_locs))) {
         spatial_locs <- annotate_spatlocs_with_spatgrid_2D(
-            spatloc = spatial_locs, spatgrid = spatial_grid)
+            spatloc = spatial_locs, spatgrid = spatial_grid
+        )
     }
 
 
@@ -2573,7 +2697,8 @@ detectSpatialPatterns <- function(gobject,
         X = t(loc_av_expr_matrix),
         scale.unit = scale_unit,
         ncp = ncp,
-        graph = FALSE)
+        graph = FALSE
+    )
 
     # screeplot
     screeplot <- factoextra::fviz_eig(mypca, addlabels = TRUE, ylim = c(0, 50))
@@ -2605,7 +2730,8 @@ detectSpatialPatterns <- function(gobject,
         data.table::setnames(pca_matrix_DT, old = "dimkeep", dims_to_keep)
     } else {
         pca_matrix_DT <- data.table::as.data.table(pca_matrix[
-            , seq_along(dims_to_keep)])
+            , seq_along(dims_to_keep)
+        ])
         pca_matrix_DT[, loc_ID := colnames(loc_av_expr_matrix)]
     }
 
@@ -2620,7 +2746,8 @@ detectSpatialPatterns <- function(gobject,
         data.table::setnames(feat_matrix_DT, old = "featkeep", dims_to_keep)
     } else {
         feat_matrix_DT <- data.table::as.data.table(feat_matrix[
-            , seq_along(dims_to_keep)])
+            , seq_along(dims_to_keep)
+        ])
         feat_matrix_DT[, gene_ID := rownames(loc_av_expr_matrix)]
     }
 
@@ -2658,19 +2785,20 @@ detectSpatialPatterns <- function(gobject,
 #' change save_name in save_param
 #' @returns ggplot
 #' @export
-showPattern2D <- function(gobject,
-    spatPatObj,
-    dimension = 1,
-    trim = c(0.02, 0.98),
-    background_color = "white",
-    grid_border_color = "grey",
-    show_legend = TRUE,
-    point_size = 1,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "showPattern2D") {
+showPattern2D <- function(
+        gobject,
+        spatPatObj,
+        dimension = 1,
+        trim = c(0.02, 0.98),
+        background_color = "white",
+        grid_border_color = "grey",
+        show_legend = TRUE,
+        point_size = 1,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "showPattern2D") {
     if (!"spatPatObj" %in% class(spatPatObj)) {
         stop("spatPatObj needs to be the output from detectSpatialPatterns")
     }
@@ -2685,16 +2813,21 @@ showPattern2D <- function(gobject,
 
     # annotate grid with PC values
     annotated_grid <- merge(
-        spatPatObj$spatial_grid, by.x = "gr_name", PC_DT, by.y = "loc_ID")
+        spatPatObj$spatial_grid,
+        by.x = "gr_name", PC_DT, by.y = "loc_ID"
+    )
 
     # trim PC values
     if (!is.null(trim)) {
         boundaries <- stats::quantile(annotated_grid[[
-            selected_PC]], probs = trim)
+            selected_PC
+        ]], probs = trim)
         annotated_grid[[selected_PC]][annotated_grid[[
-            selected_PC]] < boundaries[1]] <- boundaries[1]
+            selected_PC
+        ]] < boundaries[1]] <- boundaries[1]
         annotated_grid[[selected_PC]][annotated_grid[[
-            selected_PC]] > boundaries[2]] <- boundaries[2]
+            selected_PC
+        ]] > boundaries[2]] <- boundaries[2]
     }
 
     # 2D-plot
@@ -2772,24 +2905,25 @@ showPattern <- function(gobject, spatPatObj, ...) {
 #' change save_name in save_param
 #' @returns plotly
 #' @export
-showPattern3D <- function(gobject,
-    spatPatObj,
-    dimension = 1,
-    trim = c(0.02, 0.98),
-    background_color = "white",
-    grid_border_color = "grey",
-    show_legend = TRUE,
-    point_size = 1,
-    axis_scale = c("cube", "real", "custom"),
-    custom_ratio = NULL,
-    x_ticks = NULL,
-    y_ticks = NULL,
-    z_ticks = NULL,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "showPattern3D") {
+showPattern3D <- function(
+        gobject,
+        spatPatObj,
+        dimension = 1,
+        trim = c(0.02, 0.98),
+        background_color = "white",
+        grid_border_color = "grey",
+        show_legend = TRUE,
+        point_size = 1,
+        axis_scale = c("cube", "real", "custom"),
+        custom_ratio = NULL,
+        x_ticks = NULL,
+        y_ticks = NULL,
+        z_ticks = NULL,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "showPattern3D") {
     # data.table variables
     center_x <- x_start <- x_end <- center_y <- y_start <- y_end <-
         center_z <- z_start <- z_end <- NULL
@@ -2808,16 +2942,21 @@ showPattern3D <- function(gobject,
 
     # annotate grid with PC values
     annotated_grid <- merge(
-        spatPatObj$spatial_grid, by.x = "gr_name", PC_DT, by.y = "loc_ID")
+        spatPatObj$spatial_grid,
+        by.x = "gr_name", PC_DT, by.y = "loc_ID"
+    )
 
     # trim PC values
     if (!is.null(trim)) {
         boundaries <- stats::quantile(annotated_grid[[
-            selected_PC]], probs = trim)
+            selected_PC
+        ]], probs = trim)
         annotated_grid[[selected_PC]][annotated_grid[[
-            selected_PC]] < boundaries[1]] <- boundaries[1]
+            selected_PC
+        ]] < boundaries[1]] <- boundaries[1]
         annotated_grid[[selected_PC]][annotated_grid[[
-            selected_PC]] > boundaries[2]] <- boundaries[2]
+            selected_PC
+        ]] > boundaries[2]] <- boundaries[2]
     }
 
 
@@ -2852,7 +2991,8 @@ showPattern3D <- function(gobject,
         )
     ))
     dpl <- dpl %>% plotly::colorbar(
-        title = paste(paste("dim.", dimension, sep = ""), "genes", sep = " "))
+        title = paste(paste("dim.", dimension, sep = ""), "genes", sep = " ")
+    )
 
     # output plot
     return(GiottoVisuals::plot_output_handler(
@@ -2890,18 +3030,19 @@ showPattern3D <- function(gobject,
 #' change save_name in save_param
 #' @returns ggplot
 #' @export
-showPatternGenes <- function(gobject,
-    spatPatObj,
-    dimension = 1,
-    top_pos_genes = 5,
-    top_neg_genes = 5,
-    point_size = 1,
-    return_DT = FALSE,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "showPatternGenes") {
+showPatternGenes <- function(
+        gobject,
+        spatPatObj,
+        dimension = 1,
+        top_pos_genes = 5,
+        top_neg_genes = 5,
+        point_size = 1,
+        return_DT = FALSE,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "showPatternGenes") {
     # data.table variables
     gene_ID <- NULL
 
@@ -2921,11 +3062,14 @@ showPatternGenes <- function(gobject,
 
     # order and subset
     gene_cor_DT <- gene_cor_DT[
-        !is.na(get(selected_PC))][order(get(selected_PC))]
+        !is.na(get(selected_PC))
+    ][order(get(selected_PC))]
 
     subset <- gene_cor_DT[
-        c(1:top_neg_genes, (nrow(
-            gene_cor_DT) - top_pos_genes):nrow(gene_cor_DT))]
+        c(seq_len(top_neg_genes), (nrow(
+            gene_cor_DT
+        ) - top_pos_genes):nrow(gene_cor_DT))
+    ]
     subset[, gene_ID := factor(gene_ID, gene_ID)]
 
     ## return DT and make not plot ##
@@ -2937,7 +3081,8 @@ showPatternGenes <- function(gobject,
     pl <- pl + ggplot2::theme_classic()
     pl <- pl + ggplot2::geom_point(
         data = subset,
-        aes_string(x = selected_PC, y = "gene_ID"), size = point_size)
+        aes_string(x = selected_PC, y = "gene_ID"), size = point_size
+    )
     pl <- pl + ggplot2::geom_vline(xintercept = 0, linetype = 2)
     pl <- pl + ggplot2::labs(x = "correlation", y = "", title = selected_PC)
     pl <- pl + ggplot2::theme(plot.title = element_text(hjust = 0.5))
@@ -2970,13 +3115,14 @@ showPatternGenes <- function(gobject,
 #' @returns Data.table with genes associated with selected dimension (PC).
 #' @details Description.
 #' @export
-selectPatternGenes <- function(spatPatObj,
-    dimensions = 1:5,
-    top_pos_genes = 10,
-    top_neg_genes = 10,
-    min_pos_cor = 0.5,
-    min_neg_cor = -0.5,
-    return_top_selection = FALSE) {
+selectPatternGenes <- function(
+        spatPatObj,
+        dimensions = 1:5,
+        top_pos_genes = 10,
+        top_neg_genes = 10,
+        min_pos_cor = 0.5,
+        min_neg_cor = -0.5,
+        return_top_selection = FALSE) {
     if (!"spatPatObj" %in% class(spatPatObj)) {
         stop("spatPatObj needs to be the output from detectSpatialPatterns")
     }
@@ -2996,11 +3142,15 @@ selectPatternGenes <- function(spatPatObj,
 
     # melt and select
     gene_cor_DT_m <- data.table::melt.data.table(
-        gene_cor_DT, id.vars = "gene_ID")
+        gene_cor_DT,
+        id.vars = "gene_ID"
+    )
     gene_cor_DT_m[, top_pos_rank := rank(value), by = "variable"]
     gene_cor_DT_m[, top_neg_rank := rank(-value), by = "variable"]
     selection <- gene_cor_DT_m[
-        top_pos_rank %in% 1:top_pos_genes | top_neg_rank %in% 1:top_neg_genes]
+        top_pos_rank %in% seq_len(top_pos_genes) |
+            top_neg_rank %in% seq_len(top_neg_genes)
+    ]
 
     # filter on min correlation
     selection <- selection[value > min_pos_cor | value < min_neg_cor]
@@ -3017,9 +3167,11 @@ selectPatternGenes <- function(spatPatObj,
     # add other genes back
     output_selection <- uniq_selection[, .(gene_ID, variable)]
     other_genes <- gene_cor_DT[!gene_ID %in% output_selection$gene_ID][[
-        "gene_ID"]]
+        "gene_ID"
+    ]]
     other_genes_DT <- data.table::data.table(
-        gene_ID = other_genes, variable = "noDim")
+        gene_ID = other_genes, variable = "noDim"
+    )
 
     comb_output_genes <- rbind(output_selection, other_genes_DT)
     setnames(comb_output_genes, "variable", "patDim")
@@ -3040,10 +3192,9 @@ selectPatternGenes <- function(spatPatObj,
 #' @title do_spatial_knn_smoothing
 #' @name do_spatial_knn_smoothing
 #' @description smooth gene expression over a kNN spatial network
-#' @param gobject giotto object
-#' @param expression_values gene expression values to use
+#' @param expression_matrix gene expression values to use
 #' @param subset_feats subset of features to use
-#' @param spatial_network_name name of spatial network to use
+#' @param spatial_network spatial network to use
 #' @param b smoothing factor beteen 0 and 1 (default: automatic)
 #' @returns matrix with smoothened gene expression values based on kNN
 #' spatial network
@@ -3053,10 +3204,11 @@ selectPatternGenes <- function(spatPatObj,
 #' number of k-neighbors in the selected spatial network. Setting b = 0 means
 #' no smoothing and b = 1 means no contribution from its own expression.
 #' @keywords internal
-do_spatial_knn_smoothing <- function(expression_matrix,
-    spatial_network,
-    subset_feats = NULL,
-    b = NULL) {
+do_spatial_knn_smoothing <- function(
+        expression_matrix,
+        spatial_network,
+        subset_feats = NULL,
+        b = NULL) {
     # checks
     if (!is.null(b)) {
         if (b > 1 | b < 0) {
@@ -3084,7 +3236,9 @@ do_spatial_knn_smoothing <- function(expression_matrix,
     expr_values_dt <- data.table::as.data.table(as.matrix(expr_values))
     expr_values_dt[, feat_ID := rownames(expr_values)]
     expr_values_dt_m <- data.table::melt.data.table(
-        expr_values_dt, id.vars = "feat_ID", variable.name = "cell_ID")
+        expr_values_dt,
+        id.vars = "feat_ID", variable.name = "cell_ID"
+    )
 
 
     # merge spatial network and matrix
@@ -3098,13 +3252,16 @@ do_spatial_knn_smoothing <- function(expression_matrix,
     # exclude 0's?
     # trimmed mean?
     spatial_network_ext_smooth <- spatial_network_ext[
-        , mean(value), by = c("to", "feat_ID")]
+        , mean(value),
+        by = c("to", "feat_ID")
+    ]
 
     # convert back to matrix
     spatial_smooth_dc <- data.table::dcast.data.table(
         data = spatial_network_ext_smooth,
         formula = feat_ID ~ to,
-        value.var = "V1")
+        value.var = "V1"
+    )
     spatial_smooth_matrix <- dt_to_matrix(spatial_smooth_dc)
 
     # if network was not fully connected, some cells might be missing and
@@ -3117,11 +3274,13 @@ do_spatial_knn_smoothing <- function(expression_matrix,
     if (length(missing_cells) > 0) {
         missing_matrix <- expr_values[, missing_cells]
         spatial_smooth_matrix <- cbind(spatial_smooth_matrix[
-            rownames(expr_values), ], missing_matrix)
+            rownames(expr_values),
+        ], missing_matrix)
     }
 
     spatial_smooth_matrix <- spatial_smooth_matrix[
-        rownames(expr_values), colnames(expr_values)]
+        rownames(expr_values), colnames(expr_values)
+    ]
 
     # combine original and smoothed values according to smoothening b
     # create best guess for b if not given
@@ -3169,11 +3328,12 @@ evaluate_provided_spatial_locations <- function(spatial_locs) {
 #' @description smooth gene expression over a defined spatial grid
 #' @returns matrix with smoothened gene expression values based on spatial grid
 #' @keywords internal
-do_spatial_grid_averaging <- function(expression_matrix,
-    spatial_grid,
-    spatial_locs,
-    subset_feats = NULL,
-    min_cells_per_grid = 4) {
+do_spatial_grid_averaging <- function(
+        expression_matrix,
+        spatial_grid,
+        spatial_locs,
+        subset_feats = NULL,
+        min_cells_per_grid = 4) {
     # matrix
     expr_values <- expression_matrix
     if (!is.null(subset_feats)) {
@@ -3191,10 +3351,12 @@ do_spatial_grid_averaging <- function(expression_matrix,
     # annoate spatial locations with spatial grid
     if (all(c("sdimx", "sdimy", "sdimz") %in% colnames(spatial_locs))) {
         spatial_locs <- annotate_spatlocs_with_spatgrid_3D(
-            spatloc = spatial_locs, spatgrid = spatial_grid)
+            spatloc = spatial_locs, spatgrid = spatial_grid
+        )
     } else if (all(c("sdimx", "sdimy") %in% colnames(spatial_locs))) {
         spatial_locs <- annotate_spatlocs_with_spatgrid_2D(
-            spatloc = spatial_locs, spatgrid = spatial_grid)
+            spatloc = spatial_locs, spatgrid = spatial_grid
+        )
     }
 
 
@@ -3210,210 +3372,84 @@ do_spatial_grid_averaging <- function(expression_matrix,
     loc_av_expr_list <- list()
     for (loc_name in loc_names) {
         loc_cell_IDs <- spatial_locs[gr_loc == loc_name]$cell_ID
-        subset_expr <- expr_values[, colnames(expr_values) %in% loc_cell_IDs]
-        if (is.vector(subset_expr) == TRUE) {
-            loc_av_expr <- subset_expr
-        } else {
-            loc_av_expr <- rowMeans(subset_expr)
-        }
-        loc_av_expr_list[[loc_name]] <- loc_av_expr
-    }
-    loc_av_expr_matrix <- do.call("cbind", loc_av_expr_list)
-    loc_av_expr_matrix <- as.matrix(loc_av_expr_matrix)
-
-    return(loc_av_expr_matrix)
-}
-
-
-
-#' @title detectSpatialCorFeatsMatrix
-#' @name detectSpatialCorFeatsMatrix
-#' @description Detect genes that are spatially correlated
-#' @param expression_matrix provided expression matrix
-#' @param method method to use for spatial averaging
-#' @param spatial_network provided spatial network
-#' @param spatial_grid provided spatial grid
-#' @param spatial_locs provided spatial locations
-#' @param subset_feats subset of features to use
-#' @param network_smoothing smoothing factor beteen 0 and 1
-#' (has automatic default, see details)
-#' @param min_cells_per_grid minimum number of cells to consider a grid
-#' @param cor_method correlation method
-#' @returns returns a spatial correlation object: `spatCorObject`
-#' @details
-#' For `method = network`, it expects a fully connected spatial network.
-#' You can make sure to create a
-#' fully connected network by setting minimal_k > 0 in the
-#' \code{\link{createSpatialNetwork}} function.
-#'   1. **grid-averaging:** average gene expression values within a predefined
-#'   spatial grid
-#'   2. **network-averaging:** smoothens the gene expression matrix by
-#'   averaging the expression within one cell by using the neighbours within
-#'   the predefined spatial network. \eqn{b} is a smoothening factor passed by
-#'   `network_smoothing` param that defaults to \eqn{1 - 1/k}, where \eqn{k}
-#'   is the median number of k-neighbors in the selected spatial network.
-#'   Setting \eqn{b = 0} means no smoothing and \eqn{b = 1} means no
-#'   contribution from its own expression.
-#'
-#' The `spatCorObject` can be further explored with `showSpatialCorGenes()`
-#' @seealso \code{\link{showSpatialCorFeats}}
-#' @md
-#' @examples
-#' g <- GiottoData::loadGiottoMini("visium")
-#'
-#' detectSpatialCorFeatsMatrix(expression_matrix = getExpression(
-#' g, output = "matrix"), method = "network",
-#' spatial_network = getSpatialNetwork(g, output = "networkDT"))
-#' @export
-detectSpatialCorFeatsMatrix <- function(expression_matrix,
-    method = c("grid", "network"),
-    spatial_network,
-    spatial_grid,
-    spatial_locs,
-    subset_feats = NULL,
-    network_smoothing = NULL,
-    min_cells_per_grid = 4,
-    cor_method = c("pearson", "kendall", "spearman")) {
-    ## correlation method to be used
-    cor_method <- match.arg(
-        cor_method, choices = c("pearson", "kendall", "spearman"))
-
-    ## method to be used
-    method <- match.arg(method, choices = c("grid", "network"))
-
-    ## spatial averaging or smoothing
-    if (method == "grid") {
-        loc_av_expr_matrix <- do_spatial_grid_averaging(
-            expression_matrix = as.matrix(expression_matrix),
-            spatial_grid = spatial_grid,
-            spatial_locs = spatial_locs,
-            subset_feats = subset_feats,
-            min_cells_per_grid = min_cells_per_grid
-        )
-
-        # data.table variables
-        feat_ID <- variable <- NULL
-
-        cor_spat_matrix <- cor_flex(t_flex(
-            as.matrix(loc_av_expr_matrix)), method = cor_method)
-        cor_spat_matrixDT <- data.table::as.data.table(cor_spat_matrix)
-        cor_spat_matrixDT[, feat_ID := rownames(cor_spat_matrix)]
-        cor_spat_DT <- data.table::melt.data.table(
-            data = cor_spat_matrixDT,
-            id.vars = "feat_ID", value.name = "spat_cor"
-        )
-    }
-
-    if (method == "network") {
-        knn_av_expr_matrix <- do_spatial_knn_smoothing(
-            expression_matrix = as.matrix(expression_matrix),
-            spatial_network = spatial_network,
-            subset_feats = subset_feats,
-            b = network_smoothing
-        )
-
-
-
-        cor_spat_matrix <- cor_flex(t_flex(as.matrix(
-            knn_av_expr_matrix)), method = cor_method)
-        cor_spat_matrixDT <- data.table::as.data.table(cor_spat_matrix)
-        cor_spat_matrixDT[, feat_ID := rownames(cor_spat_matrix)]
-        cor_spat_DT <- data.table::melt.data.table(
-            data = cor_spat_matrixDT,
-            id.vars = "feat_ID", value.name = "spat_cor"
-        )
-    }
-
-
-
-    # data.table variables
-    cordiff <- spat_cor <- expr_cor <- spatrank <- exprrank <- rankdiff <- NULL
-
-    ## 2. perform expression correlation at single-cell level without
-    ## spatial information
-
-    # matrix
-    expr_values <- expression_matrix
-    if (!is.null(subset_feats)) {
-        expr_values <- expr_values[rownames(expr_values) %in% subset_feats, ]
-    }
-
-    cor_matrix <- cor_flex(t_flex(expr_values), method = cor_method)
-    cor_matrixDT <- data.table::as.data.table(cor_matrix)
-    cor_matrixDT[, feat_ID := rownames(cor_matrix)]
-    cor_DT <- data.table::melt.data.table(
-        data = cor_matrixDT,
-        id.vars = "feat_ID", value.name = "expr_cor"
-    )
-
-    ## 3. merge spatial and expression correlation
-    data.table::setorder(cor_spat_DT, feat_ID, variable)
-    data.table::setorder(cor_DT, feat_ID, variable)
-    doubleDT <- cbind(cor_spat_DT, expr_cor = cor_DT[["expr_cor"]])
-
-    # difference in correlation scores
-    doubleDT[, cordiff := spat_cor - expr_cor]
-
-    # difference in rank scores
-    doubleDT[, spatrank := data.table::frank(
-        -spat_cor, ties.method = "first"), by = feat_ID]
-    doubleDT[, exprrank := data.table::frank(
-        -expr_cor, ties.method = "first"), by = feat_ID]
-    doubleDT[, rankdiff := spatrank - exprrank]
-
-    # sort data
-    data.table::setorder(doubleDT, feat_ID, -spat_cor)
-
-    spatCorObject <- list(
-        cor_DT = doubleDT,
-        feat_order = rownames(cor_spat_matrix),
-        cor_hclust = list(),
-        cor_clusters = list()
-    )
-
-    class(spatCorObject) <- append(class(spatCorObject), "spatCorObject")
+        subset_expr <- expr_values[, colnames(expr_values) %in% loc_cell_IDs]
+        if (is.vector(subset_expr) == TRUE) {
+            loc_av_expr <- subset_expr
+        } else {
+            loc_av_expr <- rowMeans(subset_expr)
+        }
+        loc_av_expr_list[[loc_name]] <- loc_av_expr
+    }
+    loc_av_expr_matrix <- do.call("cbind", loc_av_expr_list)
+    loc_av_expr_matrix <- as.matrix(loc_av_expr_matrix)
 
-    return(spatCorObject)
+    return(loc_av_expr_matrix)
 }
 
 
 
-#' @title detectSpatialCorFeats
+#' @title Detect spatially correlated features
 #' @name detectSpatialCorFeats
-#' @description Detect features that are spatially correlated
+#' @description Detect features that are spatially correlated. Functions for
+#' starting from either a gobject (`detectSpatialCorFeats()`) or individual
+#' pieces of data (`detectSpatialCorFeatsMatrix()`) are provided.
 #' @param gobject giotto object
 #' @param spat_unit spatial unit
 #' @param feat_type feature type
-#' @param spat_loc_name name for spatial locations
-#' @param method method to use for spatial averaging
 #' @param expression_values gene expression values to use
-#' @param subset_feats subset of feats to use
+#' @param expression_matrix provided expression matrix
+#' @param spat_loc_name name for spatial locations
+#' @param spatial_locs provided spatial locations
 #' @param spatial_network_name name of spatial network to use
-#' @param network_smoothing  smoothing factor beteen 0 and 1
-#' (default: automatic)
+#' @param spatial_network provided spatial network
 #' @param spatial_grid_name name of spatial grid to use
+#' @param spatial_grid provided spatial grid
+#' @param method method to use for spatial averaging
+#' @param subset_feats subset of features to use
+#' @param network_smoothing smoothing factor between 0 and 1
+#' (has automatic default, see details)
 #' @param min_cells_per_grid minimum number of cells to consider a grid
 #' @param cor_method correlation method
-#' @returns returns a spatial correlation object: "spatCorObject"
+#' @returns returns a spatial correlation object: `spatCorObject`
 #' @details
-#' For method = network, it expects a fully connected spatial network. You
-#' can make sure to create a
+#' For `method = network`, it expects a fully connected spatial network.
+#' You can make sure to create a
 #' fully connected network by setting minimal_k > 0 in the
-#'  \code{\link{createSpatialNetwork}} function.
-#' \itemize{
-#'  \item{1. grid-averaging: }{average gene expression values within a predefined spatial grid}
-#'  \item{2. network-averaging: }{smoothens the gene expression matrix by averaging the expression within one cell
-#'  by using the neighbours within the predefined spatial network. b is a smoothening factor
-#'  that defaults to 1 - 1/k, where k is the median number of  k-neighbors in the
-#'  selected spatial network. Setting b = 0 means no smoothing and b = 1 means no contribution
-#'  from its own expression.}
-#' }
-#' The spatCorObject can be further explored with showSpatialCorFeats()
+#' \code{\link{createSpatialNetwork}} function.
+#'   1. **grid-averaging:** average gene expression values within a predefined
+#'   spatial grid
+#'   2. **network-averaging:** smoothens the gene expression matrix by
+#'   averaging the expression within one cell by using the neighbours within
+#'   the predefined spatial network. \eqn{b} is a smoothening factor passed by
+#'   `network_smoothing` param that defaults to \eqn{1 - 1/k}, where \eqn{k}
+#'   is the median number of k-neighbors in the selected spatial network.
+#'   Setting \eqn{b = 0} means no smoothing and \eqn{b = 1} means no
+#'   contribution from its own expression.
+#'
+#' The `spatCorObject` can be further explored with `showSpatialCorFeats()`
 #' @seealso \code{\link{showSpatialCorFeats}}
+#' @md
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #'
+#' # Perform with data in a gobject
 #' detectSpatialCorFeats(g, method = "network")
+#'
+#' # This analysis can also be performed with data outside of the gobject
+#' detectSpatialCorFeatsMatrix(
+#'     expression_matrix = getExpression(
+#'         g,
+#'         output = "matrix"
+#'     ),
+#'     method = "network",
+#'     spatial_network = getSpatialNetwork(g, output = "networkDT")
+#' )
+#'
+NULL
+
+
+
+#' @rdname detectSpatialCorFeats
 #' @export
 detectSpatialCorFeats <- function(gobject,
     spat_unit = NULL,
@@ -3440,7 +3476,9 @@ detectSpatialCorFeats <- function(gobject,
 
     ## correlation method to be used
     cor_method <- match.arg(
-        cor_method, choices = c("pearson", "kendall", "spearman"))
+        cor_method,
+        choices = c("pearson", "kendall", "spearman")
+    )
 
     ## method to be used
     method <- match.arg(method, choices = c("grid", "network"))
@@ -3448,7 +3486,8 @@ detectSpatialCorFeats <- function(gobject,
     # get expression matrix
     values <- match.arg(
         expression_values,
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_values <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -3464,7 +3503,8 @@ detectSpatialCorFeats <- function(gobject,
 
 
     # get spatial locations
-    spatial_locs <- getSpatialLocations(gobject,
+    spatial_locs <- getSpatialLocations(
+        gobject,
         spat_unit = spat_unit,
         name = spat_loc_name,
         output = "data.table",
@@ -3494,7 +3534,8 @@ detectSpatialCorFeats <- function(gobject,
         feat_ID <- variable <- NULL
 
         cor_spat_matrix <- cor_flex(t_flex(as.matrix(
-            loc_av_expr_matrix)), method = cor_method)
+            loc_av_expr_matrix
+        )), method = cor_method)
         cor_spat_matrixDT <- data.table::as.data.table(cor_spat_matrix)
         cor_spat_matrixDT[, feat_ID := rownames(cor_spat_matrix)]
         cor_spat_DT <- data.table::melt.data.table(
@@ -3523,7 +3564,8 @@ detectSpatialCorFeats <- function(gobject,
 
 
         cor_spat_matrix <- cor_flex(t_flex(as.matrix(
-            knn_av_expr_matrix)), method = cor_method)
+            knn_av_expr_matrix
+        )), method = cor_method)
         cor_spat_matrixDT <- data.table::as.data.table(cor_spat_matrix)
         cor_spat_matrixDT[, feat_ID := rownames(cor_spat_matrix)]
         cor_spat_DT <- data.table::melt.data.table(
@@ -3557,9 +3599,13 @@ detectSpatialCorFeats <- function(gobject,
 
     # difference in rank scores
     doubleDT[, spatrank := frank(
-        -spat_cor, ties.method = "first"), by = feat_ID]
+        -spat_cor,
+        ties.method = "first"
+    ), by = feat_ID]
     doubleDT[, exprrank := frank(
-        -expr_cor, ties.method = "first"), by = feat_ID]
+        -expr_cor,
+        ties.method = "first"
+    ), by = feat_ID]
     doubleDT[, rankdiff := spatrank - exprrank]
 
     # sort data
@@ -3578,82 +3624,133 @@ detectSpatialCorFeats <- function(gobject,
 }
 
 
-
-#' @title detectSpatialCorGenes
-#' @name detectSpatialCorGenes
-#' @description Detect genes that are spatially correlated
-#' @param gobject giotto object
-#' @param feat_type feature type
-#' @param spat_unit spatial unit
-#' @param method method to use for spatial averaging
-#' @param expression_values gene expression values to use
-#' @param subset_feats subset of feats to use
-#' @param subset_genes deprecated, use \code{subset_feats}
-#' @param spatial_network_name name of spatial network to use
-#' @param network_smoothing  smoothing factor beteen 0 and 1
-#' (default: automatic)
-#' @param spatial_grid_name name of spatial grid to use
-#' @param min_cells_per_grid minimum number of cells to consider a grid
-#' @param cor_method correlation method
-#' @returns returns a spatial correlation object: "spatCorObject"
-#' @details
-#' For method = network, it expects a fully connected spatial network. You
-#' can make sure to create a
-#' fully connected network by setting minimal_k > 0 in the
-#' \code{\link{createSpatialNetwork}} function.
-#' \itemize{
-#'  \item{1. grid-averaging: }{average gene expression values within a
-#'  predefined spatial grid}
-#'  \item{2. network-averaging: }{smoothens the gene expression matrix by
-#'  averaging the expression within one cell
-#'  by using the neighbours within the predefined spatial network. b is a
-#'  smoothening factor that defaults to 1 - 1/k, where k is the median
-#'  number of  k-neighbors in the selected spatial network. Setting b = 0
-#'  means no smoothing and b = 1 means no contribution
-#'  from its own expression.}
-#' }
-#' The spatCorObject can be further explored with showSpatialCorGenes()
-#' @seealso \code{\link{showSpatialCorGenes}}
+#' @rdname detectSpatialCorFeats
 #' @export
-detectSpatialCorGenes <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    method = c("grid", "network"),
-    expression_values = c("normalized", "scaled", "custom"),
-    subset_feats = NULL,
-    subset_genes = NULL,
-    spatial_network_name = "Delaunay_network",
-    network_smoothing = NULL,
-    spatial_grid_name = "spatial_grid",
-    min_cells_per_grid = 4,
-    cor_method = c("pearson", "kendall", "spearman")) {
-    ## deprecated arguments
-    if (!is.null(subset_genes)) {
-        subset_feats <- subset_genes
-        warning("subset_genes is deprecated, use subset_feats in the future")
+detectSpatialCorFeatsMatrix <- function(
+        expression_matrix,
+        method = c("grid", "network"),
+        spatial_network,
+        spatial_grid,
+        spatial_locs,
+        subset_feats = NULL,
+        network_smoothing = NULL,
+        min_cells_per_grid = 4,
+        cor_method = c("pearson", "kendall", "spearman")) {
+    ## correlation method to be used
+    cor_method <- match.arg(
+        cor_method,
+        choices = c("pearson", "kendall", "spearman")
+    )
+
+    ## method to be used
+    method <- match.arg(method, choices = c("grid", "network"))
+
+    ## spatial averaging or smoothing
+    if (method == "grid") {
+        loc_av_expr_matrix <- do_spatial_grid_averaging(
+            expression_matrix = as.matrix(expression_matrix),
+            spatial_grid = spatial_grid,
+            spatial_locs = spatial_locs,
+            subset_feats = subset_feats,
+            min_cells_per_grid = min_cells_per_grid
+        )
+
+        # data.table variables
+        feat_ID <- variable <- NULL
+
+        cor_spat_matrix <- cor_flex(t_flex(
+            as.matrix(loc_av_expr_matrix)
+        ), method = cor_method)
+        cor_spat_matrixDT <- data.table::as.data.table(cor_spat_matrix)
+        cor_spat_matrixDT[, feat_ID := rownames(cor_spat_matrix)]
+        cor_spat_DT <- data.table::melt.data.table(
+            data = cor_spat_matrixDT,
+            id.vars = "feat_ID", value.name = "spat_cor"
+        )
     }
 
-    warning("Deprecated and replaced by detectSpatialCorFeats")
+    if (method == "network") {
+        knn_av_expr_matrix <- do_spatial_knn_smoothing(
+            expression_matrix = as.matrix(expression_matrix),
+            spatial_network = spatial_network,
+            subset_feats = subset_feats,
+            b = network_smoothing
+        )
+
 
-    detectSpatialCorFeats(
-        gobject = gobject,
-        feat_type = feat_type,
-        spat_unit = spat_unit,
-        method = method,
-        expression_values = expression_values,
-        subset_feats = subset_feats,
-        spatial_network_name = spatial_network_name,
-        network_smoothing = network_smoothing,
-        spatial_grid_name = spatial_grid_name,
-        min_cells_per_grid = min_cells_per_grid,
-        cor_method = cor_method
+
+        cor_spat_matrix <- cor_flex(t_flex(as.matrix(
+            knn_av_expr_matrix
+        )), method = cor_method)
+        cor_spat_matrixDT <- data.table::as.data.table(cor_spat_matrix)
+        cor_spat_matrixDT[, feat_ID := rownames(cor_spat_matrix)]
+        cor_spat_DT <- data.table::melt.data.table(
+            data = cor_spat_matrixDT,
+            id.vars = "feat_ID", value.name = "spat_cor"
+        )
+    }
+
+
+
+    # data.table variables
+    cordiff <- spat_cor <- expr_cor <- spatrank <- exprrank <- rankdiff <- NULL
+
+    ## 2. perform expression correlation at single-cell level without
+    ## spatial information
+
+    # matrix
+    expr_values <- expression_matrix
+    if (!is.null(subset_feats)) {
+        expr_values <- expr_values[rownames(expr_values) %in% subset_feats, ]
+    }
+
+    cor_matrix <- cor_flex(t_flex(expr_values), method = cor_method)
+    cor_matrixDT <- data.table::as.data.table(cor_matrix)
+    cor_matrixDT[, feat_ID := rownames(cor_matrix)]
+    cor_DT <- data.table::melt.data.table(
+        data = cor_matrixDT,
+        id.vars = "feat_ID", value.name = "expr_cor"
+    )
+
+    ## 3. merge spatial and expression correlation
+    data.table::setorder(cor_spat_DT, feat_ID, variable)
+    data.table::setorder(cor_DT, feat_ID, variable)
+    doubleDT <- cbind(cor_spat_DT, expr_cor = cor_DT[["expr_cor"]])
+
+    # difference in correlation scores
+    doubleDT[, cordiff := spat_cor - expr_cor]
+
+    # difference in rank scores
+    doubleDT[, spatrank := data.table::frank(
+        -spat_cor,
+        ties.method = "first"
+    ), by = feat_ID]
+    doubleDT[, exprrank := data.table::frank(
+        -expr_cor,
+        ties.method = "first"
+    ), by = feat_ID]
+    doubleDT[, rankdiff := spatrank - exprrank]
+
+    # sort data
+    data.table::setorder(doubleDT, feat_ID, -spat_cor)
+
+    spatCorObject <- list(
+        cor_DT = doubleDT,
+        feat_order = rownames(cor_spat_matrix),
+        cor_hclust = list(),
+        cor_clusters = list()
     )
+
+    class(spatCorObject) <- append(class(spatCorObject), "spatCorObject")
+
+    return(spatCorObject)
 }
 
 
 
 
 
+
 #' @title showSpatialCorFeats
 #' @name showSpatialCorFeats
 #' @description Shows and filters spatially correlated features
@@ -3670,15 +3767,16 @@ detectSpatialCorGenes <- function(gobject,
 #' @param show_top_feats show top features per gene
 #' @returns data.table with filtered information
 #' @export
-showSpatialCorFeats <- function(spatCorObject,
-    use_clus_name = NULL,
-    selected_clusters = NULL,
-    feats = NULL,
-    min_spat_cor = 0.5,
-    min_expr_cor = NULL,
-    min_cor_diff = NULL,
-    min_rank_diff = NULL,
-    show_top_feats = NULL) {
+showSpatialCorFeats <- function(
+        spatCorObject,
+        use_clus_name = NULL,
+        selected_clusters = NULL,
+        feats = NULL,
+        min_spat_cor = 0.5,
+        min_expr_cor = NULL,
+        min_cor_diff = NULL,
+        min_rank_diff = NULL,
+        show_top_feats = NULL) {
     # data.table variables
     clus <- feat_ID <- spat_cor <- cor_diff <- rankdiff <- NULL
 
@@ -3696,9 +3794,12 @@ showSpatialCorFeats <- function(spatCorObject,
         clusters <- clusters_part
         names_clusters <- names(clusters_part)
         clusters_DT <- data.table::data.table(
-            "feat_ID" = names_clusters, "clus" = clusters)
+            "feat_ID" = names_clusters, "clus" = clusters
+        )
         filter_DT <- data.table::merge.data.table(
-            filter_DT, clusters_DT, by = "feat_ID")
+            filter_DT, clusters_DT,
+            by = "feat_ID"
+        )
     }
 
     ## 0. subset clusters
@@ -3758,15 +3859,16 @@ showSpatialCorFeats <- function(spatCorObject,
 #' @param show_top_genes show top genes per gene
 #' @returns data.table with filtered information
 #' @export
-showSpatialCorGenes <- function(spatCorObject,
-    use_clus_name = NULL,
-    selected_clusters = NULL,
-    genes = NULL,
-    min_spat_cor = 0.5,
-    min_expr_cor = NULL,
-    min_cor_diff = NULL,
-    min_rank_diff = NULL,
-    show_top_genes = NULL) {
+showSpatialCorGenes <- function(
+        spatCorObject,
+        use_clus_name = NULL,
+        selected_clusters = NULL,
+        genes = NULL,
+        min_spat_cor = 0.5,
+        min_expr_cor = NULL,
+        min_cor_diff = NULL,
+        min_rank_diff = NULL,
+        show_top_genes = NULL) {
     warning("Deprecated and replaced by showSpatialCorFeats")
 
     showSpatialCorFeats(
@@ -3801,13 +3903,16 @@ showSpatialCorGenes <- function(spatCorObject,
 #' g <- GiottoData::loadGiottoMini("visium")
 #'
 #' clusterSpatialCorFeats(spatCorObject = detectSpatialCorFeats(
-#' g, method = "network"))
+#'     g,
+#'     method = "network"
+#' ))
 #' @export
-clusterSpatialCorFeats <- function(spatCorObject,
-    name = "spat_clus",
-    hclust_method = "ward.D",
-    k = 10,
-    return_obj = TRUE) {
+clusterSpatialCorFeats <- function(
+        spatCorObject,
+        name = "spat_clus",
+        hclust_method = "ward.D",
+        k = 10,
+        return_obj = TRUE) {
     # check input
     if (!"spatCorObject" %in% class(spatCorObject)) {
         stop("spatCorObject needs to be the output from
@@ -3817,7 +3922,9 @@ clusterSpatialCorFeats <- function(spatCorObject,
     # create correlation matrix
     cor_DT <- spatCorObject[["cor_DT"]]
     cor_DT_dc <- data.table::dcast.data.table(
-        cor_DT, formula = feat_ID ~ variable, value.var = "spat_cor")
+        cor_DT,
+        formula = feat_ID ~ variable, value.var = "spat_cor"
+    )
     cor_matrix <- dt_to_matrix(cor_DT_dc)
 
     # re-ordering matrix
@@ -3853,11 +3960,12 @@ clusterSpatialCorFeats <- function(spatCorObject,
 #' @param return_obj return spatial correlation object (spatCorObject)
 #' @returns spatCorObject or cluster results
 #' @export
-clusterSpatialCorGenes <- function(spatCorObject,
-    name = "spat_clus",
-    hclust_method = "ward.D",
-    k = 10,
-    return_obj = TRUE) {
+clusterSpatialCorGenes <- function(
+        spatCorObject,
+        name = "spat_clus",
+        hclust_method = "ward.D",
+        k = 10,
+        return_obj = TRUE) {
     warning("Deprecated and replaced by clusterSpatialCorFeats")
 
     clusterSpatialCorFeats(
@@ -3896,20 +4004,21 @@ clusterSpatialCorGenes <- function(spatCorObject,
 #' \code{\link[ComplexHeatmap]{Heatmap}} function from ComplexHeatmap
 #' @returns Heatmap generated by ComplexHeatmap
 #' @export
-heatmSpatialCorFeats <- function(gobject,
-    spatCorObject,
-    use_clus_name = NULL,
-    show_cluster_annot = TRUE,
-    show_row_dend = TRUE,
-    show_column_dend = FALSE,
-    show_row_names = FALSE,
-    show_column_names = FALSE,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "heatmSpatialCorFeats",
-    ...) {
+heatmSpatialCorFeats <- function(
+        gobject,
+        spatCorObject,
+        use_clus_name = NULL,
+        show_cluster_annot = TRUE,
+        show_row_dend = TRUE,
+        show_column_dend = FALSE,
+        show_row_names = FALSE,
+        show_column_names = FALSE,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "heatmSpatialCorFeats",
+        ...) {
     ## check input
     if (!"spatCorObject" %in% class(spatCorObject)) {
         stop("spatCorObject needs to be the output from
@@ -3922,7 +4031,9 @@ heatmSpatialCorFeats <- function(gobject,
     ## create correlation matrix
     cor_DT <- spatCorObject[["cor_DT"]]
     cor_DT_dc <- data.table::dcast.data.table(
-        cor_DT, formula = feat_ID ~ variable, value.var = "spat_cor")
+        cor_DT,
+        formula = feat_ID ~ variable, value.var = "spat_cor"
+    )
     cor_matrix <- dt_to_matrix(cor_DT_dc)
 
     # re-ordering matrix
@@ -4030,18 +4141,21 @@ heatmSpatialCorGenes <- function(...) {
 #' spatCorObject <- detectSpatialCorFeats(g, method = "network")
 #' clusters <- clusterSpatialCorFeats(spatCorObject = spatCorObject)
 #'
-#' rankSpatialCorGroups(gobject = g, spatCorObject = clusters,
-#' use_clus_name = "spat_clus")
+#' rankSpatialCorGroups(
+#'     gobject = g, spatCorObject = clusters,
+#'     use_clus_name = "spat_clus"
+#' )
 #' @md
 #' @export
-rankSpatialCorGroups <- function(gobject,
-    spatCorObject,
-    use_clus_name = NULL,
-    show_plot = NULL,
-    return_plot = FALSE,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "rankSpatialCorGroups") {
+rankSpatialCorGroups <- function(
+        gobject,
+        spatCorObject,
+        use_clus_name = NULL,
+        show_plot = NULL,
+        return_plot = FALSE,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "rankSpatialCorGroups") {
     ## check input
     if (!"spatCorObject" %in% class(spatCorObject)) {
         stop("spatCorObject needs to be the output from
@@ -4061,7 +4175,9 @@ rankSpatialCorGroups <- function(gobject,
     ## create correlation matrix
     cor_DT <- spatCorObject[["cor_DT"]]
     cor_DT_dc <- data.table::dcast.data.table(
-        cor_DT, formula = feat_ID ~ variable, value.var = "spat_cor")
+        cor_DT,
+        formula = feat_ID ~ variable, value.var = "spat_cor"
+    )
     cor_matrix <- dt_to_matrix(cor_DT_dc)
 
     # re-ordering matrix
@@ -4081,13 +4197,15 @@ rankSpatialCorGroups <- function(gobject,
 
         sub_cor_matrix <- cor_matrix[
             rownames(cor_matrix) %in% selected_feats,
-            colnames(cor_matrix) %in% selected_feats]
+            colnames(cor_matrix) %in% selected_feats
+        ]
         mean_score <- mean_flex(sub_cor_matrix)
         res_cor_list[[id]] <- mean_score
 
         sub_neg_cor_matrix <- cor_matrix[
             rownames(cor_matrix) %in% selected_feats,
-            !colnames(cor_matrix) %in% selected_feats]
+            !colnames(cor_matrix) %in% selected_feats
+        ]
         mean_neg_score <- mean_flex(sub_neg_cor_matrix)
         res_neg_cor_list[[id]] <- mean_neg_score
     }
@@ -4134,7 +4252,7 @@ rankSpatialCorGroups <- function(gobject,
 
 
 
-#' @title getBalancedSpatCoexpressionFeats
+#' @title Get balanced spatial coexpression features
 #' @name getBalancedSpatCoexpressionFeats
 #' @description Extract features from spatial co-expression modules in a
 #' balanced manner
@@ -4148,18 +4266,21 @@ rankSpatialCorGroups <- function(gobject,
 #' @returns balanced vector with features for each co-expression module
 #' @details There are 3 different ways of selecting features from the spatial
 #' co-expression modules
-#' \itemize{
-#'   \item{1. weighted: }{Features are ranked based on summarized pairwise co-expression scores}
-#'   \item{2. random: }{A random selection of features, set seed for reproducibility}
-#'   \item{3. informed: }{Features are selected based on prior information/ranking}
-#' }
+#'   1. **weighted:** Features are ranked based on summarized pairwise
+#'   co-expression scores
+#'   2. **random:** A random selection of features, set seed for
+#'   reproducibility
+#'   3. **informed:** Features are selected based on prior information/ranking
+#'
+#' @md
 #' @export
-getBalancedSpatCoexpressionFeats <- function(spatCorObject,
-    maximum = 50,
-    rank = c("weighted", "random", "informed"),
-    informed_ranking = NULL,
-    seed = NA,
-    verbose = TRUE) {
+getBalancedSpatCoexpressionFeats <- function(
+        spatCorObject,
+        maximum = 50,
+        rank = c("weighted", "random", "informed"),
+        informed_ranking = NULL,
+        seed = NA,
+        verbose = TRUE) {
     # data.table vars
     feat_ID <- variable <- combo <- spat_cor <- rnk <- feat_id <- V1 <- NULL
 
@@ -4188,7 +4309,8 @@ getBalancedSpatCoexpressionFeats <- function(spatCorObject,
                 wrap_msg(
                     "There are only ", feat_length, " features for cluster ",
                     clus, "\n",
-                    "Maximum will be set to ", feat_length)
+                    "Maximum will be set to ", feat_length
+                )
             } else {
                 maximum_to_use <- maximum
             }
@@ -4219,7 +4341,8 @@ getBalancedSpatCoexpressionFeats <- function(spatCorObject,
             selected_cluster_features <- names(clusters[clusters == clus])
             subset_cor_data <- cor_data[
                 feat_ID %in% selected_cluster_features &
-                    variable %in% selected_cluster_features]
+                    variable %in% selected_cluster_features
+            ]
             subset_cor_data <- subset_cor_data[feat_ID != variable]
             subset_cor_data <- dt_sort_combine_two_columns(
                 DT = subset_cor_data,
@@ -4232,10 +4355,12 @@ getBalancedSpatCoexpressionFeats <- function(spatCorObject,
             # create a ranked data.table
             rnk1DT <- data.table::data.table(
                 feat_id = subset_cor_data$feat_ID,
-                rnk = seq_along(subset_cor_data$feat_ID))
+                rnk = seq_along(subset_cor_data$feat_ID)
+            )
             rnk2DT <- data.table::data.table(
                 feat_id = subset_cor_data$variable,
-                rnk = seq_along(subset_cor_data$variable))
+                rnk = seq_along(subset_cor_data$variable)
+            )
             rnkDT <- data.table::rbindlist(list(rnk1DT, rnk2DT))
             data.table::setorder(rnkDT, rnk)
 
@@ -4249,12 +4374,13 @@ getBalancedSpatCoexpressionFeats <- function(spatCorObject,
                 wrap_msg(
                     "There are only ", feat_length, " features for cluster ",
                     clus, "\n",
-                    "Maximum will be set to ", feat_length)
+                    "Maximum will be set to ", feat_length
+                )
             } else {
                 maximum_to_use <- maximum
             }
 
-            selected_feats <- rnkcombined[1:maximum_to_use][["feat_id"]]
+            selected_feats <- rnkcombined[seq_len(maximum_to_use)][["feat_id"]]
 
             clus_id <- rep(clus, length(selected_feats))
             names(clus_id) <- selected_feats
@@ -4287,7 +4413,8 @@ getBalancedSpatCoexpressionFeats <- function(spatCorObject,
                 wrap_msg(
                     "There are only ", feat_length, " features for cluster ",
                     clus, "\n",
-                    "Maximum will be set to ", feat_length)
+                    "Maximum will be set to ", feat_length
+                )
             } else {
                 maximum_to_use <- maximum
             }
@@ -4295,7 +4422,8 @@ getBalancedSpatCoexpressionFeats <- function(spatCorObject,
 
             informed_subset <- informed_ranking_numerical[
                 names(informed_ranking_numerical) %in%
-                    selected_cluster_features]
+                    selected_cluster_features
+            ]
             informed_subset <- sort(informed_subset)
 
             feat_length <- length(informed_subset)
@@ -4304,12 +4432,13 @@ getBalancedSpatCoexpressionFeats <- function(spatCorObject,
                 wrap_msg(
                     "There are only ", feat_length, " features for cluster ",
                     clus, "\n",
-                    "Maximum will be set to ", feat_length)
+                    "Maximum will be set to ", feat_length
+                )
             } else {
                 maximum_to_use <- maximum
             }
 
-            selected_feats <- names(informed_subset[1:maximum_to_use])
+            selected_feats <- names(informed_subset[seq_len(maximum_to_use)])
 
             clus_id <- rep(clus, length(selected_feats))
             names(clus_id) <- selected_feats
@@ -4345,25 +4474,30 @@ getBalancedSpatCoexpressionFeats <- function(spatCorObject,
 #' @param show_pattern show the discrete spatial pattern
 #' @param pattern_colors 2 color vector for the spatial pattern
 #' @param normalization_params additional parameters for (re-)normalizing
-#' @returns Reprocessed Giotto object for which one gene has a forced 
+#' @returns Reprocessed Giotto object for which one gene has a forced
 #' spatial pattern
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' simulateOneGenePatternGiottoObject(gobject = g, 
-#' pattern_cell_ids = c("AAAGGGATGTAGCAAG-1", "TCAAACAACCGCGTCG-1",
-#' "ACGATCATACATAGAG-1", "TATGCTCCCTACTTAC-1"),
-#' gene_name = "Gna12")
+#'
+#' simulateOneGenePatternGiottoObject(
+#'     gobject = g,
+#'     pattern_cell_ids = c(
+#'         "AAAGGGATGTAGCAAG-1", "TCAAACAACCGCGTCG-1",
+#'         "ACGATCATACATAGAG-1", "TATGCTCCCTACTTAC-1"
+#'     ),
+#'     gene_name = "Gna12"
+#' )
 #' @export
-simulateOneGenePatternGiottoObject <- function(gobject,
-    pattern_name = "pattern",
-    pattern_cell_ids = NULL,
-    gene_name = NULL,
-    spatial_prob = 0.95,
-    gradient_direction = NULL,
-    show_pattern = TRUE,
-    pattern_colors = c("in" = "green", "out" = "red"),
-    normalization_params = list()) {
+simulateOneGenePatternGiottoObject <- function(
+        gobject,
+        pattern_name = "pattern",
+        pattern_cell_ids = NULL,
+        gene_name = NULL,
+        spatial_prob = 0.95,
+        gradient_direction = NULL,
+        show_pattern = TRUE,
+        pattern_colors = c("in" = "green", "out" = "red"),
+        normalization_params = list()) {
     # data.table variables
     cell_ID <- sdimx_y <- sdimx <- sdimy <- NULL
 
@@ -4374,7 +4508,8 @@ simulateOneGenePatternGiottoObject <- function(gobject,
     ## create and add annotation for pattern
     cell_meta <- pDataDT(gobject)
     cell_meta[, (pattern_name) := ifelse(
-        cell_ID %in% pattern_cell_ids, "in", "out")]
+        cell_ID %in% pattern_cell_ids, "in", "out"
+    )]
 
     newgobject <- addCellMetadata(
         gobject,
@@ -4403,24 +4538,30 @@ simulateOneGenePatternGiottoObject <- function(gobject,
         copy_obj = TRUE
     )
     cell_meta <- data.table::merge.data.table(
-        cell_meta, cell_coord, by = "cell_ID")
+        cell_meta, cell_coord,
+        by = "cell_ID"
+    )
 
     ## get number of cells within pattern
     cell_number <- nrow(cell_meta[get(pattern_name) == "in"])
 
 
     ## normalized expression
-    #expr_data <- newgobject@norm_expr
-    expr_data <- getExpression(gobject = newgobject, 
-                                values = "normalized",
-                                output = "matrix")
+    # expr_data <- newgobject@norm_expr
+    expr_data <- getExpression(
+        gobject = newgobject,
+        values = "normalized",
+        output = "matrix"
+    )
     result_list <- list()
 
     ## raw expression
-    #raw_expr_data <- newgobject@raw_exprs
-    raw_expr_data <- getExpression(gobject = newgobject, 
-                                   values = "raw",
-                                   output = "matrix")
+    # raw_expr_data <- newgobject@raw_exprs
+    raw_expr_data <- getExpression(
+        gobject = newgobject,
+        values = "raw",
+        output = "matrix"
+    )
     raw_result_list <- list()
 
 
@@ -4448,15 +4589,19 @@ simulateOneGenePatternGiottoObject <- function(gobject,
     outside_prob <- 1 - spatial_prob
     prob_vector <- c(
         rep(spatial_prob, cell_number),
-        rep(outside_prob, remaining_cell_number))
+        rep(outside_prob, remaining_cell_number)
+    )
 
     # first get the 'in' pattern sample values randomly
     sample_values <- sample(
-        sort_expr_gene, replace = FALSE, size = cell_number, prob = prob_vector)
+        sort_expr_gene,
+        replace = FALSE, size = cell_number, prob = prob_vector
+    )
 
     # then take the remaining 'out' pattern values randomly
     remain_values <- sort_expr_gene[
-        !names(sort_expr_gene) %in% names(sample_values)]
+        !names(sort_expr_gene) %in% names(sample_values)
+    ]
     remain_values <- sample(remain_values, size = length(remain_values))
 
 
@@ -4506,19 +4651,23 @@ simulateOneGenePatternGiottoObject <- function(gobject,
 
     # change the original matrices
     raw_expr_data[rownames(raw_expr_data) == gene_name, ] <- new_sim_raw_values
-    #newgobject@raw_exprs <- raw_expr_data
-    newgobject <- setExpression(gobject = newgobject,
-                                x = createExprObj(
-                                    expression_data = raw_expr_data,
-                                    name = "raw"),
-                                name = "raw",
-                                provenance = prov(getCellMetadata(newgobject)))
+    # newgobject@raw_exprs <- raw_expr_data
+    newgobject <- setExpression(
+        gobject = newgobject,
+        x = createExprObj(
+            expression_data = raw_expr_data,
+            name = "raw"
+        ),
+        name = "raw",
+        provenance = prov(getCellMetadata(newgobject))
+    )
 
     # recalculate normalized values
     newgobject <- do.call(
-        "normalizeGiotto", 
-        args = c(gobject = newgobject, normalization_params))
-    
+        "normalizeGiotto",
+        args = c(gobject = newgobject, normalization_params)
+    )
+
     newgobject <- addStatistics(gobject = newgobject)
 
     return(newgobject)
@@ -4534,28 +4683,33 @@ simulateOneGenePatternGiottoObject <- function(gobject,
 #' @description runs all spatial tests for 1 probability and 1 rep
 #' @returns data.table
 #' @keywords internal
-run_spatial_sim_tests_one_rep <- function(gobject,
-    pattern_name = "pattern",
-    pattern_cell_ids = NULL,
-    gene_name = NULL,
-    spatial_prob = 0.95,
-    show_pattern = FALSE,
-    spatial_network_name = "kNN_network",
-    spat_methods = c("binSpect_single", "binSpect_multi", "spatialDE",
-                    "spark", "silhouetteRank"),
-    spat_methods_params = list(NA, NA, NA, NA, NA),
-    spat_methods_names = c("binSpect_single", "binSpect_multi", "spatialDE",
-                            "spark", "silhouetteRank"),
-    save_plot = FALSE,
-    save_raw = FALSE,
-    save_norm = FALSE,
-    save_dir = "~",
-    save_name = "plot",
-    run_simulations = TRUE,
-    ...) {
+run_spatial_sim_tests_one_rep <- function(
+        gobject,
+        pattern_name = "pattern",
+        pattern_cell_ids = NULL,
+        gene_name = NULL,
+        spatial_prob = 0.95,
+        show_pattern = FALSE,
+        spatial_network_name = "kNN_network",
+        spat_methods = c(
+            "binSpect_single", "binSpect_multi", "spatialDE",
+            "spark", "silhouetteRank"
+        ),
+        spat_methods_params = list(NA, NA, NA, NA, NA),
+        spat_methods_names = c(
+            "binSpect_single", "binSpect_multi", "spatialDE",
+            "spark", "silhouetteRank"
+        ),
+        save_plot = FALSE,
+        save_raw = FALSE,
+        save_norm = FALSE,
+        save_dir = "~",
+        save_name = "plot",
+        run_simulations = TRUE,
+        ...) {
     # data.table variables
     genes <- prob <- time <- adj.p.value <- method <- p.val <- sd <-
-        qval <- pval <- g <- adjusted_pvalue <- NULL
+        qval <- pval <- g <- adjusted_pvalue <- feats <- NULL
 
     ## test if spat_methods, params and names have the same length
     if (length(spat_methods) != length(spat_methods_params)) {
@@ -4582,7 +4736,7 @@ run_spatial_sim_tests_one_rep <- function(gobject,
     # save plot
     if (save_plot == TRUE) {
         spatFeatPlot2D(simulate_patch,
-            expression_values = "normalized", 
+            expression_values = "normalized",
             feats = gene_name,
             point_shape = "border",
             point_border_stroke = 0.1,
@@ -4604,9 +4758,11 @@ run_spatial_sim_tests_one_rep <- function(gobject,
 
         write.table(
             x = as.matrix(getExpression(
-                gobject = simulate_patch, values = "raw", output = "matrix")),
+                gobject = simulate_patch, values = "raw", output = "matrix"
+            )),
             file = paste0(
-                save_dir, "/", pattern_name, "/", save_name, "_raw_data.txt"),
+                save_dir, "/", pattern_name, "/", save_name, "_raw_data.txt"
+            ),
             sep = "\t"
         )
     }
@@ -4618,11 +4774,13 @@ run_spatial_sim_tests_one_rep <- function(gobject,
 
         write.table(
             x = as.matrix(getExpression(
-                gobject = simulate_patch, 
-                values = "normalized", 
-                output = "matrix")),
+                gobject = simulate_patch,
+                values = "normalized",
+                output = "matrix"
+            )),
             file = paste0(
-                save_dir, "/", pattern_name, "/", save_name, "_norm_data.txt"),
+                save_dir, "/", pattern_name, "/", save_name, "_norm_data.txt"
+            ),
             sep = "\t"
         )
     }
@@ -4636,8 +4794,10 @@ run_spatial_sim_tests_one_rep <- function(gobject,
             # method
             selected_method <- spat_methods[test]
             if (!selected_method %in%
-                c("binSpect_single", "binSpect_multi", "spatialDE", "spark",
-                "silhouetteRank")) {
+                c(
+                    "binSpect_single", "binSpect_multi", "spatialDE", "spark",
+                    "silhouetteRank"
+                )) {
                 stop(selected_method, " is not a know spatial method")
             }
 
@@ -4718,9 +4878,11 @@ run_spatial_sim_tests_one_rep <- function(gobject,
                 spatial_gene_results[, time := total_time[["elapsed"]]]
 
                 spatial_gene_results <- spatial_gene_results[
-                    , .(feats, adj.p.value, prob, time)]
+                    , .(feats, adj.p.value, prob, time)
+                ]
                 colnames(spatial_gene_results) <- c(
-                    "feats", "adj.p.value", "prob", "time")
+                    "feats", "adj.p.value", "prob", "time"
+                )
 
                 spatial_gene_results[, method := selected_name]
             } else if (selected_method == "binSpect_multi") {
@@ -4738,20 +4900,25 @@ run_spatial_sim_tests_one_rep <- function(gobject,
                 spatial_gene_results[, time := total_time[["elapsed"]]]
 
                 spatial_gene_results <- spatial_gene_results[
-                    , .(feats, p.val, prob, time)]
+                    , .(feats, p.val, prob, time)
+                ]
                 colnames(spatial_gene_results) <- c(
-                    "feats", "adj.p.value", "prob", "time")
+                    "feats", "adj.p.value", "prob", "time"
+                )
 
                 spatial_gene_results[, method := selected_name]
             } else if (selected_method == "spatialDE") {
                 start <- proc.time()
                 new_raw_sim_matrix <- getExpression(simulate_patch,
-                                                    values = "raw",
-                                                    output = "matrix")
+                    values = "raw",
+                    output = "matrix"
+                )
                 sd_cells <- apply(new_raw_sim_matrix, 2, sd)
                 sd_non_zero_cells <- names(sd_cells[sd_cells != 0])
                 simulate_patch_fix <- subsetGiotto(
-                    simulate_patch, cell_ids = sd_non_zero_cells)
+                    simulate_patch,
+                    cell_ids = sd_non_zero_cells
+                )
 
                 spatial_gene_results <- do.call("spatialDE", c(
                     gobject = simulate_patch_fix,
@@ -4759,14 +4926,17 @@ run_spatial_sim_tests_one_rep <- function(gobject,
                 ))
 
                 spatialDE_spatialgenes_sim_res <- spatial_gene_results$results$results
-                if (is.null(spatialDE_spatialgenes_sim_res))
+                if (is.null(spatialDE_spatialgenes_sim_res)) {
                     spatialDE_spatialgenes_sim_res <- spatial_gene_results$results
+                }
 
                 spatialDE_spatialgenes_sim_res <- data.table::as.data.table(
-                    spatialDE_spatialgenes_sim_res)
+                    spatialDE_spatialgenes_sim_res
+                )
                 data.table::setorder(spatialDE_spatialgenes_sim_res, qval, pval)
                 spatialDE_result <- spatialDE_spatialgenes_sim_res[
-                    g == gene_name]
+                    g == gene_name
+                ]
 
                 spatialDE_time <- proc.time() - start
 
@@ -4774,9 +4944,11 @@ run_spatial_sim_tests_one_rep <- function(gobject,
                 spatialDE_result[, time := spatialDE_time[["elapsed"]]]
 
                 spatial_gene_results <- spatialDE_result[
-                    , .(g, qval, prob, time)]
+                    , .(g, qval, prob, time)
+                ]
                 colnames(spatial_gene_results) <- c(
-                    "feats", "adj.p.value", "prob", "time")
+                    "feats", "adj.p.value", "prob", "time"
+                )
                 spatial_gene_results[, method := "spatialDE"]
             } else if (selected_method == "spark") {
                 ## spark
@@ -4793,9 +4965,11 @@ run_spatial_sim_tests_one_rep <- function(gobject,
                 spark_result[, time := spark_time[["elapsed"]]]
 
                 spatial_gene_results <- spark_result[
-                    , .(genes, adjusted_pvalue, prob, time)]
+                    , .(genes, adjusted_pvalue, prob, time)
+                ]
                 colnames(spatial_gene_results) <- c(
-                    "genes", "adj.p.value", "prob", "time")
+                    "genes", "adj.p.value", "prob", "time"
+                )
                 spatial_gene_results[, method := "spark"]
             } else if (selected_method == "silhouetteRank") {
                 ## silhouetterank
@@ -4807,7 +4981,9 @@ run_spatial_sim_tests_one_rep <- function(gobject,
                 ))
 
                 data.table::setnames(
-                    spatial_gene_results, old = "gene", new = "genes")
+                    spatial_gene_results,
+                    old = "gene", new = "genes"
+                )
                 spatial_gene_results <- spatial_gene_results[genes == gene_name]
                 silh_time <- proc.time() - start
 
@@ -4816,9 +4992,11 @@ run_spatial_sim_tests_one_rep <- function(gobject,
 
                 # silhrank uses qval by default
                 spatial_gene_results <- spatial_gene_results[
-                    , .(genes, qval, prob, time)]
+                    , .(genes, qval, prob, time)
+                ]
                 colnames(spatial_gene_results) <- c(
-                    "genes", "adj.p.value", "prob", "time")
+                    "genes", "adj.p.value", "prob", "time"
+                )
                 spatial_gene_results[, method := "silhouette"]
             }
 
@@ -4842,25 +5020,30 @@ run_spatial_sim_tests_one_rep <- function(gobject,
 #' repetitions
 #' @returns data.table
 #' @keywords internal
-run_spatial_sim_tests_multi <- function(gobject,
-    pattern_name = "pattern",
-    pattern_cell_ids = NULL,
-    gene_name = NULL,
-    spatial_probs = c(0.5, 1),
-    reps = 2,
-    spatial_network_name = "kNN_network",
-    spat_methods = c("binSpect_single", "binSpect_multi", "spatialDE",
-                    "spark", "silhouetteRank"),
-    spat_methods_params = list(NA, NA, NA, NA, NA),
-    spat_methods_names = c("binSpect_single", "binSpect_multi", "spatialDE",
-                            "spark", "silhouetteRank"),
-    save_plot = FALSE,
-    save_raw = FALSE,
-    save_norm = FALSE,
-    save_dir = "~",
-    verbose = TRUE,
-    run_simulations = TRUE,
-    ...) {
+run_spatial_sim_tests_multi <- function(
+        gobject,
+        pattern_name = "pattern",
+        pattern_cell_ids = NULL,
+        gene_name = NULL,
+        spatial_probs = c(0.5, 1),
+        reps = 2,
+        spatial_network_name = "kNN_network",
+        spat_methods = c(
+            "binSpect_single", "binSpect_multi", "spatialDE",
+            "spark", "silhouetteRank"
+        ),
+        spat_methods_params = list(NA, NA, NA, NA, NA),
+        spat_methods_names = c(
+            "binSpect_single", "binSpect_multi", "spatialDE",
+            "spark", "silhouetteRank"
+        ),
+        save_plot = FALSE,
+        save_raw = FALSE,
+        save_norm = FALSE,
+        save_dir = "~",
+        verbose = TRUE,
+        run_simulations = TRUE,
+        ...) {
     prob_list <- list()
     for (prob_ind in seq_along(spatial_probs)) {
         prob_i <- spatial_probs[prob_ind]
@@ -4868,12 +5051,14 @@ run_spatial_sim_tests_multi <- function(gobject,
         if (verbose) message("start with ", prob_i)
 
         rep_list <- list()
-        for (rep_i in 1:reps) {
+        for (rep_i in seq_len(reps)) {
             if (verbose) message("repetition = ", rep_i)
 
 
-            plot_name <- paste0("plot_", gene_name, "_prob",
-                                prob_i, "_rep", rep_i)
+            plot_name <- paste0(
+                "plot_", gene_name, "_prob",
+                prob_i, "_rep", rep_i
+            )
 
 
             rep_res <- run_spatial_sim_tests_one_rep(gobject,
@@ -4946,34 +5131,43 @@ run_spatial_sim_tests_multi <- function(gobject,
 #' @returns data.table with results
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' runPatternSimulation(gobject = g, pattern_cell_ids = c("AAAGGGATGTAGCAAG-1",
-#' "TCAAACAACCGCGTCG-1", "ACGATCATACATAGAG-1", "TATGCTCCCTACTTAC-1"),
-#' spatial_network_name = "spatial_network", gene_names = c("Gna12", "Ccnd2"))
+#'
+#' runPatternSimulation(
+#'     gobject = g, pattern_cell_ids = c(
+#'         "AAAGGGATGTAGCAAG-1",
+#'         "TCAAACAACCGCGTCG-1", "ACGATCATACATAGAG-1", "TATGCTCCCTACTTAC-1"
+#'     ),
+#'     spatial_network_name = "spatial_network", gene_names = c("Gna12", "Ccnd2")
+#' )
 #' @export
-runPatternSimulation <- function(gobject,
-    pattern_name = "pattern",
-    pattern_colors = c("in" = "green", "out" = "red"),
-    pattern_cell_ids = NULL,
-    gene_names = NULL,
-    spatial_probs = c(0.5, 1),
-    reps = 2,
-    spatial_network_name = "kNN_network",
-    spat_methods = c("binSpect_single", "binSpect_multi", "spatialDE",
-                    "spark", "silhouetteRank"),
-    spat_methods_params = list(NA, NA, NA, NA, NA),
-    spat_methods_names = c("binSpect_single", "binSpect_multi", "spatialDE",
-                            "spark", "silhouetteRank"),
-    scalefactor = 6000,
-    save_plot = TRUE,
-    save_raw = TRUE,
-    save_norm = TRUE,
-    save_dir = "~",
-    max_col = 4,
-    height = 7,
-    width = 7,
-    run_simulations = TRUE,
-    ...) {
+runPatternSimulation <- function(
+        gobject,
+        pattern_name = "pattern",
+        pattern_colors = c("in" = "green", "out" = "red"),
+        pattern_cell_ids = NULL,
+        gene_names = NULL,
+        spatial_probs = c(0.5, 1),
+        reps = 2,
+        spatial_network_name = "kNN_network",
+        spat_methods = c(
+            "binSpect_single", "binSpect_multi", "spatialDE",
+            "spark", "silhouetteRank"
+        ),
+        spat_methods_params = list(NA, NA, NA, NA, NA),
+        spat_methods_names = c(
+            "binSpect_single", "binSpect_multi", "spatialDE",
+            "spark", "silhouetteRank"
+        ),
+        scalefactor = 6000,
+        save_plot = TRUE,
+        save_raw = TRUE,
+        save_norm = TRUE,
+        save_dir = "~",
+        max_col = 4,
+        height = 7,
+        width = 7,
+        run_simulations = TRUE,
+        ...) {
     # data.table variables
     prob <- method <- adj.p.value <- time <- NULL
 
@@ -4984,8 +5178,10 @@ runPatternSimulation <- function(gobject,
         pattern_cell_ids = pattern_cell_ids,
         gene_name = gene_names[1],
         spatial_prob = 1,
-        normalization_params = list(scalefactor = scalefactor,
-                                    verbose = TRUE)
+        normalization_params = list(
+            scalefactor = scalefactor,
+            verbose = TRUE
+        )
     )
 
     spatPlot2D(example_patch,
@@ -5053,13 +5249,17 @@ runPatternSimulation <- function(gobject,
 
             if (save_plot == TRUE) {
                 subdir <- paste0(save_dir, "/", pattern_name, "/")
-                if (!file.exists(subdir)) dir.create(
-                    path = subdir, recursive = TRUE)
+                if (!file.exists(subdir)) {
+                    dir.create(
+                        path = subdir, recursive = TRUE
+                    )
+                }
                 # write results
                 data.table::fwrite(
                     x = generesults,
                     file = paste0(subdir, "/", gene, "_results.txt"),
-                    sep = "\t", quote = FALSE)
+                    sep = "\t", quote = FALSE
+                )
             }
 
             all_results[[gene_ind]] <- generesults
@@ -5081,21 +5281,28 @@ runPatternSimulation <- function(gobject,
             pl <- ggplot2::ggplot()
             pl <- pl + ggplot2::geom_boxplot(
                 data = results,
-                ggplot2::aes(x = method, y = adj.p.value, color = prob))
+                ggplot2::aes(x = method, y = adj.p.value, color = prob)
+            )
             pl <- pl + ggplot2::geom_point(
                 data = results,
                 ggplot2::aes(x = method, y = adj.p.value, color = prob),
-                size = 2, position = ggplot2::position_jitterdodge())
+                size = 2, position = ggplot2::position_jitterdodge()
+            )
             pl <- pl + ggplot2::theme_bw() +
                 ggplot2::theme(axis.text.x = ggplot2::element_text(
-                        angle = 90, vjust = 1, hjust = 1))
+                    angle = 90, vjust = 1, hjust = 1
+                ))
             pl <- pl + ggplot2::facet_wrap(~genes, nrow = nr_rows)
             pl <- pl + ggplot2::geom_hline(
-                yintercept = 0.05, color = "red", linetype = 2)
+                yintercept = 0.05, color = "red", linetype = 2
+            )
 
-            grDevices::pdf(file = paste0(
-                save_dir, "/", pattern_name, "_boxplot_pvalues.pdf"),
-                width = width, height = height)
+            grDevices::pdf(
+                file = paste0(
+                    save_dir, "/", pattern_name, "_boxplot_pvalues.pdf"
+                ),
+                width = width, height = height
+            )
             print(pl)
             grDevices::dev.off()
 
@@ -5105,19 +5312,26 @@ runPatternSimulation <- function(gobject,
             pl <- ggplot2::ggplot()
             pl <- pl + ggplot2::geom_boxplot(
                 data = results,
-                ggplot2::aes(x = method, y = -log10(adj.p.value), color = prob))
+                ggplot2::aes(x = method, y = -log10(adj.p.value), color = prob)
+            )
             pl <- pl + ggplot2::geom_point(
                 data = results,
                 ggplot2::aes(x = method, y = -log10(adj.p.value), color = prob),
-                size = 2, position = ggplot2::position_jitterdodge())
+                size = 2, position = ggplot2::position_jitterdodge()
+            )
             pl <- pl + ggplot2::theme_bw() + ggplot2::theme(
                 axis.text.x = ggplot2::element_text(
-                    angle = 90, vjust = 1, hjust = 1))
+                    angle = 90, vjust = 1, hjust = 1
+                )
+            )
             pl <- pl + ggplot2::facet_wrap(~genes, nrow = nr_rows)
 
-            grDevices::pdf(file = paste0(
-                save_dir, "/", pattern_name, "_boxplot_log10pvalues.pdf"),
-                width = width, height = height)
+            grDevices::pdf(
+                file = paste0(
+                    save_dir, "/", pattern_name, "_boxplot_log10pvalues.pdf"
+                ),
+                width = width, height = height
+            )
             print(pl)
             grDevices::dev.off()
 
@@ -5126,18 +5340,25 @@ runPatternSimulation <- function(gobject,
             pl <- ggplot2::ggplot()
             pl <- pl + ggplot2::geom_boxplot(
                 data = results,
-                ggplot2::aes(x = method, y = time, color = prob))
+                ggplot2::aes(x = method, y = time, color = prob)
+            )
             pl <- pl + ggplot2::geom_point(
                 data = results,
                 ggplot2::aes(x = method, y = time, color = prob), size = 2,
-                position = ggplot2::position_jitterdodge())
+                position = ggplot2::position_jitterdodge()
+            )
             pl <- pl + ggplot2::theme_bw() + ggplot2::theme(
                 axis.text.x = ggplot2::element_text(
-                    angle = 90, vjust = 1, hjust = 1))
+                    angle = 90, vjust = 1, hjust = 1
+                )
+            )
 
-            grDevices::pdf(file = paste0(
-                save_dir, "/", pattern_name, "_boxplot_time.pdf"),
-                width = width, height = height)
+            grDevices::pdf(
+                file = paste0(
+                    save_dir, "/", pattern_name, "_boxplot_time.pdf"
+                ),
+                width = width, height = height
+            )
             print(pl)
             grDevices::dev.off()
         }
@@ -5147,7 +5368,8 @@ runPatternSimulation <- function(gobject,
         data.table::fwrite(
             x = results,
             file = paste0(save_dir, "/", pattern_name, "_results.txt"),
-            sep = "\t", quote = FALSE)
+            sep = "\t", quote = FALSE
+        )
         return(results)
     } else {
         return(NULL)
diff --git a/R/spatial_interaction.R b/R/spatial_interaction.R
index cb9f6c5f0..a643c66d0 100644
--- a/R/spatial_interaction.R
+++ b/R/spatial_interaction.R
@@ -51,7 +51,7 @@ make_simulated_network <- function(gobject,
                         spatial_network_annot$to_cell_type)
     middle_point <- length(all_cell_type) / 2
 
-    for (sim in 1:number_of_simulations) {
+    for (sim in seq_len(number_of_simulations)) {
         if (set_seed == TRUE) {
             seed_number <- seed_number + sim
             set.seed(seed = seed_number)
@@ -60,7 +60,7 @@ make_simulated_network <- function(gobject,
         reshuffled_all_cell_type <- sample(
             x = all_cell_type, size = length(all_cell_type), replace = FALSE)
 
-        new_from_cell_type <- reshuffled_all_cell_type[1:middle_point]
+        new_from_cell_type <- reshuffled_all_cell_type[seq_len(middle_point)]
         s1_list[[sim]] <- new_from_cell_type
 
         new_to_cell_type <- reshuffled_all_cell_type[
@@ -70,7 +70,7 @@ make_simulated_network <- function(gobject,
 
     s1_vector <- do.call("c", s1_list)
     s2_vector <- do.call("c", s2_list)
-    round_vector <- rep(x = 1:number_of_simulations, each = length_ints)
+    round_vector <- rep(x = seq_len(number_of_simulations), each = length_ints)
     round_vector <- paste0("sim", round_vector)
 
     # data.table variables
@@ -80,7 +80,7 @@ make_simulated_network <- function(gobject,
         s1 = s1_vector, s2 = s2_vector, round = round_vector)
     uniq_sim_comb <- unique(sample_dt[, .(s1, s2)])
     uniq_sim_comb[, unified_int := paste(
-        sort(c(s1, s2)), collapse = "--"), by = 1:nrow(uniq_sim_comb)]
+        sort(c(s1, s2)), collapse = "--"), by = seq_len(nrow(uniq_sim_comb))]
     sample_dt[uniq_sim_comb, unified_int := unified_int, on = c(
         s1 = "s1", s2 = "s2")]
     sample_dt[, type_int := ifelse(s1 == s2, "homo", "hetero")]
@@ -190,7 +190,8 @@ cellProximityEnrichment <- function(gobject,
     minimum_simulations <- unique_ints[rep(
         seq_len(nrow(unique_ints)), number_of_simulations), ]
     minimum_simulations[, round := rep(
-        paste0("sim", 1:number_of_simulations), each = nrow(unique_ints))]
+        paste0("sim", seq_len(number_of_simulations)),
+        each = nrow(unique_ints))]
     minimum_simulations[, N := 0]
 
     table_sim_minimum_results <- rbind(table_sim_results, minimum_simulations)
@@ -312,7 +313,7 @@ cellProximityEnrichment <- function(gobject,
 
     # order
     table_mean_results_dc <- table_mean_results_dc[order(-PI_value)]
-    table_mean_results_dc[, int_ranking := 1:.N]
+    table_mean_results_dc[, int_ranking := seq_len(.N)]
 
     return(list(raw_sim_table = table_results,
                 enrichm_res = table_mean_results_dc))
@@ -698,7 +699,7 @@ NULL
         seed_number_list <- seed_number:(seed_number + (n - 1))
     }
 
-    result <- lapply(X = 1:n, FUN = function(x) {
+    result <- lapply(X = seq_len(n), FUN = function(x) {
         seed_number <- seed_number_list[x]
 
         perm_rand <- .do_permuttest_random(
@@ -1051,9 +1052,13 @@ NULL
 
 #' @title findInteractionChangedFeats
 #' @name findInteractionChangedFeats
+#' @aliases findICF
 #' @description Identifies cell-to-cell Interaction Changed Features (ICF),
 #' i.e. features that are differentially expressed due to proximity to other
-#' cell types.
+#' cell types. This function is appropriate for single-cell level data. For
+#' data from spot-based spatial assays or spatially binned data, see
+#' [findICFSpot()], which runs on top of DWLS results or similar spot-level
+#' cell-type enrichment outputs
 #' @param gobject giotto object
 #' @param feat_type feature type
 #' @param spat_unit spatial unit
@@ -1072,33 +1077,48 @@ NULL
 #' @param do_parallel run calculations in parallel with mclapply
 #' @param set_seed set a seed for reproducibility
 #' @param seed_number seed number
-#' @returns icfObject that contains the Interaction Changed differential
+#' @returns `icfObject` that contains the Interaction Changed differential
 #' feature scores
 #' @details Function to calculate if features are differentially expressed in
 #' cell types when they interact (approximated by physical proximity) with
-#' other cell types. The results data.table in the icfObject contains
+#' other cell types. The results data.table in the `icfObject` contains
 #' - at least - the following columns:
-#' \itemize{
-#'  \item{features:}{ All or selected list of tested features}
-#'  \item{sel:}{ average feature expression in the interacting cells from the target cell type }
-#'  \item{other:}{ average feature expression in the NOT-interacting cells from the target cell type }
-#'  \item{log2fc:}{ log2 fold-change between sel and other}
-#'  \item{diff:}{ spatial expression difference between sel and other}
-#'  \item{p.value:}{ associated p-value}
-#'  \item{p.adj:}{ adjusted p-value}
-#'  \item{cell_type:}{ target cell type}
-#'  \item{int_cell_type:}{ interacting cell type}
-#'  \item{nr_select:}{ number of cells for selected target cell type}
-#'  \item{int_nr_select:}{ number of cells for interacting cell type}
-#'  \item{nr_other:}{ number of other cells of selected target cell type}
-#'  \item{int_nr_other:}{ number of other cells for interacting cell type}
-#'  \item{unif_int:}{ cell-cell interaction}
-#' }
+#'   * **features:** All or selected list of tested features
+#'   * **sel:** average feature expression in the interacting cells from the
+#'   target cell type
+#'   * **other:** average feature expression in the NOT-interacting cells from
+#'   the target cell type
+#'   * **log2fc:** log2 fold-change between sel and other
+#'   * **diff:** spatial expression difference between sel and other
+#'   * **p.value:** associated p-value
+#'   * **p.adj:** adjusted p-value
+#'   * **cell_type:** target cell type
+#'   * **int_cell_type:** interacting cell type
+#'   * **nr_select:** number of cells for selected target cell type
+#'   * **int_nr_select:** number of cells for interacting cell type
+#'   * **nr_other:** number of other cells of selected target cell type
+#'   * **int_nr_other:** number of other cells for interacting cell type
+#'   * **unif_int:** cell-cell interaction
+#'
+#' @seealso [filterInteractionChangedFeats()] [findICFSpot()]
+#' @md
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #'
-#' findInteractionChangedFeats(g, cluster_column = "leiden_clus",
-#' selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
+#' icf1 <- findInteractionChangedFeats(g,
+#'     cluster_column = "leiden_clus",
+#'     selected_feats = c("Gna12", "Ccnd2", "Btbd17"),
+#'     nr_permutations = 10
+#' )
+#' force(icf1)
+#' force(icf1$ICFscores)
+#'
+#' # this is just an alias with a shorter name
+#' icf2 <- findICF(g,
+#'     cluster_column = "leiden_clus",
+#'     selected_feats = c("Gna12", "Ccnd2", "Btbd17"),
+#'     nr_permutations = 10
+#'  )
 #' @export
 findInteractionChangedFeats <- function(gobject,
     feat_type = NULL,
@@ -1164,8 +1184,9 @@ findInteractionChangedFeats <- function(gobject,
     mean_method <- match.arg(mean_method, choices = c("arithmic", "geometric"))
 
     ## metadata
-    cell_metadata <- pDataDT(gobject, feat_type = feat_type)
-
+    cell_metadata <- pDataDT(
+        gobject, spat_unit = spat_unit, feat_type = feat_type
+    )
 
 
     ## annotated spatial network
@@ -1178,267 +1199,108 @@ findInteractionChangedFeats <- function(gobject,
 
     all_interactions <- unique(annot_spatnetwork$unified_int)
 
-    if (do_parallel == TRUE) {
-        fin_result <- lapply_flex(
-            X = all_interactions, future.seed = TRUE, FUN = function(x) {
+    ## prepare function
+    fcp_feats_per_i <- function(x) {
+        .findCellProximityFeats_per_interaction(
+            expr_values = expr_values,
+            cell_metadata = cell_metadata,
+            annot_spatnetwork = annot_spatnetwork,
+            minimum_unique_cells = minimum_unique_cells,
+            minimum_unique_int_cells = minimum_unique_int_cells,
+            sel_int = x,
+            cluster_column = cluster_column,
+            exclude_selected_cells_from_test = exclude_selected_cells_from_test,
+            diff_test = diff_test,
+            mean_method = mean_method,
+            offset = offset,
+            adjust_method = adjust_method,
+            nr_permutations = nr_permutations,
+            set_seed = set_seed,
+            seed_number = seed_number
+        )
+    }
 
-            tempres <- .findCellProximityFeats_per_interaction(
-                expr_values = expr_values,
-                cell_metadata = cell_metadata,
-                annot_spatnetwork = annot_spatnetwork,
-                minimum_unique_cells = minimum_unique_cells,
-                minimum_unique_int_cells = minimum_unique_int_cells,
-                sel_int = x,
-                cluster_column = cluster_column,
-                exclude_selected_cells_from_test = exclude_selected_cells_from_test,
-                diff_test = diff_test,
-                mean_method = mean_method,
-                offset = offset,
-                adjust_method = adjust_method,
-                nr_permutations = nr_permutations,
-                set_seed = set_seed,
-                seed_number = seed_number
-            )
-        })
-    } else {
+    if (isTRUE(do_parallel)) { # parallel
+        fin_result <- lapply_flex(
+            X = all_interactions, future.seed = TRUE, FUN = fcp_feats_per_i
+        )
+    } else { # sequential
         fin_result <- list()
 
         for (i in seq_along(all_interactions)) {
             x <- all_interactions[i]
-
-
-            tempres <- .findCellProximityFeats_per_interaction(
-                expr_values = expr_values,
-                cell_metadata = cell_metadata,
-                annot_spatnetwork = annot_spatnetwork,
-                minimum_unique_cells = minimum_unique_cells,
-                minimum_unique_int_cells = minimum_unique_int_cells,
-                sel_int = x,
-                cluster_column = cluster_column,
-                exclude_selected_cells_from_test = exclude_selected_cells_from_test,
-                diff_test = diff_test,
-                mean_method = mean_method,
-                offset = offset,
-                adjust_method = adjust_method,
-                nr_permutations = nr_permutations,
-                set_seed = set_seed,
-                seed_number = seed_number
-            )
-
+            tempres <- fcp_feats_per_i(x)
             fin_result[[i]] <- tempres
         }
     }
 
     final_result <- do.call("rbind", fin_result)
 
-
-
-    # data.table variables
+    # NSE variables
     spec_int <- cell_type <- int_cell_type <- type_int <- NULL
 
     final_result[, spec_int := paste0(cell_type, "--", int_cell_type)]
     final_result[, type_int := ifelse(
         cell_type == int_cell_type, "homo", "hetero")]
 
-
-    # return(final_result)
-
     permutation_test <- ifelse(
         diff_test == "permutation", nr_permutations, "no permutations")
 
-    icfObject <- list(
-        ICFscores = final_result,
-        Giotto_info = list(
-            "values" = values,
-            "cluster" = cluster_column,
-            "spatial network" = spatial_network_name
+    icfObject <- structure(
+        .Data = list(
+            ICFscores = final_result,
+            Giotto_info = list(
+                "values" = values,
+                "cluster" = cluster_column,
+                "spatial network" = spatial_network_name
+            ),
+            test_info = list(
+                "test" = diff_test,
+                "p.adj" = adjust_method,
+                "min cells" = minimum_unique_cells,
+                "min interacting cells" = minimum_unique_int_cells,
+                "exclude selected cells" = exclude_selected_cells_from_test,
+                "perm" = permutation_test
+            )
         ),
-        test_info = list(
-            "test" = diff_test,
-            "p.adj" = adjust_method,
-            "min cells" = minimum_unique_cells,
-            "min interacting cells" = minimum_unique_int_cells,
-            "exclude selected cells" = exclude_selected_cells_from_test,
-            "perm" = permutation_test
-        )
+        class = "icfObject"
     )
-    class(icfObject) <- append("icfObject", class(icfObject))
     return(icfObject)
 }
 
-
-
-#' @title findInteractionChangedGenes
-#' @name findInteractionChangedGenes
-#' @description Identifies cell-to-cell Interaction Changed Features (ICF),
-#' i.e. genes that are differentially expressed due to interactions with other
-#' cell types.
-#' @param ... params to pass to \code{findInteractionChangedFeats}
-#' @seealso \code{\link{findInteractionChangedFeats}}
-#' @returns interaction changed genes
-#' @export
-findInteractionChangedGenes <- function(...) {
-    .Deprecated(new = "findInteractionChangedFeats")
-
-    findInteractionChangedFeats(...)
-}
-
-
-
-#' @title findCellProximityGenes
-#' @name findCellProximityGenes
-#' @description Identifies cell-to-cell Interaction Changed Features (ICF),
-#' i.e. genes that are differentially expressed due to proximity to other cell
-#' types.
-#' @inheritDotParams findInteractionChangedFeats
-#' @seealso \code{\link{findInteractionChangedFeats}}
-#' @returns cell-cell interaction changed genes
+#' @rdname findInteractionChangedFeats
 #' @export
-findCellProximityGenes <- function(...) {
-    .Deprecated(new = "findInteractionChangedFeats")
-
-    findInteractionChangedFeats(...)
-}
-
+findICF <- findInteractionChangedFeats
 
-
-
-
-#' @title findICF
-#' @name findICF
-#' @description Identifies cell-to-cell Interaction Changed Features (ICF),
-#' i.e. features that are differentially expressed due to proximity to other
-#' cell types.
-#' @param gobject giotto object
-#' @param feat_type feature type
-#' @param spat_unit spatial unit
-#' @param expression_values expression values to use
-#' @param selected_feats subset of selected features (optional)
-#' @param cluster_column name of column to use for cell types
-#' @param spatial_network_name name of spatial network to use
-#' @param minimum_unique_cells minimum number of target cells required
-#' @param minimum_unique_int_cells minimum number of interacting cells required
-#' @param diff_test which differential expression test
-#' @param mean_method method to use to calculate the mean
-#' @param offset offset value to use when calculating log2 ratio
-#' @param adjust_method which method to adjust p-values
-#' @param nr_permutations number of permutations if diff_test = permutation
-#' @param exclude_selected_cells_from_test exclude interacting cells other cells
-#' @param do_parallel run calculations in parallel with mclapply
-#' @param set_seed set a seed for reproducibility
-#' @param seed_number seed number
-#' @returns `icfObject` that contains the Interaction Changed differential gene
-#' scores
-#' @details Function to calculate if genes are differentially expressed in
-#' cell types when they interact (approximated by physical proximity) with
-#' other cell types. The results data.table in the `icfObject` contains
-#' - at least - the following columns:
-#' \itemize{
-#'  \item{features:}{ All or selected list of tested features}
-#'  \item{sel:}{ average feature expression in the interacting cells from the target cell type }
-#'  \item{other:}{ average feature expression in the NOT-interacting cells from the target cell type }
-#'  \item{log2fc:}{ log2 fold-change between sel and other}
-#'  \item{diff:}{ spatial expression difference between sel and other}
-#'  \item{p.value:}{ associated p-value}
-#'  \item{p.adj:}{ adjusted p-value}
-#'  \item{cell_type:}{ target cell type}
-#'  \item{int_cell_type:}{ interacting cell type}
-#'  \item{nr_select:}{ number of cells for selected target cell type}
-#'  \item{int_nr_select:}{ number of cells for interacting cell type}
-#'  \item{nr_other:}{ number of other cells of selected target cell type}
-#'  \item{int_nr_other:}{ number of other cells for interacting cell type}
-#'  \item{unif_int:}{ cell-cell interaction}
-#' }
-#' @seealso \code{\link{findInteractionChangedFeats}}
-#' @examples
-#' g <- GiottoData::loadGiottoMini("visium")
-#'
-#' findICF(g, cluster_column = "leiden_clus",
-#' selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
+#' @name print.icfObject
+#' @title icfObject print method
+#' @param x object to print
+#' @param \dots additional params to pass (none implemented)
+#' @keywords internal
 #' @export
-findICF <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    expression_values = "normalized",
-    selected_feats = NULL,
-    cluster_column,
-    spatial_network_name = "Delaunay_network",
-    minimum_unique_cells = 1,
-    minimum_unique_int_cells = 1,
-    diff_test = c("permutation", "limma", "t.test", "wilcox"),
-    mean_method = c("arithmic", "geometric"),
-    offset = 0.1,
-    adjust_method = c(
-        "bonferroni", "BH", "holm", "hochberg", "hommel",
-        "BY", "fdr", "none"
-    ),
-    nr_permutations = 100,
-    exclude_selected_cells_from_test = TRUE,
-    do_parallel = TRUE,
-    set_seed = TRUE,
-    seed_number = 1234) {
-    findInteractionChangedFeats(
-        gobject = gobject,
-        feat_type = feat_type,
-        spat_unit = spat_unit,
-        expression_values = expression_values,
-        selected_feats = selected_feats,
-        cluster_column = cluster_column,
-        spatial_network_name = spatial_network_name,
-        minimum_unique_cells = minimum_unique_cells,
-        minimum_unique_int_cells = minimum_unique_int_cells,
-        diff_test = diff_test,
-        mean_method = mean_method,
-        offset = offset,
-        adjust_method = adjust_method,
-        nr_permutations = nr_permutations,
-        exclude_selected_cells_from_test = exclude_selected_cells_from_test,
-        do_parallel = do_parallel,
-        set_seed = set_seed,
-        seed_number = seed_number
+print.icfObject <- function(x, ...) {
+    cat("An object of class", class(x), "\n")
+    info <- list(
+        dimensions = sprintf("%d, %d (icfs, attributes)",
+                             nrow(x$ICFscores), ncol(x$ICFscores))
     )
+    print_list(info, pre = " -")
+    cat("<giotto info>\n")
+    print_list(x$Giotto_info, pre = " -")
+    cat("<test info>\n")
+    print_list(x$test_info, pre = " -")
 }
 
 
 
 
-#' @title findICG
-#' @name findICG
-#' @description Identifies cell-to-cell Interaction Changed Features (ICF),
-#' i.e. genes that are differentially expressed due to interaction with other
-#' cell types.
-#' @inheritDotParams findICF
-#' @seealso \code{\link{findICF}}
-#' @returns cell-cell interaction changed features
-#' @export
-findICG <- function(...) {
-    .Deprecated(new = "findICF")
-
-    findICF(...)
-}
-
-
-
-#' @title findCPG
-#' @name findCPG
-#' @description Identifies cell-to-cell Interaction Changed Features (ICF),
-#' i.e. genes that are differentially expressed due to proximity to other cell
-#' types.
-#' @inheritDotParams findICF
-#' @returns cell-to-cell Interaction Changed Genes
-#' @seealso \code{\link{findICF}}
-#' @export
-findCPG <- function(...) {
-    .Deprecated(new = "findICF")
-
-    findICF(...)
-}
 
 
 
 
 #' @title filterInteractionChangedFeats
 #' @name filterInteractionChangedFeats
+#' @aliases filterICF
 #' @description Filter Interaction Changed Feature scores.
 #' @param icfObject ICF (interaction changed feature) score object
 #' @param min_cells minimum number of source cell type
@@ -1452,7 +1314,27 @@ findCPG <- function(...) {
 #' @param min_zscore minimum z-score change
 #' @param zscores_column calculate z-scores over cell types or genes
 #' @param direction differential expression directions to keep
-#' @returns icfObject that contains the filtered differential feature scores
+#' @returns `icfObject` that contains the filtered differential feature scores
+#' @md
+#' @examples
+#' g <- GiottoData::loadGiottoMini("visium")
+#'
+#' icf <- findInteractionChangedFeats(g,
+#'      cluster_column = "leiden_clus",
+#'      selected_feats = c("Gna12", "Ccnd2", "Btbd17"),
+#'      nr_permutations = 10
+#' )
+#' force(icf)
+#' force(icf$ICFscores)
+#'
+#' icf_filter1 <- filterInteractionChangedFeats(icf, min_cells = 4)
+#' force(icf_filter1)
+#' force(icf_filter1$ICFscores)
+#'
+#' # filterICF is a simple alias with a shortened name
+#' icf_filter2 <- filterICF(icf, min_cells = 4)
+#' force(icf_filter2)
+#'
 #' @export
 filterInteractionChangedFeats <- function(icfObject,
     min_cells = 4,
@@ -1465,7 +1347,7 @@ filterInteractionChangedFeats <- function(icfObject,
     min_zscore = 2,
     zscores_column = c("cell_type", "feats"),
     direction = c("both", "up", "down")) {
-    # data.table variables
+    # NSE vars
     nr_select <- int_nr_select <- zscores <- log2fc <- sel <- other <-
         p.adj <- NULL
 
@@ -1521,123 +1403,19 @@ filterInteractionChangedFeats <- function(icfObject,
     return(newobj)
 }
 
-
-#' @title filterInteractionChangedGenes
-#' @name filterInteractionChangedGenes
-#' @description Filter Interaction Changed Feature scores.
-#' @inheritDotParams filterInteractionChangedFeats
-#' @seealso \code{\link{filterInteractionChangedFeats}}
-#' @returns filtered interaction changed feature scores
-#' @export
-filterInteractionChangedGenes <- function(...) {
-    .Deprecated(new = "filterInteractionChangedFeats")
-
-    filterInteractionChangedFeats(...)
-}
-
-
-#' @title filterCellProximityGenes
-#' @name filterCellProximityGenes
-#' @description Filter Interaction Changed Feature scores.
-#' @inheritDotParams filterInteractionChangedFeats
-#' @seealso \code{\link{filterInteractionChangedFeats}}
-#' @returns proximity genes
+#' @rdname filterInteractionChangedFeats
 #' @export
-filterCellProximityGenes <- function(...) {
-    .Deprecated(new = "filterInteractionChangedFeats")
-
-    filterInteractionChangedFeats(...)
-}
+filterICF <- filterInteractionChangedFeats
 
 
 
 
 
-#' @title filterICF
-#' @name filterICF
-#' @description Filter Interaction Changed Feature scores.
-#' @param icfObject ICF (interaction changed feature) score object
-#' @param min_cells minimum number of source cell type
-#' @param min_cells_expr minimum expression level for source cell type
-#' @param min_int_cells minimum number of interacting neighbor cell type
-#' @param min_int_cells_expr minimum expression level for interacting neighbor
-#' cell type
-#' @param min_fdr minimum adjusted p-value
-#' @param min_spat_diff minimum absolute spatial expression difference
-#' @param min_log2_fc minimum log2 fold-change
-#' @param min_zscore minimum z-score change
-#' @param zscores_column calculate z-scores over cell types or features
-#' @param direction differential expression directions to keep
-#' @returns icfObject that contains the filtered differential feature scores
-#' @examples
-#' g <- GiottoData::loadGiottoMini("visium")
-#'
-#' g_icf <- findInteractionChangedFeats(g, cluster_column = "leiden_clus")
-#'
-#' filterICF(g_icf)
-#' @export
-filterICF <- function(icfObject,
-    min_cells = 4,
-    min_cells_expr = 1,
-    min_int_cells = 4,
-    min_int_cells_expr = 1,
-    min_fdr = 0.1,
-    min_spat_diff = 0.2,
-    min_log2_fc = 0.2,
-    min_zscore = 2,
-    zscores_column = c("cell_type", "feats"),
-    direction = c("both", "up", "down")) {
-    filterInteractionChangedFeats(
-        icfObject = icfObject,
-        min_cells = min_cells,
-        min_cells_expr = min_cells_expr,
-        min_int_cells = min_int_cells,
-        min_int_cells_expr = min_int_cells_expr,
-        min_fdr = min_fdr,
-        min_spat_diff = min_spat_diff,
-        min_log2_fc = min_log2_fc,
-        min_zscore = min_zscore,
-        zscores_column = zscores_column,
-        direction = direction
-    )
-}
-
-
-
 
-#' @title filterICG
-#' @name filterICG
-#' @description Filter Interaction Changed Gene scores.
-#' @inheritDotParams filterICF
-#' @seealso \code{\link{filterICF}}
-#' @returns filtered interaction changed gene scores
-#' @export
-filterICG <- function(...) {
-    .Deprecated(new = "filterICF")
 
-    filterICF(...)
-}
 
-
-
-#' @title filterCPG
-#' @name filterCPG
-#' @description Filter Interaction Changed Gene scores.
-#' @inheritDotParams filterICF
-#' @seealso \code{\link{filterICF}}
-#' @returns filtered interaction changed gene scores
-#' @export
-filterCPG <- function(...) {
-    .Deprecated(new = "filterICF")
-
-    filterICF(...)
-}
-
-
-
-
-# * ####
-# FTF feat-to-feat (pairs of ICF) ####
+# * ####
+# FTF feat-to-feat (pairs of ICF) ####
 
 #' @title Combine ICF scores per interaction
 #' @name .combineInteractionChangedFeatures_per_interaction
@@ -1981,6 +1759,7 @@ filterCPG <- function(...) {
 
 #' @title combineInteractionChangedFeats
 #' @name combineInteractionChangedFeats
+#' @aliases combineICF
 #' @description Combine ICF scores in a pairwise manner.
 #' @param icfObject ICF (interaction changed feat) score object
 #' @param selected_ints subset of selected cell-cell interactions (optional)
@@ -1996,14 +1775,19 @@ filterCPG <- function(...) {
 #' @param min_log2_fc minimum absolute log2 fold-change
 #' @param do_parallel run calculations in parallel with mclapply
 #' @param verbose verbose
-#' @returns combIcfObject that contains the filtered differential feature scores
+#' @returns `combIcfObject` that contains the filtered differential feature
+#' scores
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #' g_icf <- findInteractionChangedFeats(g,
-#' cluster_column = "leiden_clus",
-#' selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
+#'     cluster_column = "leiden_clus",
+#'     selected_feats = c("Gna12", "Ccnd2", "Btbd17"),
+#'     nr_permutations = 10
+#' )
 #'
-#' combineInteractionChangedFeats(g_icf)
+#' cicf <- combineInteractionChangedFeats(g_icf)
+#' force(cicf)
+#' combineICF(g_icf) # this is a shortened alias
 #' @export
 combineInteractionChangedFeats <- function(icfObject,
     selected_ints = NULL,
@@ -2017,7 +1801,7 @@ combineInteractionChangedFeats <- function(icfObject,
     min_log2_fc = 0.5,
     do_parallel = TRUE,
     verbose = TRUE) {
-    # data.table variables
+    # NSE vars
     unif_int <- feat1_feat2 <- feats_1 <- feats_2 <- comb_logfc <-
         log2fc_1 <- log2fc_2 <- direction <- NULL
 
@@ -2090,145 +1874,62 @@ combineInteractionChangedFeats <- function(icfObject,
         ifelse(log2fc_1 < 0 & log2fc_2 < 0, "both_down", "mixed")
     )]
 
-    combIcfObject <- list(
-        combICFscores = final_results,
-        Giotto_info = list(
-            "values" = icfObject[["Giotto_info"]][["values"]],
-            "cluster" = icfObject[["Giotto_info"]][["cluster"]],
-            "spatial network" = icfObject[["Giotto_info"]][["spatial network"]]
+    combIcfObject <- structure(
+        .Data = list(
+            combICFscores = final_results,
+            Giotto_info = list(
+                "values" = icfObject[["Giotto_info"]][["values"]],
+                "cluster" = icfObject[["Giotto_info"]][["cluster"]],
+                "spatial network" =
+                    icfObject[["Giotto_info"]][["spatial network"]]
+            ),
+            test_info = list(
+                "test" = icfObject[["test_info"]][["test"]],
+                "p.adj" = icfObject[["test_info"]][["p.adj"]],
+                "min cells" = icfObject[["test_info"]][["min cells"]],
+                "min interacting cells" = icfObject[["test_info"]][[
+                    "min interacting cells"]],
+                "exclude selected cells" = icfObject[["test_info"]][[
+                    "exclude selected cells"]],
+                "perm" = icfObject[["test_info"]][["perm"]]
+            )
         ),
-        test_info = list(
-            "test" = icfObject[["test_info"]][["test"]],
-            "p.adj" = icfObject[["test_info"]][["p.adj"]],
-            "min cells" = icfObject[["test_info"]][["min cells"]],
-            "min interacting cells" = icfObject[["test_info"]][[
-                "min interacting cells"]],
-            "exclude selected cells" = icfObject[["test_info"]][[
-                "exclude selected cells"]],
-            "perm" = icfObject[["test_info"]][["perm"]]
-        )
+        class = "combIcfObject"
     )
-    class(combIcfObject) <- append(class(combIcfObject), "combIcfObject")
     return(combIcfObject)
 }
 
-
-#' @title combineInteractionChangedGenes
-#' @name combineInteractionChangedGenes
-#' @description Combine ICF scores in a pairwise manner.
-#' @inheritDotParams combineInteractionChangedFeats
-#' @returns ICF scores
-#' @seealso \code{\link{combineInteractionChangedFeats}}
-#' @export
-combineInteractionChangedGenes <- function(...) {
-    .Deprecated(new = "combineInteractionChangedFeats")
-
-    combineInteractionChangedFeats(...)
-}
-
-
-#' @title combineCellProximityGenes
-#' @name combineCellProximityGenes
-#' @description Combine ICF scores in a pairwise manner.
-#' @inheritDotParams combineInteractionChangedFeats
-#' @returns ICF scores
-#' @seealso \code{\link{combineInteractionChangedFeats}}
+#' @rdname combineInteractionChangedFeats
 #' @export
-combineCellProximityGenes <- function(...) {
-    .Deprecated(new = "combineInteractionChangedFeats")
-
-    combineInteractionChangedFeats(...)
-}
+combineICF <- combineInteractionChangedFeats
 
-#' @title combineICF
-#' @name combineICF
-#' @description Combine ICF scores in a pairwise manner.
-#' @param icfObject ICF (interaction changed feat) score object
-#' @param selected_ints subset of selected cell-cell interactions (optional)
-#' @param selected_feats subset of selected Feats (optional)
-#' @param specific_feats_1 specific Featset combo
-#' (need to position match specific_genes_2)
-#' @param specific_feats_2 specific Featset combo
-#' (need to position match specific_genes_1)
-#' @param min_cells minimum number of target cell type
-#' @param min_int_cells minimum number of interacting cell type
-#' @param min_fdr minimum adjusted p-value
-#' @param min_spat_diff minimum absolute spatial expression difference
-#' @param min_log2_fc minimum absolute log2 fold-change
-#' @param do_parallel run calculations in parallel with mclapply
-#' @param verbose verbose
-#' @returns icfObject that contains the filtered differential feats scores
-#' @examples
-#' g <- GiottoData::loadGiottoMini("visium")
-#' g_icf <- findInteractionChangedFeats(g, cluster_column = "leiden_clus",
-#' selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
-#'
-#' combineICF(g_icf)
+#' @name print.combIcfObject
+#' @title combIcfObject print method
+#' @param x object to print
+#' @param \dots additional params to pass (none implemented)
+#' @keywords internal
 #' @export
-combineICF <- function(icfObject,
-    selected_ints = NULL,
-    selected_feats = NULL,
-    specific_feats_1 = NULL,
-    specific_feats_2 = NULL,
-    min_cells = 5,
-    min_int_cells = 3,
-    min_fdr = 0.05,
-    min_spat_diff = 0,
-    min_log2_fc = 0.5,
-    do_parallel = TRUE,
-    verbose = TRUE) {
-    combineInteractionChangedFeats(
-        icfObject = icfObject,
-        selected_ints = selected_ints,
-        selected_feats = selected_feats,
-        specific_feats_1 = specific_feats_1,
-        specific_feats_2 = specific_feats_2,
-        min_cells = min_cells,
-        min_int_cells = min_int_cells,
-        min_fdr = min_fdr,
-        min_spat_diff = min_spat_diff,
-        min_log2_fc = min_log2_fc,
-        do_parallel = do_parallel,
-        verbose = verbose
+print.combIcfObject <- function(x, ...) {
+    cat("An object of class", class(x), "\n")
+    info <- list(
+        dimensions = sprintf("%d, %d (icf pairs, attributes)",
+                             nrow(x$combICFscores), ncol(x$combICFscores))
     )
+    print_list(info, pre = " -")
+    cat("<giotto info>\n")
+    print_list(x$Giotto_info, pre = " -")
+    cat("<test info>\n")
+    print_list(x$test_info, pre = " -")
 }
 
 
-#' @title combineICG
-#' @name combineICG
-#' @description Combine ICF scores in a pairwise manner.
-#' @inheritDotParams combineICF
-#' @returns ICF scores
-#' @seealso \code{\link{combineICF}}
-#' @export
-combineICG <- function(...) {
-    .Deprecated(new = "combineICF")
-
-    combineICF(...)
-}
-
-#' @title combineCPG
-#' @name combineCPG
-#' @description Combine ICF scores in a pairwise manner.
-#' @inheritDotParams combineICF
-#' @returns ICF scores
-#' @seealso \code{\link{combineICF}}
-#' @export
-combineCPG <- function(...) {
-    .Deprecated(new = "combineICF")
-
-    combineICF(...)
-}
-
-
-
 
 # * ####
 # cell communication ####
 
 
 #' @title average_feat_feat_expression_in_groups
-#' @name average_feat_feat_expression_in_groups
+#' @name .average_feat_feat_expression_in_groups
 #' @description calculate average expression per cluster
 #' @param gobject giotto object to use
 #' @param spat_unit spatial unit
@@ -2238,7 +1939,7 @@ combineCPG <- function(...) {
 #' @param feat_set_2 second specific feat set from feat pairs
 #' @returns data.table with average expression scores for each cluster
 #' @keywords internal
-average_feat_feat_expression_in_groups <- function(gobject,
+.average_feat_feat_expression_in_groups <- function(gobject,
     spat_unit = NULL,
     feat_type = NULL,
     cluster_column = "cell_types",
@@ -2296,7 +1997,8 @@ average_feat_feat_expression_in_groups <- function(gobject,
     lig_test <- data.table::as.data.table(
         reshape2::melt(ligand_match, measure.vars = all_ligand_cols))
     lig_test[, ligand := rep(rownames(ligand_match), ncol(ligand_match))]
-    lig_test[, ligand := strsplit(ligand, "\\.")[[1]][1], by = 1:nrow(lig_test)]
+    lig_test[, ligand := strsplit(ligand, "\\.")[[1]][1],
+            by = seq_len(nrow(lig_test))]
     lig_test[, LR_comb := rep(LR_pairs, ncol(ligand_match))]
     setnames(lig_test, "value", "lig_expr")
     setnames(lig_test, "variable", "lig_cell_type")
@@ -2306,7 +2008,7 @@ average_feat_feat_expression_in_groups <- function(gobject,
         receptor_match, measure.vars = all_receptor_cols))
     rec_test[, receptor := rep(rownames(receptor_match), ncol(receptor_match))]
     rec_test[, receptor := strsplit(
-        receptor, "\\.")[[1]][1], by = 1:nrow(rec_test)]
+        receptor, "\\.")[[1]][1], by = seq_len(nrow(rec_test))]
     rec_test[, LR_comb := rep(LR_pairs, ncol(receptor_match))]
     setnames(rec_test, "value", "rec_expr")
     setnames(rec_test, "variable", "rec_cell_type")
@@ -2355,8 +2057,13 @@ average_feat_feat_expression_in_groups <- function(gobject,
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #'
-#' exprCellCellcom(g, cluster_column = "leiden_clus",
-#' feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik")
+#' res <- exprCellCellcom(g,
+#'     cluster_column = "leiden_clus",
+#'     feat_set_1 = "Gm19935",
+#'     feat_set_2 = "9630013A20Rik"
+#' )
+#'
+#' force(res)
 #' @export
 exprCellCellcom <- function(gobject,
     feat_type = NULL,
@@ -2408,7 +2115,7 @@ exprCellCellcom <- function(gobject,
     names(nr_cells) <- nr_cell_types$cluster_column
 
 
-    comScore <- average_feat_feat_expression_in_groups(
+    comScore <- .average_feat_feat_expression_in_groups(
         gobject = gobject,
         feat_type = feat_type,
         spat_unit = spat_unit,
@@ -2436,60 +2143,67 @@ exprCellCellcom <- function(gobject,
     # not yet available
 
 
-    for (sim in 1:random_iter) {
-        if (verbose == TRUE) cat("simulation ", sim)
+    progressr::with_progress({
+        pb <- progressr::progressor(steps = random_iter)
 
+        for (sim in seq_len(random_iter)) {
 
-        # create temporary giotto
-        tempGiotto <- subsetGiotto(
-            gobject = gobject,
-            feat_type = feat_type,
-            spat_unit = spat_unit
-        )
+            # create temporary giotto
+            tempGiotto <- subsetGiotto(
+                gobject = gobject,
+                feat_type = feat_type,
+                spat_unit = spat_unit
+            )
 
-        # randomize annoation
-        cell_types <- cell_metadata[[cluster_column]]
-        if (set_seed == TRUE) {
-            seed_number <- seed_number + sim
-            set.seed(seed = seed_number)
-        }
-        random_cell_types <- sample(x = cell_types, size = length(cell_types))
-        tempGiotto <- addCellMetadata(
-            gobject = tempGiotto,
-            feat_type = feat_type,
-            spat_unit = spat_unit,
-            new_metadata = random_cell_types,
-            by_column = FALSE # on purpose since values are random
-        )
+            # randomize annoation
+            cell_types <- cell_metadata[[cluster_column]]
+            if (set_seed == TRUE) {
+                seed_number <- seed_number + sim
+                set.seed(seed = seed_number)
+            }
+            random_cell_types <- sample(
+                x = cell_types, size = length(cell_types)
+            )
+            tempGiotto <- addCellMetadata(
+                gobject = tempGiotto,
+                feat_type = feat_type,
+                spat_unit = spat_unit,
+                new_metadata = random_cell_types,
+                by_column = FALSE # on purpose since values are random
+            )
 
-        # get random communication scores
-        randomScore <- average_feat_feat_expression_in_groups(
-            gobject = tempGiotto,
-            feat_type = feat_type,
-            spat_unit = spat_unit,
-            cluster_column = "random_cell_types",
-            feat_set_1 = feat_set_1,
-            feat_set_2 = feat_set_2
-        )
+            # get random communication scores
+            randomScore <- .average_feat_feat_expression_in_groups(
+                gobject = tempGiotto,
+                feat_type = feat_type,
+                spat_unit = spat_unit,
+                cluster_column = "random_cell_types",
+                feat_set_1 = feat_set_1,
+                feat_set_2 = feat_set_2
+            )
+
+            # average random score
+            total_av <- total_av + randomScore[["LR_expr"]]
 
-        # average random score
-        total_av <- total_av + randomScore[["LR_expr"]]
+            # difference between observed and random
+            difference <- comScore[["LR_expr"]] - randomScore[["LR_expr"]]
+
+            # calculate total difference
+            if (detailed == FALSE) {
+                total_sum <- total_sum + difference
+            } else {
+                total_sum[, sim] <- difference
+            }
 
-        # difference between observed and random
-        difference <- comScore[["LR_expr"]] - randomScore[["LR_expr"]]
+            # calculate p-values
+            difference[difference > 0] <- 1
+            difference[difference < 0] <- -1
+            total_bool <- total_bool + difference
 
-        # calculate total difference
-        if (detailed == FALSE) {
-            total_sum <- total_sum + difference
-        } else {
-            total_sum[, sim] <- difference
+            pb(sprintf("simulation %d", sim))
         }
+    })
 
-        # calculate p-values
-        difference[difference > 0] <- 1
-        difference[difference < 0] <- -1
-        total_bool <- total_bool + difference
-    }
 
     comScore[, rand_expr := total_av / random_iter]
 
@@ -2582,7 +2296,7 @@ exprCellCellcom <- function(gobject,
             set.seed(seed = seed_number)
         }
         sub_sample_ids <- possible_metadata[get(cluster_column) == uniq_type][
-            sample(x = 1:.N, size = length_random)][["cell_ID"]]
+            sample(x = seq_len(.N), size = length_random)][["cell_ID"]]
         sample_ids[[i]] <- sub_sample_ids
     }
     return(unlist(sample_ids))
@@ -2591,9 +2305,9 @@ exprCellCellcom <- function(gobject,
 
 
 
-#' @title specificCellCellcommunicationScores
-#' @name specificCellCellcommunicationScores
-#' @description Specific Cell-Cell communication scores based on spatial
+#' @title Spatial cell cell communication scoring
+#' @name spatCellCellcom
+#' @description Spatial Cell-Cell communication scores based on spatial
 #' expression of interacting cells
 #' @param gobject giotto object to use
 #' @param feat_type feature type
@@ -2602,10 +2316,8 @@ exprCellCellcom <- function(gobject,
 #' interacting cells
 #' @param cluster_column cluster column with cell type information
 #' @param random_iter number of iterations
-#' @param cell_type_1 first cell type
-#' @param cell_type_2 second cell type
-#' @param feat_set_1 first specific gene set from gene pairs
-#' @param feat_set_2 second specific gene set from gene pairs
+#' @param feat_set_1 first specific feature set from feature pairs
+#' @param feat_set_2 second specific feature set from feature pairs
 #' @param gene_set_1 deprecated, use feat_set_1
 #' @param gene_set_2 deprecated, use feat_set_2
 #' @param log2FC_addendum addendum to add when calculating log2FC
@@ -2615,50 +2327,73 @@ exprCellCellcom <- function(gobject,
 #' (random variance and z-score)
 #' @param adjust_method which method to adjust p-values
 #' @param adjust_target adjust multiple hypotheses at the cell or feature level
+#' @param do_parallel run calculations in parallel with mclapply
+#' @param cores number of cores to use if do_parallel = TRUE
 #' @param set_seed set a seed for reproducibility
 #' @param seed_number seed number
 #' @param verbose verbose
-#' @returns Cell-Cell communication scores for feature pairs based on spatial 
+#' @returns Cell-Cell communication scores for feature pairs based on spatial
 #' interaction
-#' @details Statistical framework to identify if pairs of features
+#' @details Statistical framework to identify if pairs of genes
 #' (such as ligand-receptor combinations)
 #' are expressed at higher levels than expected based on a reshuffled null
 #' distribution of feature expression values in cells that are spatially in
 #' proximity to each other.
-#' \itemize{
-#'  \item{LR_comb:}{Pair of ligand and receptor}
-#'  \item{lig_cell_type:}{ cell type to assess expression level of ligand }
-#'  \item{lig_expr:}{ average expression of ligand in lig_cell_type }
-#'  \item{ligand:}{ ligand name }
-#'  \item{rec_cell_type:}{ cell type to assess expression level of receptor }
-#'  \item{rec_expr:}{ average expression of receptor in rec_cell_type}
-#'  \item{receptor:}{ receptor name }
-#'  \item{LR_expr:}{ combined average ligand and receptor expression }
-#'  \item{lig_nr:}{ total number of cells from lig_cell_type that spatially interact with cells from rec_cell_type }
-#'  \item{rec_nr:}{ total number of cells from rec_cell_type that spatially interact with cells from lig_cell_type }
-#'  \item{rand_expr:}{ average combined ligand and receptor expression from random spatial permutations }
-#'  \item{av_diff:}{ average difference between LR_expr and rand_expr over all random spatial permutations }
-#'  \item{sd_diff:}{ (optional) standard deviation of the difference between LR_expr and rand_expr over all random spatial permutations }
-#'  \item{z_score:}{ (optinal) z-score }
-#'  \item{log2fc:}{ log2 fold-change (LR_expr/rand_expr) }
-#'  \item{pvalue:}{ p-value }
-#'  \item{LR_cell_comb:}{ cell type pair combination }
-#'  \item{p.adj:}{ adjusted p-value }
-#'  \item{PI:}{ significanc score: log2fc * -log10(p.adj) }
-#' }
+#' * **LR_comb:** Pair of ligand and receptor
+#' * **lig_cell_type:** cell type to assess expression level of ligand
+#' * **lig_expr:** average expression of ligand in lig_cell_type
+#' * **ligand:** ligand name
+#' * **rec_cell_type:** cell type to assess expression level of receptor
+#' * **rec_expr:** average expression of receptor in rec_cell_type
+#' * **receptor:** receptor name
+#' * **LR_expr:** combined average ligand and receptor expression
+#' * **lig_nr:** total number of cells from lig_cell_type that spatially
+#' interact with cells from rec_cell_type
+#' * **rec_nr:** total number of cells from rec_cell_type that spatially
+#' interact with cells from lig_cell_type
+#' * **rand_expr:** average combined ligand and receptor expression from
+#' random spatial permutations
+#' * **av_diff:** average difference between LR_expr and rand_expr over all
+#' random spatial permutations
+#' * **sd_diff:** (optional) standard deviation of the difference between
+#' LR_expr and rand_expr over all random spatial permutations
+#' * **z_score:** (optional) z-score
+#' * **log2fc:** log2 fold-change (LR_expr/rand_expr)
+#' * **pvalue:** p-value
+#' * **LR_cell_comb:** cell type pair combination
+#' * **p.adj:** adjusted p-value
+#' * **PI:** significance score: \eqn{log2fc * -log10(p.adj)}
+#'
+#' @md
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' specificCellCellcommunicationScores(g, cluster_column = "leiden_clus")
+#'
+#' res1 <- spatCellCellcom(
+#'     gobject = g,
+#'     cluster_column = "leiden_clus",
+#'     feat_set_1 = "Gm19935",
+#'     feat_set_2 = "9630013A20Rik",
+#'     verbose = "a lot",
+#'     random_iter = 10
+#' )
+#' force(res1)
+#'
+#' res2 <- specificCellCellcommunicationScores(g,
+#'     cluster_column = "leiden_clus",
+#'     cell_type_1 = 1,
+#'     cell_type_2 = 2,
+#'     feat_set_1 = "Gm19935",
+#'     feat_set_2 = "9630013A20Rik"
+#' )
+#'
+#' force(res2)
 #' @export
-specificCellCellcommunicationScores <- function(gobject,
+spatCellCellcom <- function(gobject,
     feat_type = NULL,
     spat_unit = NULL,
     spatial_network_name = "Delaunay_network",
-    cluster_column = "cell_types",
-    random_iter = 100,
-    cell_type_1 = "astrocyte",
-    cell_type_2 = "endothelial",
+    cluster_column = NULL,
+    random_iter = 1000,
     feat_set_1,
     feat_set_2,
     gene_set_1 = NULL,
@@ -2671,9 +2406,13 @@ specificCellCellcommunicationScores <- function(gobject,
         "BY", "none"
     ),
     adjust_target = c("feats", "cells"),
-    set_seed = FALSE,
+    do_parallel = TRUE,
+    cores = NA,
+    set_seed = TRUE,
     seed_number = 1234,
-    verbose = TRUE) {
+    verbose = c("a little", "a lot", "none")) {
+    verbose <- match.arg(verbose, choices = c("a little", "a lot", "none"))
+
     # Set feat_type and spat_unit
     spat_unit <- set_default_spat_unit(
         gobject = gobject,
@@ -2685,6 +2424,19 @@ specificCellCellcommunicationScores <- function(gobject,
         feat_type = feat_type
     )
 
+    ## check if spatial network exists ##
+    spat_networks <- list_spatial_networks_names(gobject,
+        spat_unit = spat_unit
+    )
+
+    if (!spatial_network_name %in% spat_networks) {
+        stop(
+            spatial_network_name, " is not an existing spatial network \n",
+            "use showNetworks() to see the available networks \n",
+            "or create a new spatial network with createSpatialNetwork()"
+        )
+    }
+
     ## deprecated arguments
     if (!is.null(gene_set_1)) {
         feat_set_1 <- gene_set_1
@@ -2695,32 +2447,198 @@ specificCellCellcommunicationScores <- function(gobject,
         warning("gene_set_2 is deprecated, use feat_set_2 in the future")
     }
 
+    if (is.null(cluster_column)) {
+        stop("Name of column in cell metadata with cell type info is needed")
+    }
 
-    # data.table variables
-    from_to <- cell_ID <- lig_cell_type <- rec_cell_type <- lig_nr <-
-        rec_nr <- rand_expr <- NULL
-    av_diff <- log2fc <- LR_expr <- pvalue <- LR_cell_comb <- p.adj <-
-        LR_comb <- PI <- NULL
-    sd_diff <- z_score <- NULL
-
-    # get parameters
-    adjust_method <- match.arg(adjust_method, choices = c(
-        "fdr", "bonferroni", "BH", "holm", "hochberg", "hommel",
-        "BY", "none"
-    ))
-    adjust_target <- match.arg(adjust_target, choices = c("feats", "cells"))
 
-    # metadata
-    cell_metadata <- pDataDT(
-        gobject = gobject,
+    cell_metadata <- pDataDT(gobject,
         feat_type = feat_type,
         spat_unit = spat_unit
     )
 
-    # get annotated spatial network
-    annot_network <- annotateSpatialNetwork(gobject,
-        feat_type = feat_type,
-        spat_unit = spat_unit,
+    ## get all combinations between cell types
+    all_uniq_values <- unique(cell_metadata[[cluster_column]])
+    same_DT <- data.table::data.table(
+        V1 = all_uniq_values, V2 = all_uniq_values)
+    combn_DT <- data.table::as.data.table(t(combn(all_uniq_values, m = 2)))
+    combn_DT <- rbind(same_DT, combn_DT)
+
+    ## parallel option ##
+    if (isTRUE(do_parallel)) {
+        savelist <- lapply_flex(
+            X = seq_len(nrow(combn_DT)), future.seed = TRUE,
+            cores = cores, fun = function(row) {
+            cell_type_1 <- combn_DT[row][["V1"]]
+            cell_type_2 <- combn_DT[row][["V2"]]
+
+            specific_scores <- specificCellCellcommunicationScores(
+                gobject = gobject,
+                feat_type = feat_type,
+                spat_unit = spat_unit,
+                cluster_column = cluster_column,
+                random_iter = random_iter,
+                cell_type_1 = cell_type_1,
+                cell_type_2 = cell_type_2,
+                feat_set_1 = feat_set_1,
+                feat_set_2 = feat_set_2,
+                spatial_network_name = spatial_network_name,
+                log2FC_addendum = log2FC_addendum,
+                min_observations = min_observations,
+                detailed = detailed,
+                adjust_method = adjust_method,
+                adjust_target = adjust_target,
+                set_seed = set_seed,
+                seed_number = seed_number,
+                verbose = verbose %in% c("a lot")
+            )
+        })
+    } else {
+        ## for loop over all combinations ##
+        savelist <- list()
+        countdown <- nrow(combn_DT)
+
+        for (row in seq_len(nrow(combn_DT))) {
+            cell_type_1 <- combn_DT[row][["V1"]]
+            cell_type_2 <- combn_DT[row][["V2"]]
+
+            if (verbose == "a little" || verbose == "a lot")
+                cat(sprintf("[PROCESS nr  %d :  %d  and  %d]  ",
+                            countdown, cell_type_1, cell_type_2))
+
+            if (verbose %in% c("a little", "none")) {
+                specific_verbose <- FALSE
+            } else {
+                specific_verbose <- TRUE
+            }
+
+            specific_scores <- specificCellCellcommunicationScores(
+                gobject = gobject,
+                feat_type = feat_type,
+                spat_unit = spat_unit,
+                cluster_column = cluster_column,
+                random_iter = random_iter,
+                cell_type_1 = cell_type_1,
+                cell_type_2 = cell_type_2,
+                feat_set_1 = feat_set_1,
+                feat_set_2 = feat_set_2,
+                spatial_network_name = spatial_network_name,
+                log2FC_addendum = log2FC_addendum,
+                min_observations = min_observations,
+                detailed = detailed,
+                adjust_method = adjust_method,
+                adjust_target = adjust_target,
+                set_seed = set_seed,
+                seed_number = seed_number,
+                verbose = specific_verbose
+            )
+            savelist[[row]] <- specific_scores
+            countdown <- countdown - 1
+        }
+    }
+
+    finalDT <- do.call("rbind", savelist)
+
+    # data.table variables
+    LR_comb <- LR_expr <- NULL
+
+    data.table::setorder(finalDT, LR_comb, -LR_expr)
+
+    return(finalDT)
+}
+
+
+
+
+
+#' @rdname spatCellCellcom
+#' @param cell_type_1 character. First cell type
+#' @param cell_type_2 character. Second cell type
+#' @export
+specificCellCellcommunicationScores <- function(
+        gobject,
+        feat_type = NULL,
+        spat_unit = NULL,
+        spatial_network_name = "Delaunay_network",
+        cluster_column = NULL,
+        random_iter = 100,
+        cell_type_1 = NULL,
+        cell_type_2 = NULL,
+        feat_set_1,
+        feat_set_2,
+        gene_set_1 = NULL,
+        gene_set_2 = NULL,
+        log2FC_addendum = 0.1,
+        min_observations = 2,
+        detailed = FALSE,
+        adjust_method = c(
+            "fdr", "bonferroni", "BH", "holm", "hochberg", "hommel",
+            "BY", "none"
+        ),
+        adjust_target = c("feats", "cells"),
+        set_seed = FALSE,
+        seed_number = 1234,
+        verbose = TRUE
+) {
+
+    # Set feat_type and spat_unit
+    spat_unit <- set_default_spat_unit(
+        gobject = gobject,
+        spat_unit = spat_unit
+    )
+    feat_type <- set_default_feat_type(
+        gobject = gobject,
+        spat_unit = spat_unit,
+        feat_type = feat_type
+    )
+
+    ## deprecated arguments
+    if (!is.null(gene_set_1)) {
+        feat_set_1 <- gene_set_1
+        warning("gene_set_1 is deprecated, use feat_set_1 in the future")
+    }
+    if (!is.null(gene_set_2)) {
+        feat_set_2 <- gene_set_2
+        warning("gene_set_2 is deprecated, use feat_set_2 in the future")
+    }
+
+    if (is.null(cluster_column)) {
+        stop("Name of column in cell metadata with cell type info is needed")
+    }
+
+    if (is.null(cell_type_1) || is.null(cell_type_2)) {
+        stop(sprintf(
+            "`%s` and `%s` in `%s` must be given",
+            "cell_type_1", "cell_type_2", "cluster_column")
+        )
+    }
+
+
+    # data.table variables
+    from_to <- cell_ID <- lig_cell_type <- rec_cell_type <- lig_nr <-
+        rec_nr <- rand_expr <- NULL
+    av_diff <- log2fc <- LR_expr <- pvalue <- LR_cell_comb <- p.adj <-
+        LR_comb <- PI <- NULL
+    sd_diff <- z_score <- NULL
+
+    # get parameters
+    adjust_method <- match.arg(adjust_method, choices = c(
+        "fdr", "bonferroni", "BH", "holm", "hochberg", "hommel",
+        "BY", "none"
+    ))
+    adjust_target <- match.arg(adjust_target, choices = c("feats", "cells"))
+
+    # metadata
+    cell_metadata <- pDataDT(
+        gobject = gobject,
+        feat_type = feat_type,
+        spat_unit = spat_unit
+    )
+
+    # get annotated spatial network
+    annot_network <- annotateSpatialNetwork(gobject,
+        feat_type = feat_type,
+        spat_unit = spat_unit,
         spatial_network_name = spatial_network_name,
         cluster_column = cluster_column
     )
@@ -2747,8 +2665,8 @@ specificCellCellcommunicationScores <- function(gobject,
 
         # get information about number of cells
         temp_meta <- pDataDT(subsetGiotto,
-            feat_type = feat_type,
-            spat_unit = spat_unit
+                             feat_type = feat_type,
+                             spat_unit = spat_unit
         )
         nr_cell_types <- temp_meta[cell_ID %in% subset_ids][
             , .N, by = c(cluster_column)]
@@ -2756,7 +2674,7 @@ specificCellCellcommunicationScores <- function(gobject,
         names(nr_cells) <- nr_cell_types$cell_types
 
         # get average communication scores
-        comScore <- average_feat_feat_expression_in_groups(
+        comScore <- .average_feat_feat_expression_in_groups(
             gobject = subsetGiotto,
             feat_type = feat_type,
             spat_unit = spat_unit,
@@ -2765,8 +2683,8 @@ specificCellCellcommunicationScores <- function(gobject,
             feat_set_2 = feat_set_2
         )
         comScore <- comScore[(lig_cell_type == cell_type_1 &
-                                rec_cell_type == cell_type_2) |
-            (lig_cell_type == cell_type_2 & rec_cell_type == cell_type_1)]
+                                  rec_cell_type == cell_type_2) |
+                                 (lig_cell_type == cell_type_2 & rec_cell_type == cell_type_1)]
 
         comScore[, lig_nr := nr_cells[lig_cell_type]]
         comScore[, rec_nr := nr_cells[rec_cell_type]]
@@ -2786,11 +2704,13 @@ specificCellCellcommunicationScores <- function(gobject,
         subset_metadata <- cell_metadata[cell_ID %in% subset_ids]
         needed_cell_types <- subset_metadata[[cluster_column]]
 
-
+        if (verbose) cat("simulations:")
 
         ## simulations ##
-        for (sim in 1:random_iter) {
-            if (verbose == TRUE) cat("simulation ", sim)
+        for (sim in seq_len(random_iter)) {
+            if (verbose) {
+                cat(sprintf(" %s ", sim))
+            }
 
             # get random ids and subset
             if (set_seed == TRUE) {
@@ -2814,7 +2734,7 @@ specificCellCellcommunicationScores <- function(gobject,
             )
 
             # get random communication scores
-            randomScore <- average_feat_feat_expression_in_groups(
+            randomScore <- .average_feat_feat_expression_in_groups(
                 gobject = tempGiotto,
                 feat_type = feat_type,
                 spat_unit = spat_unit,
@@ -2824,7 +2744,7 @@ specificCellCellcommunicationScores <- function(gobject,
             )
             randomScore <- randomScore[(lig_cell_type == cell_type_1 &
                                             rec_cell_type == cell_type_2) |
-                (lig_cell_type == cell_type_2 & rec_cell_type == cell_type_1)]
+                                           (lig_cell_type == cell_type_2 & rec_cell_type == cell_type_1)]
 
 
 
@@ -2879,267 +2799,63 @@ specificCellCellcommunicationScores <- function(gobject,
 
         # get minimum adjusted p.value that is not zero
         all_p.adj <- comScore[["p.adj"]]
-        lowest_p.adj <- min(all_p.adj[all_p.adj != 0])
+        nonzero_p.adj <- all_p.adj[all_p.adj != 0]
+        if (length(nonzero_p.adj) == 0L) {
+            warning(
+                call. = FALSE,
+                "no adjusted p.values that are not zero; returning Inf"
+            )
+            if (verbose) cat("<- Inf returned")
+            lowest_p.adj <- Inf
+        } else {
+            lowest_p.adj <- min(nonzero_p.adj)
+        }
+
         comScore[, PI := ifelse(p.adj == 0, log2fc * (
             -log10(lowest_p.adj)), log2fc * (-log10(p.adj)))]
 
+        if (verbose) cat("\n")
+
         return(comScore)
     }
 }
 
 
-#' @title spatCellCellcom
-#' @name spatCellCellcom
-#' @description Spatial Cell-Cell communication scores based on spatial
-#' expression of interacting cells
-#' @param gobject giotto object to use
-#' @param feat_type feature type
-#' @param spat_unit spatial unit
-#' @param spatial_network_name spatial network to use for identifying
-#' interacting cells
-#' @param cluster_column cluster column with cell type information
-#' @param random_iter number of iterations
-#' @param feat_set_1 first specific feature set from feature pairs
-#' @param feat_set_2 second specific feature set from feature pairs
-#' @param gene_set_1 deprecated, use feat_set_1
-#' @param gene_set_2 deprecated, use feat_set_2
-#' @param log2FC_addendum addendum to add when calculating log2FC
-#' @param min_observations minimum number of interactions needed to be
-#' considered
-#' @param detailed provide more detailed information
-#' (random variance and z-score)
-#' @param adjust_method which method to adjust p-values
-#' @param adjust_target adjust multiple hypotheses at the cell or feature level
-#' @param do_parallel run calculations in parallel with mclapply
-#' @param cores number of cores to use if do_parallel = TRUE
-#' @param set_seed set a seed for reproducibility
-#' @param seed_number seed number
-#' @param verbose verbose
-#' @returns Cell-Cell communication scores for feature pairs based on spatial
-#' interaction
-#' @details Statistical framework to identify if pairs of genes
-#' (such as ligand-receptor combinations)
-#' are expressed at higher levels than expected based on a reshuffled null
-#' distribution of feature expression values in cells that are spatially in
-#' proximity to each other.
-#' * **LR_comb:** Pair of ligand and receptor
-#' * **lig_cell_type:** cell type to assess expression level of ligand
-#' * **lig_expr:** average expression of ligand in lig_cell_type
-#' * **ligand:** ligand name
-#' * **rec_cell_type:** cell type to assess expression level of receptor
-#' * **rec_expr:** average expression of receptor in rec_cell_type
-#' * **receptor:** receptor name
-#' * **LR_expr:** combined average ligand and receptor expression
-#' * **lig_nr:** total number of cells from lig_cell_type that spatially
-#' interact with cells from rec_cell_type
-#' * **rec_nr:** total number of cells from rec_cell_type that spatially
-#' interact with cells from lig_cell_type
-#' * **rand_expr:** average combined ligand and receptor expression from
-#' random spatial permutations
-#' * **av_diff:** average difference between LR_expr and rand_expr over all
-#' random spatial permutations
-#' * **sd_diff:** (optional) standard deviation of the difference between
-#' LR_expr and rand_expr over all random spatial permutations
-#' * **z_score:** (optional) z-score
-#' * **log2fc:** log2 fold-change (LR_expr/rand_expr)
-#' * **pvalue:** p-value
-#' * **LR_cell_comb:** cell type pair combination
-#' * **p.adj:** adjusted p-value
-#' * **PI:** significance score: log2fc * -log10(p.adj)
-#' @md
+
+
+#' @title Combine cell cell communication tables
+#' @name combCCcom
+#' @description Combine spatial and expression based cell-cell communication
+#' data.tables
+#' @param spatialCC spatial cell-cell communication scores
+#' @param exprCC expression cell-cell communication scores
+#' @param min_lig_nr minimum number of ligand cells
+#' @param min_rec_nr minimum number of receptor cells
+#' @param min_padj_value minimum adjusted p-value
+#' @param min_log2fc minimum log2 fold-change
+#' @param min_av_diff minimum average expression difference
+#' @param detailed detailed option used with \code{\link{spatCellCellcom}}
+#' (default = FALSE)
+#' @returns combined data.table with spatial and expression communication data
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #'
-#' spatCellCellcom(
-#'     gobject = g,
+#' exprCC <- exprCellCellcom(g,
+#'     cluster_column = "leiden_clus",
+#'     feat_set_1 = "Gm19935",
+#'     feat_set_2 = "9630013A20Rik"
+#' )
+#'
+#' spatialCC <- spatCellCellcom(gobject = g,
 #'     cluster_column = "leiden_clus",
 #'     feat_set_1 = "Gm19935",
 #'     feat_set_2 = "9630013A20Rik",
 #'     verbose = "a lot",
 #'     random_iter = 10
 #' )
-#' @export
-spatCellCellcom <- function(gobject,
-    feat_type = NULL,
-    spat_unit = NULL,
-    spatial_network_name = "Delaunay_network",
-    cluster_column = "cell_types",
-    random_iter = 1000,
-    feat_set_1,
-    feat_set_2,
-    gene_set_1 = NULL,
-    gene_set_2 = NULL,
-    log2FC_addendum = 0.1,
-    min_observations = 2,
-    detailed = FALSE,
-    adjust_method = c(
-        "fdr", "bonferroni", "BH", "holm", "hochberg", "hommel",
-        "BY", "none"
-    ),
-    adjust_target = c("feats", "cells"),
-    do_parallel = TRUE,
-    cores = NA,
-    set_seed = TRUE,
-    seed_number = 1234,
-    verbose = c("a little", "a lot", "none")) {
-    verbose <- match.arg(verbose, choices = c("a little", "a lot", "none"))
-
-    # Set feat_type and spat_unit
-    spat_unit <- set_default_spat_unit(
-        gobject = gobject,
-        spat_unit = spat_unit
-    )
-    feat_type <- set_default_feat_type(
-        gobject = gobject,
-        spat_unit = spat_unit,
-        feat_type = feat_type
-    )
-
-    ## check if spatial network exists ##
-    spat_networks <- list_spatial_networks_names(gobject,
-        spat_unit = spat_unit
-    )
-
-    if (!spatial_network_name %in% spat_networks) {
-        stop(
-            spatial_network_name, " is not an existing spatial network \n",
-            "use showNetworks() to see the available networks \n",
-            "or create a new spatial network with createSpatialNetwork()"
-        )
-    }
-
-    ## deprecated arguments
-    if (!is.null(gene_set_1)) {
-        feat_set_1 <- gene_set_1
-        warning("gene_set_1 is deprecated, use feat_set_1 in the future")
-    }
-    if (!is.null(gene_set_2)) {
-        feat_set_2 <- gene_set_2
-        warning("gene_set_2 is deprecated, use feat_set_2 in the future")
-    }
-
-
-    cell_metadata <- pDataDT(gobject,
-        feat_type = feat_type,
-        spat_unit = spat_unit
-    )
-
-    ## get all combinations between cell types
-    all_uniq_values <- unique(cell_metadata[[cluster_column]])
-    same_DT <- data.table::data.table(
-        V1 = all_uniq_values, V2 = all_uniq_values)
-    combn_DT <- data.table::as.data.table(t(combn(all_uniq_values, m = 2)))
-    combn_DT <- rbind(same_DT, combn_DT)
-
-    ## parallel option ##
-    if (do_parallel == TRUE) {
-        savelist <- lapply_flex(
-            X = 1:nrow(combn_DT), future.seed = TRUE,
-            cores = cores, fun = function(row) {
-            cell_type_1 <- combn_DT[row][["V1"]]
-            cell_type_2 <- combn_DT[row][["V2"]]
-
-            specific_scores <- specificCellCellcommunicationScores(
-                gobject = gobject,
-                feat_type = feat_type,
-                spat_unit = spat_unit,
-                cluster_column = cluster_column,
-                random_iter = random_iter,
-                cell_type_1 = cell_type_1,
-                cell_type_2 = cell_type_2,
-                feat_set_1 = feat_set_1,
-                feat_set_2 = feat_set_2,
-                spatial_network_name = spatial_network_name,
-                log2FC_addendum = log2FC_addendum,
-                min_observations = min_observations,
-                detailed = detailed,
-                adjust_method = adjust_method,
-                adjust_target = adjust_target,
-                set_seed = set_seed,
-                seed_number = seed_number
-            )
-        })
-    } else {
-        ## for loop over all combinations ##
-        savelist <- list()
-        countdown <- nrow(combn_DT)
-
-        for (row in 1:nrow(combn_DT)) {
-            cell_type_1 <- combn_DT[row][["V1"]]
-            cell_type_2 <- combn_DT[row][["V2"]]
-
-            if (verbose == "a little" | verbose == "a lot")
-                cat("PROCESS nr ", countdown, ": ", cell_type_1, " and ",
-                    cell_type_2)
-
-            if (verbose %in% c("a little", "none")) {
-                specific_verbose <- FALSE
-            } else {
-                specific_verbose <- TRUE
-            }
-
-            specific_scores <- specificCellCellcommunicationScores(
-                gobject = gobject,
-                feat_type = feat_type,
-                spat_unit = spat_unit,
-                cluster_column = cluster_column,
-                random_iter = random_iter,
-                cell_type_1 = cell_type_1,
-                cell_type_2 = cell_type_2,
-                feat_set_1 = feat_set_1,
-                feat_set_2 = feat_set_2,
-                spatial_network_name = spatial_network_name,
-                log2FC_addendum = log2FC_addendum,
-                min_observations = min_observations,
-                detailed = detailed,
-                adjust_method = adjust_method,
-                adjust_target = adjust_target,
-                set_seed = set_seed,
-                seed_number = seed_number,
-                verbose = specific_verbose
-            )
-            savelist[[row]] <- specific_scores
-            countdown <- countdown - 1
-        }
-    }
-
-    finalDT <- do.call("rbind", savelist)
-
-    # data.table variables
-    LR_comb <- LR_expr <- NULL
-
-    data.table::setorder(finalDT, LR_comb, -LR_expr)
-
-    return(finalDT)
-}
-
-
-
-
-#' @title combCCcom
-#' @name combCCcom
-#' @description Combine spatial and expression based cell-cell communication
-#' data.tables
-#' @param spatialCC spatial cell-cell communication scores
-#' @param exprCC expression cell-cell communication scores
-#' @param min_lig_nr minimum number of ligand cells
-#' @param min_rec_nr minimum number of receptor cells
-#' @param min_padj_value minimum adjusted p-value
-#' @param min_log2fc minimum log2 fold-change
-#' @param min_av_diff minimum average expression difference
-#' @param detailed detailed option used with \code{\link{spatCellCellcom}}
-#' (default = FALSE)
-#' @returns combined data.table with spatial and expression communication data
-#' @examples
-#' g <- GiottoData::loadGiottoMini("visium")
-#'
-#' exprCC <- exprCellCellcom(g, cluster_column = "leiden_clus",
-#' feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik")
-#' spatialCC <- spatCellCellcom(gobject = g, cluster_column = "leiden_clus",
-#' feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot",
-#' random_iter = 10)
-#'
-#' combCCcom(spatialCC = spatialCC, exprCC = exprCC)
+#'
+#' combCC <- combCCcom(spatialCC = spatialCC, exprCC = exprCC)
+#' force(combCC)
 #' @export
 combCCcom <- function(spatialCC,
     exprCC,
@@ -3201,3 +2917,190 @@ combCCcom <- function(spatialCC,
 
     return(merge_DT)
 }
+
+
+
+
+
+
+
+# DEPRECATED ####
+
+#' @title deprecated
+#' @name findInteractionChangedGenes
+#' @description Identifies cell-to-cell Interaction Changed Features (ICF),
+#' i.e. genes that are differentially expressed due to interactions with other
+#' cell types.
+#' @param ... params to pass to \code{findInteractionChangedFeats}
+#' @seealso \code{\link{findInteractionChangedFeats}}
+#' @returns interaction changed genes
+#' @export
+findInteractionChangedGenes <- function(...) {
+    .Deprecated(new = "findInteractionChangedFeats")
+
+    findInteractionChangedFeats(...)
+}
+
+
+
+#' @title deprecated
+#' @name findCellProximityGenes
+#' @description Identifies cell-to-cell Interaction Changed Features (ICF),
+#' i.e. genes that are differentially expressed due to proximity to other cell
+#' types.
+#' @inheritDotParams findInteractionChangedFeats
+#' @seealso \code{\link{findInteractionChangedFeats}}
+#' @returns cell-cell interaction changed genes
+#' @export
+findCellProximityGenes <- function(...) {
+    .Deprecated(new = "findInteractionChangedFeats")
+
+    findInteractionChangedFeats(...)
+}
+
+
+
+#' @title deprecated
+#' @name findICG
+#' @description Identifies cell-to-cell Interaction Changed Features (ICF),
+#' i.e. genes that are differentially expressed due to interaction with other
+#' cell types.
+#' @inheritDotParams findICF
+#' @seealso \code{\link{findICF}}
+#' @returns cell-cell interaction changed features
+#' @export
+findICG <- function(...) {
+    .Deprecated(new = "findICF")
+
+    findICF(...)
+}
+
+
+
+#' @title deprecated
+#' @name findCPG
+#' @description Identifies cell-to-cell Interaction Changed Features (ICF),
+#' i.e. genes that are differentially expressed due to proximity to other cell
+#' types.
+#' @inheritDotParams findICF
+#' @returns cell-to-cell Interaction Changed Genes
+#' @seealso \code{\link{findICF}}
+#' @export
+findCPG <- function(...) {
+    .Deprecated(new = "findICF")
+
+    findICF(...)
+}
+
+#' @title deprecated
+#' @name filterInteractionChangedGenes
+#' @description Filter Interaction Changed Feature scores.
+#' @inheritDotParams filterInteractionChangedFeats
+#' @seealso \code{\link{filterInteractionChangedFeats}}
+#' @returns filtered interaction changed feature scores
+#' @export
+filterInteractionChangedGenes <- function(...) {
+    .Deprecated(new = "filterInteractionChangedFeats")
+
+    filterInteractionChangedFeats(...)
+}
+
+
+#' @title deprecated
+#' @name filterCellProximityGenes
+#' @description Filter Interaction Changed Feature scores.
+#' @inheritDotParams filterInteractionChangedFeats
+#' @seealso \code{\link{filterInteractionChangedFeats}}
+#' @returns proximity genes
+#' @export
+filterCellProximityGenes <- function(...) {
+    .Deprecated(new = "filterInteractionChangedFeats")
+
+    filterInteractionChangedFeats(...)
+}
+
+
+
+
+
+#' @title deprecated
+#' @name filterICG
+#' @description Filter Interaction Changed Gene scores.
+#' @inheritDotParams filterICF
+#' @seealso \code{\link{filterICF}}
+#' @returns filtered interaction changed gene scores
+#' @export
+filterICG <- function(...) {
+    .Deprecated(new = "filterICF")
+
+    filterICF(...)
+}
+
+
+
+#' @title deprecated
+#' @name filterCPG
+#' @description Filter Interaction Changed Gene scores.
+#' @inheritDotParams filterICF
+#' @seealso \code{\link{filterICF}}
+#' @returns filtered interaction changed gene scores
+#' @export
+filterCPG <- function(...) {
+    .Deprecated(new = "filterICF")
+
+    filterICF(...)
+}
+
+
+#' @title deprecated
+#' @name combineInteractionChangedGenes
+#' @description Combine ICF scores in a pairwise manner.
+#' @inheritDotParams combineInteractionChangedFeats
+#' @returns ICF scores
+#' @seealso \code{\link{combineInteractionChangedFeats}}
+#' @export
+combineInteractionChangedGenes <- function(...) {
+    .Deprecated(new = "combineInteractionChangedFeats")
+
+    combineInteractionChangedFeats(...)
+}
+
+
+#' @title deprecated
+#' @name combineCellProximityGenes
+#' @description Combine ICF scores in a pairwise manner.
+#' @inheritDotParams combineInteractionChangedFeats
+#' @returns ICF scores
+#' @seealso \code{\link{combineInteractionChangedFeats}}
+#' @export
+combineCellProximityGenes <- function(...) {
+    .Deprecated(new = "combineInteractionChangedFeats")
+
+    combineInteractionChangedFeats(...)
+}
+
+#' @title deprecated
+#' @name combineICG
+#' @description Combine ICF scores in a pairwise manner.
+#' @inheritDotParams combineICF
+#' @returns ICF scores
+#' @seealso \code{\link{combineICF}}
+#' @export
+combineICG <- function(...) {
+    .Deprecated(new = "combineICF")
+
+    combineICF(...)
+}
+
+#' @title deprecated
+#' @name combineCPG
+#' @description Combine ICF scores in a pairwise manner.
+#' @inheritDotParams combineICF
+#' @returns ICF scores
+#' @seealso \code{\link{combineICF}}
+#' @export
+combineCPG <- function(...) {
+    .Deprecated(new = "combineICF")
+
+    combineICF(...)
+}
diff --git a/R/spatial_interaction_spot.R b/R/spatial_interaction_spot.R
index 8360f8da0..ab8ed82d4 100644
--- a/R/spatial_interaction_spot.R
+++ b/R/spatial_interaction_spot.R
@@ -8,7 +8,7 @@
 #' @name cell_proximity_spots
 #' @description Compute cell-cell interactions observed value for internal and
 #' external spots
-#' @param dwls_values data.table of cell type enrichment in each spot and 
+#' @param dwls_values data.table of cell type enrichment in each spot and
 #' multiply by cell number in each spot
 #' @returns List of cell proximity observed value in data.table format. Columns:
 #' unified_int, type_int, V1, external, internal.
@@ -16,12 +16,13 @@ NULL
 
 
 
-#' @describeIn cell_proximity_spots Compute cell-cell interactions observed 
+#' @describeIn cell_proximity_spots Compute cell-cell interactions observed
 #' value inner each spot
 #' @param cell_IDs cell_IDs
 #' @keywords internal
-.cell_proximity_spots_internal <- function(cell_IDs,
-    dwls_values) {
+.cell_proximity_spots_internal <- function(
+        cell_IDs,
+        dwls_values) {
     # data.table variables
     value <- unified_int <- Var1 <- Var2 <- internal <- NULL
 
@@ -41,7 +42,8 @@ NULL
             unified_int_same <- names(same_ct)
             unified_int_same <- paste0(unified_int_same, "--", unified_int_same)
             same_ct <- data.table::data.table(
-                "unified_int" = unified_int_same, "internal" = same_ct)
+                "unified_int" = unified_int_same, "internal" = same_ct
+            )
         }
 
         # calculate proximity of different cell type (A==B)
@@ -55,13 +57,17 @@ NULL
             diff_ct <- data.table::as.data.table(reshape2::melt(diff_ct))
             diff_ct <- diff_ct[value != "NA"]
             diff_ct[, c("Var1", "Var2") := lapply(
-                .SD, as.character), .SDcols = c("Var1", "Var2")]
+                .SD, as.character
+            ), .SDcols = c("Var1", "Var2")]
             diff_ct[, unified_int := ifelse(
-                Var1 < Var2, paste0(Var1, "--", Var2), 
-                paste0(Var2, "--", Var1))]
+                Var1 < Var2, paste0(Var1, "--", Var2),
+                paste0(Var2, "--", Var1)
+            )]
             diff_ct <- diff_ct[, c("unified_int", "value")]
             data.table::setnames(
-                diff_ct, old = c("value"), new = c("internal"))
+                diff_ct,
+                old = c("value"), new = c("internal")
+            )
         }
 
         # merge spot proximity to proximity data.table
@@ -76,25 +82,27 @@ NULL
 }
 
 
-#' @describeIn cell_proximity_spots Compute cell-cell interactions observed 
+#' @describeIn cell_proximity_spots Compute cell-cell interactions observed
 #' value for interacted spots
 #' @param pairs data.table of paired spots. Format: cell_ID1, cell_ID2, N
 #' @keywords internal
-.cell_proximity_spots_external <- function(
-        pairs,
-        dwls_values) {
+.cell_proximity_spots_external <- function(pairs,
+    dwls_values) {
     cell_IDs <- unique(c(pairs$from, pairs$to))
     pairs <- pairs[, .N, by = c("from", "to")]
     # add internal pairs to make full matrix
     pairs_spots <- data.table::data.table(from = cell_IDs, to = cell_IDs, N = 0)
     pairs_balance <- data.table::data.table(
-        from = pairs$to, to = pairs$from, N = pairs$N)
+        from = pairs$to, to = pairs$from, N = pairs$N
+    )
     pairs_for_mat <- rbind(pairs_spots, pairs, pairs_balance)
     pairs_for_mat <- pairs_for_mat[, .N, by = c("from", "to")]
 
     # make square matrix of interaction between spots
     pairs_mat <- reshape2::acast(
-        pairs_for_mat, from ~ to, value.var = "N", fill = 0)
+        pairs_for_mat, from ~ to,
+        value.var = "N", fill = 0
+    )
     pairs_mat <- pairs_mat[cell_IDs, cell_IDs]
 
     # calculate cell-type/cell-type interactions
@@ -133,9 +141,10 @@ NULL
 #' @param pairs_external data.table of paired spots. Format: cell_ID1, cell_ID2,
 #' N. Passes to `.cell_proximity_spots_external` `pairs` param
 #' @keywords internal
-.cell_proximity_spots <- function(cell_IDs,
-    pairs_external,
-    dwls_values) {
+.cell_proximity_spots <- function(
+        cell_IDs,
+        pairs_external,
+        dwls_values) {
     # data.table variables
     V1 <- internal <- external <- s1 <- s2 <- unified_int <- type_int <- NULL
 
@@ -156,7 +165,9 @@ NULL
 
     if (length(cell_IDs) > 0) {
         proximity_dt <- merge(
-            proximity_ex, proximity_in, by = "unified_int", all = TRUE)
+            proximity_ex, proximity_in,
+            by = "unified_int", all = TRUE
+        )
     } else {
         proximity_dt <- proximity_ex[, "internal" := 0]
     }
@@ -164,19 +175,22 @@ NULL
     proximity_dt[, V1 := internal + external]
 
     proximity_dt[, s1 := strsplit(as.character(
-        unified_int), split = "--")[[1]][1], by = 1:nrow(proximity_dt)]
+        unified_int
+    ), split = "--")[[1]][1], by = seq_len(nrow(proximity_dt))]
     proximity_dt[, s2 := strsplit(as.character(
-        unified_int), split = "--")[[1]][2], by = 1:nrow(proximity_dt)]
+        unified_int
+    ), split = "--")[[1]][2], by = seq_len(nrow(proximity_dt))]
     proximity_dt[, type_int := ifelse(s1 == s2, "homo", "hetero")]
     proximity_dt <- proximity_dt[
-        , c("unified_int", "type_int", "V1", "external", "internal")]
+        , c("unified_int", "type_int", "V1", "external", "internal")
+    ]
     return(proximity_dt)
 }
 
 
 #' @title cellProximityEnrichmentSpots
 #' @name cellProximityEnrichmentSpots
-#' @description Compute cell-cell interaction enrichment for spots 
+#' @description Compute cell-cell interaction enrichment for spots
 #' (observed vs expected)
 #'
 #' @param gobject giotto object
@@ -185,55 +199,58 @@ NULL
 #' @param spatial_network_name name of spatial network to use
 #' @param cluster_column name of column to use for clusters
 #' @param cells_in_spot cell number in each spot
-#' @param number_of_simulations number of simulations to create expected 
+#' @param number_of_simulations number of simulations to create expected
 #' observations
-#' @param adjust_method method to adjust p.values 
+#' @param adjust_method method to adjust p.values
 #' (e.g. "none", "fdr", "bonferroni","BH","holm", "hochberg", "hommel","BY")
 #' @param set_seed use of seed. Default = TRUE
 #' @param seed_number seed number to use. Default = 1234
 #' @param verbose be verbose
 #'
-#' @returns List of cell Proximity scores (CPscores) in data.table format. 
+#' @returns List of cell Proximity scores (CPscores) in data.table format.
 #' The first
-#' data.table (raw_sim_table) shows the raw observations of both the original 
-#' and simulated networks. The second data.table (enrichm_res) shows the 
+#' data.table (raw_sim_table) shows the raw observations of both the original
+#' and simulated networks. The second data.table (enrichm_res) shows the
 #' enrichment results.
-#' @details Spatial proximity enrichment or depletion between pairs of cell 
+#' @details Spatial proximity enrichment or depletion between pairs of cell
 #' types is calculated by calculating the observed over the expected frequency
-#' of cell-cell proximity interactions. The expected frequency is the average 
-#' frequency calculated from a number of spatial network simulations. Each 
-#' individual simulation is obtained by reshuffling the cell type labels of 
+#' of cell-cell proximity interactions. The expected frequency is the average
+#' frequency calculated from a number of spatial network simulations. Each
+#' individual simulation is obtained by reshuffling the cell type labels of
 #' each node (spot) in the spatial network.
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' x <- findMarkers_one_vs_all(g, 
-#' cluster_column = "leiden_clus", min_feats = 20)
+#' x <- findMarkers_one_vs_all(g,
+#'     cluster_column = "leiden_clus", min_feats = 20
+#' )
 #' sign_gene <- x$feats
-#' 
-#' sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10), 
-#' nrow = length(sign_gene))
+#'
+#' sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+#'     nrow = length(sign_gene)
+#' )
 #' rownames(sign_matrix) <- sign_gene
-#' colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
-#' 
+#' colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
+#'
 #' g <- runDWLSDeconv(gobject = g, sign_matrix = sign_matrix)
-#' 
+#'
 #' cellProximityEnrichmentSpots(gobject = g)
 #' @export
-cellProximityEnrichmentSpots <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    spatial_network_name = "spatial_network",
-    cluster_column = "cell_ID",
-    cells_in_spot = 1,
-    number_of_simulations = 100,
-    adjust_method = c(
-        "none", "fdr", "bonferroni", "BH",
-        "holm", "hochberg", "hommel",
-        "BY"
-    ),
-    set_seed = TRUE,
-    seed_number = 1234,
-    verbose = FALSE) {
+cellProximityEnrichmentSpots <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        spatial_network_name = "spatial_network",
+        cluster_column = "cell_ID",
+        cells_in_spot = 1,
+        number_of_simulations = 100,
+        adjust_method = c(
+            "none", "fdr", "bonferroni", "BH",
+            "holm", "hochberg", "hommel",
+            "BY"
+        ),
+        set_seed = TRUE,
+        seed_number = 1234,
+        verbose = FALSE) {
     # p.adj test
     sel_adjust_method <- match.arg(adjust_method, choices = c(
         "none", "fdr", "bonferroni", "BH",
@@ -250,11 +267,12 @@ cellProximityEnrichmentSpots <- function(gobject,
     )
 
     # data.table variables
-    orig <- from <- to <- unified_int <- unified_cells <- type_int <- N <- 
+    orig <- from <- to <- unified_int <- unified_cells <- type_int <- N <-
         V1 <- original <- enrichm <- simulations <- NULL
 
     spatial_network_annot <- dt_sort_combine_two_columns(
-        spatial_network_annot, "to", "from", "unified_cells")
+        spatial_network_annot, "to", "from", "unified_cells"
+    )
     spatial_network_annot <- spatial_network_annot[!duplicated(unified_cells)]
 
     # exact spatial_enrichment matrix
@@ -300,7 +318,7 @@ cellProximityEnrichmentSpots <- function(gobject,
     # method for get simulation cell-type/cell-type interaction for each round
     data.table::setnames(sample_dt, old = c("s1", "s2"), new = c("from", "to"))
     table_sim_results <- NULL
-    for (sim in 1:number_of_simulations) {
+    for (sim in seq_len(number_of_simulations)) {
         r <- paste0("sim", sim)
         sim_pairs <- sample_dt[round == r, c("from", "to")]
 
@@ -325,21 +343,26 @@ cellProximityEnrichmentSpots <- function(gobject,
     # add missing combinations from original or simulations
     # probably not needed anymore
     all_sim_ints <- as.character(unique(table_results[
-        orig == "simulations"]$unified_int))
+        orig == "simulations"
+    ]$unified_int))
     all_orig_ints <- as.character(unique(table_results[
-        orig == "original"]$unified_int))
+        orig == "original"
+    ]$unified_int))
     missing_in_orig <- all_sim_ints[!all_sim_ints %in% all_orig_ints]
     missing_in_sim <- all_orig_ints[!all_orig_ints %in% all_sim_ints]
     create_missing_for_orig <- table_results[unified_int %in% missing_in_orig]
     create_missing_for_orig <- unique(create_missing_for_orig[
-        , c("orig", "V1") := list("original", 0)])
+        , c("orig", "V1") := list("original", 0)
+    ])
     create_missing_for_sim <- table_results[unified_int %in% missing_in_sim]
     create_missing_for_sim <- unique(create_missing_for_sim[
-        , c("orig", "V1") := list("simulations", 0)])
+        , c("orig", "V1") := list("simulations", 0)
+    ])
 
     table_results <- do.call(
-        "rbind", 
-        list(table_results, create_missing_for_orig, create_missing_for_sim))
+        "rbind",
+        list(table_results, create_missing_for_orig, create_missing_for_sim)
+    )
 
     ## p-values
     if (verbose) message("3/5 Calculating p-values")
@@ -358,40 +381,46 @@ cellProximityEnrichmentSpots <- function(gobject,
 
         length_simulations <- length(sim_values)
         if (length_simulations != number_of_simulations) {
-            additional_length_needed <- number_of_simulations - 
+            additional_length_needed <- number_of_simulations -
                 length_simulations
             sim_values <- c(sim_values, rep(0, additional_length_needed))
         }
 
-        p_orig_higher <- 1 - (sum((orig_value + 1) > (sim_values + 1)) / 
-                                number_of_simulations)
-        p_orig_lower <- 1 - (sum((orig_value + 1) < (sim_values + 1)) / 
-                                number_of_simulations)
+        p_orig_higher <- 1 - (sum((orig_value + 1) > (sim_values + 1)) /
+            number_of_simulations)
+        p_orig_lower <- 1 - (sum((orig_value + 1) < (sim_values + 1)) /
+            number_of_simulations)
 
         combo_list[[int_combo]] <- this_combo
         p_high[[int_combo]] <- p_orig_higher
         p_low[[int_combo]] <- p_orig_lower
     }
     res_pvalue_DT <- data.table::data.table(
-        unified_int = as.vector(combo_list), 
-        p_higher_orig = p_high, 
-        p_lower_orig = p_low)
+        unified_int = as.vector(combo_list),
+        p_higher_orig = p_high,
+        p_lower_orig = p_low
+    )
 
 
     # depletion or enrichment in barplot format
     if (verbose) message("4/5 Depletion or enrichment in barplot format")
 
     table_mean_results <- table_results[
-        , .(mean(V1)), by = c("orig", "unified_int", "type_int")]
+        , .(mean(V1)),
+        by = c("orig", "unified_int", "type_int")
+    ]
     table_mean_results_dc <- data.table::dcast.data.table(
-        data = table_mean_results, 
-        formula = type_int + unified_int ~ orig, value.var = "V1")
+        data = table_mean_results,
+        formula = type_int + unified_int ~ orig, value.var = "V1"
+    )
     table_mean_results_dc[, original := ifelse(is.na(original), 0, original)]
     table_mean_results_dc[, enrichm := log2((original + 1) / (simulations + 1))]
 
 
     table_mean_results_dc <- merge(
-        table_mean_results_dc, res_pvalue_DT, by = "unified_int")
+        table_mean_results_dc, res_pvalue_DT,
+        by = "unified_int"
+    )
     data.table::setorder(table_mean_results_dc, enrichm)
     table_mean_results_dc[, unified_int := factor(unified_int, unified_int)]
 
@@ -400,13 +429,17 @@ cellProximityEnrichmentSpots <- function(gobject,
     if (verbose) message("5/5 Calculating adjust p-values for mht")
 
     # data.table variables
-    p.adj_higher <- p.adj_lower <- p_lower_orig <- p_higher_orig <- 
+    p.adj_higher <- p.adj_lower <- p_lower_orig <- p_higher_orig <-
         PI_value <- int_ranking <- NULL
 
     table_mean_results_dc[, p.adj_higher := stats::p.adjust(
-        p_higher_orig, method = sel_adjust_method)]
+        p_higher_orig,
+        method = sel_adjust_method
+    )]
     table_mean_results_dc[, p.adj_lower := stats::p.adjust(
-        p_lower_orig, method = sel_adjust_method)]
+        p_lower_orig,
+        method = sel_adjust_method
+    )]
 
 
     table_mean_results_dc[, PI_value := ifelse(p.adj_higher <= p.adj_lower,
@@ -417,10 +450,11 @@ cellProximityEnrichmentSpots <- function(gobject,
 
     # order
     table_mean_results_dc <- table_mean_results_dc[order(-PI_value)]
-    table_mean_results_dc[, int_ranking := 1:.N]
+    table_mean_results_dc[, int_ranking := seq_len(.N)]
 
     return(list(
-        raw_sim_table = table_results, enrichm_res = table_mean_results_dc))
+        raw_sim_table = table_results, enrichm_res = table_mean_results_dc
+    ))
 }
 
 
@@ -432,7 +466,7 @@ cellProximityEnrichmentSpots <- function(gobject,
 
 #' @title featExpDWLS
 #' @name featExpDWLS
-#' @description Compute predicted feature expression value by spatialDWSL 
+#' @description Compute predicted feature expression value by spatialDWSL
 #' results and average feature expression for cell type
 #'
 #' @param gobject giotto object
@@ -442,10 +476,11 @@ cellProximityEnrichmentSpots <- function(gobject,
 #'
 #' @returns matrix
 #' @export
-featExpDWLS <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    ave_celltype_exp) {
+featExpDWLS <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        ave_celltype_exp) {
     # exact spatial_enrichment matrix
     dwls_values <- getSpatialEnrichment(gobject,
         spat_unit = spat_unit,
@@ -456,7 +491,7 @@ featExpDWLS <- function(gobject,
 
     # 1. check if cell_type_vector and matrix are compatible
     if (ncol(ave_celltype_exp) != ncol(dwls_values) - 1) {
-        stop("ncol(ave_celltype_exp) needs to be the same as 
+        stop("ncol(ave_celltype_exp) needs to be the same as
             ncol(dwls_values) - 1")
     }
 
@@ -472,7 +507,7 @@ featExpDWLS <- function(gobject,
     )
 
     average_exp <- as.matrix(ave_celltype_exp)
-    for (spot_i in 1:nrow(dwls_values)) {
+    for (spot_i in seq_len(nrow(dwls_values))) {
         spot <- dwls_values[spot_i, 1]
         spot_dwls <- dwls_values[spot_i, -1]
         data.table::setDF(spot_dwls)
@@ -491,28 +526,32 @@ featExpDWLS <- function(gobject,
 
 #' @title Calculate feature expression residual
 #' @name .cal_expr_residual
-#' @description Calculate feature expression residual 
+#' @description Calculate feature expression residual
 #' (observed_exp - DWLS_predicted)
 #'
 #' @param gobject giotto object
 #' @param spat_unit spatial unit (e.g. 'cell')
 #' @param feat_type feature type (e.g. 'rna')
-#' @param expression_values expression values to use 
+#' @param expression_values expression values to use
 #' (e.g. 'normalized', 'scaled', 'custom')
 #' @param ave_celltype_exp average expression matrix in cell types
 #' @returns matrix
 #' @keywords internal
-.cal_expr_residual <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    ave_celltype_exp) {
+.cal_expr_residual <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        ave_celltype_exp) {
     # expression data
     values <- match.arg(
-        expression_values, choices = c("normalized", "scaled", "custom"))
-    
+        expression_values,
+        choices = c("normalized", "scaled", "custom")
+    )
+
     expr_observed <- slot(gobject@expression[[spat_unit]][[
-        feat_type]][[values]], "exprMat")
+        feat_type
+    ]][[values]], "exprMat")
 
     # Compute predicted feature expression value
     expr_predicted <- featExpDWLS(
@@ -522,12 +561,14 @@ featExpDWLS <- function(gobject,
         ave_celltype_exp = ave_celltype_exp
     )
 
-    # Get the difference expression matrix between observed and predicted 
+    # Get the difference expression matrix between observed and predicted
     # expression
     intersect_feature <- intersect(
-        rownames(expr_predicted), rownames(expr_observed))
+        rownames(expr_predicted), rownames(expr_observed)
+    )
     expr_residual <- expr_observed[intersect_feature, ] - expr_predicted[
-        intersect_feature, ]
+        intersect_feature,
+    ]
     expr_residual <- as.matrix(expr_residual)
 
     return(expr_residual)
@@ -545,28 +586,31 @@ featExpDWLS <- function(gobject,
 #' @param spatial_network_name name of spatial network to use
 #' @param cluster_column name of column to use for clusters
 #'
-#' @returns matrix that rownames are cell-cell interaction pairs and colnames 
+#' @returns matrix that rownames are cell-cell interaction pairs and colnames
 #' are cell_IDs
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' x <- findMarkers_one_vs_all(g, 
-#' cluster_column = "leiden_clus", min_feats = 20)
+#' x <- findMarkers_one_vs_all(g,
+#'     cluster_column = "leiden_clus", min_feats = 20
+#' )
 #' sign_gene <- x$feats
-#' 
-#' sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10), 
-#' nrow = length(sign_gene))
+#'
+#' sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+#'     nrow = length(sign_gene)
+#' )
 #' rownames(sign_matrix) <- sign_gene
-#' colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
-#' 
+#' colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
+#'
 #' g <- runDWLSDeconv(gobject = g, sign_matrix = sign_matrix)
-#' 
+#'
 #' cellProximityEnrichmentEachSpot(gobject = g)
 #' @export
-cellProximityEnrichmentEachSpot <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    spatial_network_name = "spatial_network",
-    cluster_column = "cell_ID") {
+cellProximityEnrichmentEachSpot <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        spatial_network_name = "spatial_network",
+        cluster_column = "cell_ID") {
     spatial_network_annot <- annotateSpatialNetwork(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -576,11 +620,12 @@ cellProximityEnrichmentEachSpot <- function(gobject,
     )
 
     # data.table variables
-    V1 <- V2 <- from <- to <- int_cell_IDS <- Var1 <- Var2 <- 
+    V1 <- V2 <- from <- to <- int_cell_IDS <- Var1 <- Var2 <-
         unified_cells <- type_int <- N <- NULL
 
     spatial_network_annot <- dt_sort_combine_two_columns(
-        spatial_network_annot, "to", "from", "unified_cells")
+        spatial_network_annot, "to", "from", "unified_cells"
+    )
     spatial_network_annot <- spatial_network_annot[!duplicated(unified_cells)]
 
     # exact spatial_enrichment matrix
@@ -601,8 +646,11 @@ cellProximityEnrichmentEachSpot <- function(gobject,
     # get cell-cell types pairs
     cts <- colnames(dwls_values)
     ct_pairs <- data.table::data.table(
-        V1 = rep(cts, each = length(cts)), V2 = rep(cts, length(cts)))
-    ct_pairs[, unified_int := paste0(V1, "--", V2), by = 1:nrow(ct_pairs)]
+        V1 = rep(cts, each = length(cts)), V2 = rep(cts, length(cts))
+    )
+    ct_pairs[, unified_int := paste0(V1, "--", V2),
+        by = seq_len(nrow(ct_pairs))
+    ]
     unified_int <- ct_pairs$unified_int
 
 
@@ -639,7 +687,7 @@ cellProximityEnrichmentEachSpot <- function(gobject,
             idx2 <- which(colSums(dwls_int_cells) > 0)
             dwls_int_cells <- dwls_int_cells[, idx2]
 
-            # all the interacted cells dwls have same cell type with 
+            # all the interacted cells dwls have same cell type with
             # proportion=1
             if (length(idx2) == 1) {
                 dwls_int_cells <- matrix(dwls_int_cells,
@@ -670,7 +718,8 @@ cellProximityEnrichmentEachSpot <- function(gobject,
         spot_proximity <- reshape2::melt(spot_proximity)
         spot_proximity <- data.table::data.table(spot_proximity)
         spot_proximity[, c("Var1", "Var2") := lapply(
-            .SD, as.character), .SDcols = c("Var1", "Var2")]
+            .SD, as.character
+        ), .SDcols = c("Var1", "Var2")]
         spot_proximity[, unified_int := paste0(Var1, "--", Var2)]
 
         # add to proximityMat(matrix)
@@ -685,12 +734,13 @@ cellProximityEnrichmentEachSpot <- function(gobject,
 #' cell proximity score of selected cell for spots
 #' @returns data.table
 #' @keywords internal
-.cal_diff_per_interaction <- function(sel_int,
-    other_ints,
-    select_ind,
-    other_ind,
-    proximityMat,
-    expr_residual) {
+.cal_diff_per_interaction <- function(
+        sel_int,
+        other_ints,
+        select_ind,
+        other_ind,
+        proximityMat,
+        expr_residual) {
     pcc_diff <- sel <- other <- NULL
 
     # get data
@@ -729,7 +779,9 @@ cellProximityEnrichmentEachSpot <- function(gobject,
     expr_residual_dt[, diff := sel - other]
 
     results_dt <- data.table::merge.data.table(
-        expr_residual_dt, pcc_dt, by = "features")
+        expr_residual_dt, pcc_dt,
+        by = "features"
+    )
 
     return(results_dt)
 }
@@ -746,13 +798,14 @@ NULL
 
 #' @describeIn do_permuttest_spot Calculate original values for spots
 #' @keywords internal
-.do_permuttest_original_spot <- function(sel_int,
-    other_ints,
-    select_ind,
-    other_ind,
-    name = "orig",
-    proximityMat,
-    expr_residual) {
+.do_permuttest_original_spot <- function(
+        sel_int,
+        other_ints,
+        select_ind,
+        other_ind,
+        name = "orig",
+        proximityMat,
+        expr_residual) {
     resultsDT <- .cal_diff_per_interaction(
         sel_int = sel_int,
         other_ints = other_ints,
@@ -768,15 +821,16 @@ NULL
 
 #' @describeIn do_permuttest_spot Calculate random values for spots
 #' @keywords internal
-.do_permuttest_random_spot <- function(sel_int,
-    other_ints,
-    select_ind,
-    other_ind,
-    name = "perm_1",
-    proximityMat,
-    expr_residual,
-    set_seed = TRUE,
-    seed_number = 1234) {
+.do_permuttest_random_spot <- function(
+        sel_int,
+        other_ints,
+        select_ind,
+        other_ind,
+        name = "perm_1",
+        proximityMat,
+        expr_residual,
+        set_seed = TRUE,
+        seed_number = 1234) {
     # data.table variables
     features <- NULL
 
@@ -799,10 +853,16 @@ NULL
     prox <- proximityMat[random_sel_int, ]
     prox <- prox[prox > 0]
     random_select <- c(sample(
-        all_IDs, size = l_select_ind - 1, replace = FALSE), names(prox[1]))
-    random_other <- c(sample(
-        all_IDs, size = l_other_ind, replace = FALSE), 
-        names(prox[length(prox)]))
+        all_IDs,
+        size = l_select_ind - 1, replace = FALSE
+    ), names(prox[1]))
+    random_other <- c(
+        sample(
+            all_IDs,
+            size = l_other_ind, replace = FALSE
+        ),
+        names(prox[length(prox)])
+    )
 
     resultsDT <- .cal_diff_per_interaction(
         sel_int = random_sel_int,
@@ -821,21 +881,22 @@ NULL
 
 #' @describeIn do_permuttest_spot Calculate multiple random values for spots
 #' @keywords internal
-.do_multi_permuttest_random_spot <- function(sel_int,
-    other_ints,
-    select_ind,
-    other_ind,
-    proximityMat,
-    expr_residual,
-    n = 100,
-    cores = NA,
-    set_seed = TRUE,
-    seed_number = 1234) {
+.do_multi_permuttest_random_spot <- function(
+        sel_int,
+        other_ints,
+        select_ind,
+        other_ind,
+        proximityMat,
+        expr_residual,
+        n = 100,
+        cores = NA,
+        set_seed = TRUE,
+        seed_number = 1234) {
     if (set_seed == TRUE) {
         seed_number_list <- seed_number:(seed_number + (n - 1))
     }
 
-    result <- lapply_flex(X = 1:n, cores = cores, fun = function(x) {
+    result <- lapply_flex(X = seq_len(n), cores = cores, fun = function(x) {
         seed_number <- seed_number_list[x]
 
         perm_rand <- .do_permuttest_random_spot(
@@ -855,24 +916,25 @@ NULL
 }
 
 
-#' @describeIn do_permuttest_spot Performs permutation test on subsets of a 
+#' @describeIn do_permuttest_spot Performs permutation test on subsets of a
 #' matrix for spots
 #' @keywords internal
-.do_permuttest_spot <- function(sel_int,
-    other_ints,
-    select_ind,
-    other_ind,
-    proximityMat,
-    expr_residual,
-    n_perm = 100,
-    adjust_method = "fdr",
-    cores = 2,
-    set_seed = TRUE,
-    seed_number = 1234) {
+.do_permuttest_spot <- function(
+        sel_int,
+        other_ints,
+        select_ind,
+        other_ind,
+        proximityMat,
+        expr_residual,
+        n_perm = 100,
+        adjust_method = "fdr",
+        cores = 2,
+        set_seed = TRUE,
+        seed_number = 1234) {
     # data.table variables
     log2fc_diff <- log2fc <- sel <- other <- features <- p_higher <-
         p_lower <- perm_sel <- NULL
-    perm_other <- perm_log2fc <- perm_diff <- p.value <- p.adj <- 
+    perm_other <- perm_log2fc <- perm_diff <- p.value <- p.adj <-
         pcc_sel <- pcc_diff <- NULL
     perm_pcc_sel <- perm_pcc_diff <- pcc_other <- NULL
 
@@ -904,9 +966,12 @@ NULL
     ##
     # random_perms[, log2fc_diff := rep(original$log2fc, n_perm) - log2fc]
     random_perms[, c(
-        "perm_sel", "perm_other", "perm_pcc_sel", "perm_pcc_diff") := list(
-            mean(sel), mean(other), mean(pcc_sel), mean(pcc_diff)), 
-        by = features]
+        "perm_sel", "perm_other", "perm_pcc_sel", "perm_pcc_diff"
+    ) := list(
+        mean(sel), mean(other), mean(pcc_sel), mean(pcc_diff)
+    ),
+    by = features
+    ]
 
     ## get p-values
     random_perms[, p_higher := sum(pcc_diff > 0), by = features]
@@ -916,20 +981,23 @@ NULL
 
     ## combine results permutation and original
     random_perms_res <- unique(random_perms[, .(
-        features, perm_sel, perm_other, perm_pcc_sel, perm_pcc_diff, 
-        p_higher, p_lower)])
+        features, perm_sel, perm_other, perm_pcc_sel, perm_pcc_diff,
+        p_higher, p_lower
+    )])
     results_m <- data.table::merge.data.table(
-        random_perms_res, 
+        random_perms_res,
         original[, .(features, sel, other, diff, pcc_sel, pcc_other, pcc_diff)],
-        by = "features")
+        by = "features"
+    )
 
     # select lowest p-value and perform p.adj
     results_m[, p.value := ifelse(p_higher <= p_lower, p_higher, p_lower)]
     results_m[, p.adj := stats::p.adjust(p.value, method = adjust_method)]
 
     results_m <- results_m[, .(
-        features, sel, other, pcc_sel, pcc_other, pcc_diff, p.value, p.adj, 
-        perm_sel, perm_other, perm_pcc_sel, perm_pcc_diff)]
+        features, sel, other, pcc_sel, pcc_other, pcc_diff, p.value, p.adj,
+        perm_sel, perm_other, perm_pcc_sel, perm_pcc_diff
+    )]
     setorder(results_m, p.adj, -pcc_diff)
 
     return(results_m)
@@ -938,25 +1006,28 @@ NULL
 
 #' @title Cell proximity testing for spot data
 #' @name .do_cell_proximity_test_spot
-#' @description Performs a selected differential test on subsets of a matrix 
+#' @description Performs a selected differential test on subsets of a matrix
 #' for spots
 #' @returns differential test on subsets of a matrix
 #' @keywords internal
-.do_cell_proximity_test_spot <- function(sel_int,
-    other_ints,
-    select_ind,
-    other_ind,
-    proximityMat,
-    expr_residual,
-    diff_test,
-    n_perm = 100,
-    adjust_method = "fdr",
-    cores = 2,
-    set_seed = TRUE,
-    seed_number = 1234) {
+.do_cell_proximity_test_spot <- function(
+        sel_int,
+        other_ints,
+        select_ind,
+        other_ind,
+        proximityMat,
+        expr_residual,
+        diff_test,
+        n_perm = 100,
+        adjust_method = "fdr",
+        cores = 2,
+        set_seed = TRUE,
+        seed_number = 1234) {
     # get parameters
     diff_test <- match.arg(
-        diff_test, choices = c("permutation", "limma", "t.test", "wilcox"))
+        diff_test,
+        choices = c("permutation", "limma", "t.test", "wilcox")
+    )
     adjust_method <- match.arg(adjust_method, choices = c(
         "bonferroni", "BH", "holm", "hochberg", "hommel",
         "BY", "fdr", "none"
@@ -983,25 +1054,26 @@ NULL
 
 #' @title Find ICF per interaction for spot data
 #' @name .findICF_per_interaction_spot
-#' @description Identifies features that are differentially expressed due to 
+#' @description Identifies features that are differentially expressed due to
 #' proximity to other cell types for spots.
 #' @returns data.table
 #' @keywords internal
-.findICF_per_interaction_spot <- function(sel_int,
-    all_ints,
-    proximityMat,
-    expr_residual,
-    dwls_values,
-    dwls_cutoff = 0.001,
-    CCI_cell_score = 0.01,
-    minimum_unique_cells = 1,
-    minimum_unique_int_cells = 1,
-    diff_test = "permutation",
-    n_perm = 100,
-    adjust_method = "fdr",
-    cores = 2,
-    set_seed = TRUE,
-    seed_number = 1234) {
+.findICF_per_interaction_spot <- function(
+        sel_int,
+        all_ints,
+        proximityMat,
+        expr_residual,
+        dwls_values,
+        dwls_cutoff = 0.001,
+        CCI_cell_score = 0.01,
+        minimum_unique_cells = 1,
+        minimum_unique_int_cells = 1,
+        diff_test = "permutation",
+        n_perm = 100,
+        adjust_method = "fdr",
+        cores = 2,
+        set_seed = TRUE,
+        seed_number = 1234) {
     # data.table variables
     unified_int <- NULL
 
@@ -1026,7 +1098,8 @@ NULL
 
     ## do not continue if too few cells ##
     if (length(spec_IDs) < minimum_unique_cells | length(
-        other_IDs) < minimum_unique_cells) {
+        other_IDs
+    ) < minimum_unique_cells) {
         result <- NULL
     } else {
         result <- .do_cell_proximity_test_spot(
@@ -1064,9 +1137,10 @@ NULL
 
 #' @title findICFSpot
 #' @name findICFSpot
-#' @description Identifies cell-to-cell Interaction Changed Features (ICF) for 
-#' spots, i.e. features expression residual that are different due to proximity 
-#' to other cell types.
+#' @description Identifies cell-to-cell Interaction Changed Features (ICF) for
+#' spots, i.e. features expression residual that are different due to proximity
+#' to other cell types. Works using results from celltype deconvolution methods
+#' such as those from [runDWLSDeconv()].
 #'
 #' @param gobject A giotto object
 #' @param spat_unit spatial unit (e.g. 'cell')
@@ -1075,6 +1149,7 @@ NULL
 #' @param ave_celltype_exp average feature expression in each cell type
 #' @param selected_features subset of selected features (optional)
 #' @param spatial_network_name name of spatial network to use
+#' @param deconv_name name of deconvolution/spatial enrichment values to use
 #' @param minimum_unique_cells minimum number of target cells required
 #' @param minimum_unique_int_cells minimum number of interacting cells required
 #' @param CCI_cell_score cell proximity score to filter no interacted cell
@@ -1088,35 +1163,61 @@ NULL
 #' @param seed_number seed number
 #' @param verbose be verbose
 #'
-#' @returns icfObject that contains the differential feat scores
-#' @details Function to calculate if features expression residual are 
-#' differentially expressed in cell types when they interact 
+#' @returns `icfObject` that contains the differential feat scores
+#' @details Function to calculate if features expression residual are
+#' differentially expressed in cell types when they interact
 #' (approximated by physical proximity) with other cell types.
 #' Feature expression residual calculated as:
-#' (observed expression in spot - cell_type_proportion * 
+#' (observed expression in spot - cell_type_proportion *
 #' average_expressed_in_cell_type)
-#' The results data.table in the icfObject contains - at least - 
+#' The results data.table in the `icfObject` contains - at least -
 #' the following columns:
-#' \itemize{
-#'  \item{features:}{ All or selected list of tested features}
-#'  \item{sel:}{ average feature expression residual in the interacting cells from the target cell type }
-#'  \item{other:}{ average feature expression residual in the NOT-interacting cells from the target cell type }
-#'  \item{pcc_sel:}{ correlation between cell proximity score and expression residual in the interacting cells from the target cell type}
-#'  \item{pcc_other:}{ correlation between cell proximity score and expression residual in the NOT-interacting cells from the target cell type }
-#'  \item{pcc_diff:}{ correlation difference between sel and other}
-#'  \item{p.value:}{ associated p-value}
-#'  \item{p.adj:}{ adjusted p-value}
-#'  \item{cell_type:}{ target cell type}
-#'  \item{int_cell_type:}{ interacting cell type}
-#'  \item{nr_select:}{ number of cells for selected target cell type}
-#'  \item{int_nr_select:}{ number of cells for interacting cell type}
-#'  \item{unif_int:}{ cell-cell interaction}
-#' }
+#'  * **features:** All or selected list of tested features
+#'  * **sel:** average feature expression residual in the interacting cells
+#'  from the target cell type
+#'  * **other:** average feature expression residual in the NOT-interacting
+#'  cells from the target cell type
+#'  * **pcc_sel:** correlation between cell proximity score and expression
+#'  residual in the interacting cells from the target cell type
+#'  * **pcc_other:** correlation between cell proximity score and expression
+#'  residual in the NOT-interacting cells from the target cell type
+#'  * **pcc_diff:** correlation difference between sel and other
+#'  * **p.value:** associated p-value
+#'  * **p.adj:** adjusted p-value
+#'  * **cell_type:** target cell type
+#'  * **int_cell_type:** interacting cell type
+#'  * **nr_select:** number of cells for selected target cell type
+#'  * **int_nr_select:** number of cells for interacting cell type
+#'  * **unif_int:** cell-cell interaction
+#'
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' g_expression <- getExpression(g, output = "matrix")
+#' x <- findMarkers_one_vs_all(g,
+#'     cluster_column = "leiden_clus", min_feats = 20
+#' )
+#' sign_gene <- x$feats
+#'
+#' sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+#'     nrow = length(sign_gene)
+#' )
+#' rownames(sign_matrix) <- sign_gene
+#' colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
+#'
+#' g <- runDWLSDeconv(gobject = g, sign_matrix = sign_matrix)
+#' ave_celltype_exp <- calculateMetaTable(g, metadata_cols = "leiden_clus")
+#' ave_celltype_exp <- reshape2::dcast(ave_celltype_exp, variable~leiden_clus)
+#' rownames(ave_celltype_exp) <- ave_celltype_exp$variable
+#' ave_celltype_exp <- ave_celltype_exp[,-1]
+#' colnames(ave_celltype_exp) <- colnames(sign_matrix)
 #' 
-#' findICFSpot(g, spat_unit = "cell", feat_type = "rna", ave_celltype_exp = g_expression, spatial_network_name = "spatial_network")
+#' findICFSpot(g,
+#'     spat_unit = "cell",
+#'     feat_type = "rna",
+#'     ave_celltype_exp = ave_celltype_exp,
+#'     spatial_network_name = "spatial_network"
+#' )
+#' @seealso [findInteractionChangedFeats()]
+#' @md
 #' @export
 findICFSpot <- function(gobject,
     spat_unit = NULL,
@@ -1125,6 +1226,7 @@ findICFSpot <- function(gobject,
     ave_celltype_exp,
     selected_features = NULL,
     spatial_network_name = "Delaunay_network",
+    deconv_name = "DWLS",
     minimum_unique_cells = 5,
     minimum_unique_int_cells = 5,
     CCI_cell_score = 0.1,
@@ -1142,9 +1244,12 @@ findICFSpot <- function(gobject,
 
     # expression data
     values <- match.arg(
-        expression_values, choices = c("normalized", "scaled", "custom"))
+        expression_values,
+        choices = c("normalized", "scaled", "custom")
+    )
     features_overlap <- intersect(
-        slot(gobject, "feat_ID")[[feat_type]], rownames(ave_celltype_exp))
+        slot(gobject, "feat_ID")[[feat_type]], rownames(ave_celltype_exp)
+    )
     ave_celltype_exp_sel <- ave_celltype_exp[features_overlap, ]
     expr_residual <- .cal_expr_residual(
         gobject = gobject,
@@ -1156,7 +1261,8 @@ findICFSpot <- function(gobject,
     ## test selected features ##
     if (!is.null(selected_features)) {
         expr_residual <- expr_residual[
-            rownames(expr_residual) %in% selected_features, ]
+            rownames(expr_residual) %in% selected_features,
+        ]
     }
 
     # compute cell proximity for each spot
@@ -1169,16 +1275,18 @@ findICFSpot <- function(gobject,
     # compute correlation between features and cell-types to find ICFs
     all_ints <- data.table::data.table(unified_int = rownames(proximityMat))
     all_ints[, cell_type := strsplit(
-        as.character(unified_int), "--")[[1]][1], by = 1:nrow(all_ints)]
+        as.character(unified_int), "--"
+    )[[1]][1], by = seq_len(nrow(all_ints))]
     all_ints[, int_cell_type := strsplit(
-        as.character(unified_int), "--")[[1]][2], by = 1:nrow(all_ints)]
+        as.character(unified_int), "--"
+    )[[1]][2], by = seq_len(nrow(all_ints))]
 
     # exact spatial_enrichment matrix
     dwls_values <- getSpatialEnrichment(
         gobject = gobject,
         spat_unit = spat_unit,
         feat_type = feat_type,
-        name = "DWLS",
+        name = deconv_name,
         output = "data.table"
     )
     data.table::setDF(dwls_values)
@@ -1190,23 +1298,24 @@ findICFSpot <- function(gobject,
     if (do_parallel == TRUE) {
         fin_result <- lapply_flex(
             X = all_ints$unified_int, cores = cores, fun = function(x) {
-            tempres <- .findICF_per_interaction_spot(
-                sel_int = x,
-                all_ints = all_ints,
-                proximityMat = proximityMat,
-                expr_residual = expr_residual,
-                dwls_values = dwls_values,
-                dwls_cutoff = dwls_cutoff,
-                CCI_cell_score = CCI_cell_score,
-                minimum_unique_cells = minimum_unique_cells,
-                minimum_unique_int_cells = minimum_unique_int_cells,
-                n_perm = nr_permutations,
-                adjust_method = adjust_method,
-                cores = cores,
-                set_seed = set_seed,
-                seed_number = seed_number
-            )
-        })
+                tempres <- .findICF_per_interaction_spot(
+                    sel_int = x,
+                    all_ints = all_ints,
+                    proximityMat = proximityMat,
+                    expr_residual = expr_residual,
+                    dwls_values = dwls_values,
+                    dwls_cutoff = dwls_cutoff,
+                    CCI_cell_score = CCI_cell_score,
+                    minimum_unique_cells = minimum_unique_cells,
+                    minimum_unique_int_cells = minimum_unique_int_cells,
+                    n_perm = nr_permutations,
+                    adjust_method = adjust_method,
+                    cores = cores,
+                    set_seed = set_seed,
+                    seed_number = seed_number
+                )
+            }
+        )
     } else {
         fin_result <- list()
 
@@ -1241,30 +1350,34 @@ findICFSpot <- function(gobject,
 
     final_result[, spec_int := paste0(cell_type, "--", int_cell_type)]
     final_result[, type_int := ifelse(
-        cell_type == int_cell_type, "homo", "hetero")]
+        cell_type == int_cell_type, "homo", "hetero"
+    )]
 
 
     # return(final_result)
 
     permutation_test <- ifelse(
-        diff_test == "permutation", nr_permutations, "no permutations")
-
-    icfObject <- list(
-        ICFscores = final_result,
-        Giotto_info = list(
-            "values" = values,
-            "cluster" = "cell_ID",
-            "spatial network" = spatial_network_name
+        diff_test == "permutation", nr_permutations, "no permutations"
+    )
+
+    icfObject <- structure(
+        .Data = list(
+            ICFscores = final_result,
+            Giotto_info = list(
+                "values" = values,
+                "cluster" = "cell_ID",
+                "spatial network" = spatial_network_name
+            ),
+            test_info = list(
+                "test" = diff_test,
+                "p.adj" = adjust_method,
+                "min cells" = minimum_unique_cells,
+                "min interacting cells" = minimum_unique_int_cells,
+                "perm" = permutation_test
+            )
         ),
-        test_info = list(
-            "test" = diff_test,
-            "p.adj" = adjust_method,
-            "min cells" = minimum_unique_cells,
-            "min interacting cells" = minimum_unique_int_cells,
-            "perm" = permutation_test
-        )
+        class = "icfObject"
     )
-    class(icfObject) <- append(class(icfObject), "icfObject")
     return(icfObject)
 }
 
@@ -1275,10 +1388,10 @@ findICFSpot <- function(gobject,
 #'
 #' @param icfObject ICF (interaction changed feature) score object
 #' @param min_cells minimum number of source cell type
-#' @param min_cells_expr_resi minimum expression residual level for source 
+#' @param min_cells_expr_resi minimum expression residual level for source
 #' cell type
 #' @param min_int_cells minimum number of interacting neighbor cell type
-#' @param min_int_cells_expr_resi minimum expression residual level for 
+#' @param min_int_cells_expr_resi minimum expression residual level for
 #' interacting neighbor cell type
 #' @param min_fdr minimum adjusted p-value
 #' @param min_pcc_diff minimum absolute pcc difference difference
@@ -1290,31 +1403,34 @@ findICFSpot <- function(gobject,
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #' icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus")
-#' 
+#'
 #' filterICFSpot(icfObject = icfObject)
 #' @export
-filterICFSpot <- function(icfObject,
-    min_cells = 4,
-    min_cells_expr_resi = 0.05,
-    min_int_cells = 4,
-    min_int_cells_expr_resi = 0.05,
-    min_fdr = 0.5,
-    min_pcc_diff = 0.05,
-    min_zscore = 0.05,
-    zscores_column = c("cell_type", "features"),
-    direction = c("both", "up", "down")) {
+filterICFSpot <- function(
+        icfObject,
+        min_cells = 4,
+        min_cells_expr_resi = 0.05,
+        min_int_cells = 4,
+        min_int_cells_expr_resi = 0.05,
+        min_fdr = 0.5,
+        min_pcc_diff = 0.05,
+        min_zscore = 0.05,
+        zscores_column = c("cell_type", "features"),
+        direction = c("both", "up", "down")) {
     # data.table variables
-    nr_select <- int_nr_select <- zscores <- perm_diff <- sel <- other <- 
+    nr_select <- int_nr_select <- zscores <- perm_diff <- sel <- other <-
         p.adj <- NULL
     log2fc <- min_log2_fc <- NULL
 
     if (!"icfObject" %in% class(icfObject)) {
-        stop("icfObject needs to be the output from 
+        stop("icfObject needs to be the output from
             findInteractionChangedFeats() or findICF()")
     }
 
     zscores_column <- match.arg(
-        zscores_column, choices = c("cell_type", "features"))
+        zscores_column,
+        choices = c("cell_type", "features")
+    )
 
     ICFscore <- copy(icfObject[["ICFscores"]])
 
@@ -1325,7 +1441,8 @@ filterICFSpot <- function(icfObject,
     ## sequential filter steps ##
     # 1. minimum number of source and target cells
     selection_scores <- ICFscore[
-        nr_select >= min_cells & int_nr_select >= min_int_cells]
+        nr_select >= min_cells & int_nr_select >= min_int_cells
+    ]
 
     # 2. create z-scores for log2fc per cell type
     selection_scores[, zscores := scale(perm_diff), by = c(zscores_column)]
@@ -1333,9 +1450,11 @@ filterICFSpot <- function(icfObject,
     # 3. filter based on z-scores and minimum levels
     comb_DT <- rbind(
         selection_scores[zscores >= min_zscore & abs(
-            perm_diff) >= min_pcc_diff & sel >= min_cells_expr_resi],
+            perm_diff
+        ) >= min_pcc_diff & sel >= min_cells_expr_resi],
         selection_scores[zscores <= -min_zscore & abs(
-            perm_diff) >= min_pcc_diff & other >= min_int_cells_expr_resi]
+            perm_diff
+        ) >= min_pcc_diff & other >= min_int_cells_expr_resi]
     )
 
     # 4. filter based on adjusted p-value (fdr)
@@ -1370,30 +1489,35 @@ filterICFSpot <- function(icfObject,
 #' @returns plot
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus",
-#' selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
-#' 
-#' plotICFSpot(gobject = g, icfObject = icfObject,
-#' source_type = "1", source_markers = "Ccnd2", 
-#' ICF_features = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17"))
+#' icfObject <- findInteractionChangedFeats(g,
+#'     cluster_column = "leiden_clus",
+#'     selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10
+#' )
+#'
+#' plotICFSpot(
+#'     gobject = g, icfObject = icfObject,
+#'     source_type = "1", source_markers = "Ccnd2",
+#'     ICF_features = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17")
+#' )
 #' @export
-plotICFSpot <- function(gobject,
-    icfObject,
-    source_type,
-    source_markers,
-    ICF_features,
-    cell_color_code = NULL,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotICFSpot") {
+plotICFSpot <- function(
+        gobject,
+        icfObject,
+        source_type,
+        source_markers,
+        ICF_features,
+        cell_color_code = NULL,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotICFSpot") {
     # data.table variables
-    cell_type <- int_cell_type <- pcc_diff <- NULL
+    cell_type <- int_cell_type <- pcc_diff <- feats <- perm_diff <- NULL
 
 
     if (!"icfObject" %in% class(icfObject)) {
-        stop("icfObject needs to be the output from 
+        stop("icfObject needs to be the output from
             findInteractionChangedFeats() or findICF()")
     }
 
@@ -1418,16 +1542,20 @@ plotICFSpot <- function(gobject,
     features <- group <- NULL
 
     tempDT <- ICFscores[feats %in% all_features][
-        cell_type == source_type][int_cell_type %in% neighbor_types]
+        cell_type == source_type
+    ][int_cell_type %in% neighbor_types]
     tempDT[, features := factor(feats, levels = detected_features)]
     tempDT[, group := names(ICF_features[
-        ICF_features == feats]), by = 1:nrow(tempDT)]
+        ICF_features == feats
+    ]), by = seq_len(nrow(tempDT))]
 
 
     if (is.null(cell_color_code)) {
         mycolors <- set_default_color_discrete_cell(
-            instrs = instructions(gobject))(n = length(unique(
-                tempDT$int_cell_type)))
+            instrs = instructions(gobject)
+        )(n = length(unique(
+            tempDT$int_cell_type
+        )))
         names(mycolors) <- unique(tempDT$int_cell_type)
     } else {
         mycolors <- cell_color_code
@@ -1437,17 +1565,20 @@ plotICFSpot <- function(gobject,
     pl <- ggplot2::ggplot()
     pl <- pl + ggplot2::theme_classic() + ggplot2::theme(
         axis.text.x = ggplot2::element_text(
-            size = 14, angle = 45, vjust = 1, hjust = 1),
+            size = 14, angle = 45, vjust = 1, hjust = 1
+        ),
         axis.text.y = ggplot2::element_text(size = 14),
         axis.title = ggplot2::element_text(size = 14)
     )
     pl <- pl + ggplot2::geom_bar(
-        data = tempDT, 
-        ggplot2::aes(x = feats, y = perm_diff, fill = int_cell_type), 
-        stat = "identity", position = ggplot2::position_dodge())
+        data = tempDT,
+        ggplot2::aes(x = feats, y = perm_diff, fill = int_cell_type),
+        stat = "identity", position = ggplot2::position_dodge()
+    )
     pl <- pl + ggplot2::scale_fill_manual(values = mycolors)
     pl <- pl + ggplot2::labs(x = "", title = paste0(
-        "fold-change z-scores in ", source_type))
+        "fold-change z-scores in ", source_type
+    ))
 
     return(plot_output_handler(
         gobject = gobject,
@@ -1482,47 +1613,55 @@ plotICFSpot <- function(gobject,
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #' icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus")
-#' 
-#' plotCellProximityFeatSpot(gobject = g, icfObject = icfObject,
-#' show_plot = TRUE, save_plot = FALSE, return_plot = FALSE, 
-#' min_pcc_diff = 0.01)
+#'
+#' plotCellProximityFeatSpot(
+#'     gobject = g, icfObject = icfObject,
+#'     show_plot = TRUE, save_plot = FALSE, return_plot = FALSE,
+#'     min_pcc_diff = 0.01
+#' )
 #' @export
-plotCellProximityFeatSpot <- function(gobject,
-    icfObject,
-    method = c("volcano", "cell_barplot", "cell-cell", "cell_sankey", 
-                "heatmap", "dotplot"),
-    min_cells = 4,
-    min_cells_expr_resi = 0.05,
-    min_int_cells = 4,
-    min_int_cells_expr_resi = 0.05,
-    min_fdr = 0.5,
-    min_pcc_diff = 0.05,
-    min_zscore = 0.05,
-    zscores_column = c("cell_type", "features"),
-    direction = c("both", "up", "down"),
-    cell_color_code = NULL,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotCellProximityFeats") {
+plotCellProximityFeatSpot <- function(
+        gobject,
+        icfObject,
+        method = c(
+            "volcano", "cell_barplot", "cell-cell", "cell_sankey",
+            "heatmap", "dotplot"
+        ),
+        min_cells = 4,
+        min_cells_expr_resi = 0.05,
+        min_int_cells = 4,
+        min_int_cells_expr_resi = 0.05,
+        min_fdr = 0.5,
+        min_pcc_diff = 0.05,
+        min_zscore = 0.05,
+        zscores_column = c("cell_type", "features"),
+        direction = c("both", "up", "down"),
+        cell_color_code = NULL,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotCellProximityFeats") {
     if (!"icfObject" %in% class(icfObject)) {
-        stop("icfObject needs to be the output from 
+        stop("icfObject needs to be the output from
             findInteractionChangedFeats() or findICF()")
     }
 
     # print, return and save parameters
     show_plot <- ifelse(
-        is.null(show_plot), 
+        is.null(show_plot),
         readGiottoInstructions(gobject, param = "show_plot"),
-        show_plot)
+        show_plot
+    )
     save_plot <- ifelse(
-        is.null(save_plot), 
+        is.null(save_plot),
         readGiottoInstructions(gobject, param = "save_plot"),
-        save_plot)
+        save_plot
+    )
     return_plot <- ifelse(
-        is.null(return_plot), 
-        readGiottoInstructions(gobject, param = "return_plot"), return_plot)
+        is.null(return_plot),
+        readGiottoInstructions(gobject, param = "return_plot"), return_plot
+    )
 
 
     ## first filter
@@ -1537,16 +1676,19 @@ plotCellProximityFeatSpot <- function(gobject,
         zscores_column = c("cell_type", "features"),
         direction = c("both", "up", "down")
     )
-    
+
     message("filter complete")
 
     complete_part <- filter_icf[["ICFscores"]]
 
     ## other parameters
     method <- match.arg(
-        method, 
-        choices = c("volcano", "cell_barplot", "cell-cell", "cell_sankey", 
-                    "heatmap", "dotplot"))
+        method,
+        choices = c(
+            "volcano", "cell_barplot", "cell-cell", "cell_sankey",
+            "heatmap", "dotplot"
+        )
+    )
 
 
     # variables
@@ -1557,10 +1699,12 @@ plotCellProximityFeatSpot <- function(gobject,
         ## volcanoplot
         pl <- ggplot2::ggplot()
         pl <- pl + ggplot2::geom_point(
-            data = complete_part, 
+            data = complete_part,
             ggplot2::aes(
-                x = perm_diff, 
-                y = ifelse(is.infinite(-log10(p.adj)), 1000, -log10(p.adj))))
+                x = perm_diff,
+                y = ifelse(is.infinite(-log10(p.adj)), 1000, -log10(p.adj))
+            )
+        )
         pl <- pl + ggplot2::theme_classic()
         pl <- pl + ggplot2::geom_vline(xintercept = 0, linetype = 2)
         pl <- pl + ggplot2::labs(x = "pcc diff", y = "-log10(p.adjusted)")
@@ -1574,9 +1718,12 @@ plotCellProximityFeatSpot <- function(gobject,
         ## save plot
         if (save_plot == TRUE) {
             do.call(
-                "all_plots_save_function", 
-                c(list(gobject = gobject, plot_object = pl, 
-                        default_save_name = default_save_name), save_param))
+                "all_plots_save_function",
+                c(list(
+                    gobject = gobject, plot_object = pl,
+                    default_save_name = default_save_name
+                ), save_param)
+            )
         }
 
         ## return plot
@@ -1591,11 +1738,13 @@ plotCellProximityFeatSpot <- function(gobject,
 
         pl <- ggplot2::ggplot()
         pl <- pl + ggplot2::geom_bar(
-            data = complete_part, 
-            ggplot2::aes(x = unif_int, fill = unif_int))
+            data = complete_part,
+            ggplot2::aes(x = unif_int, fill = unif_int)
+        )
         pl <- pl + ggplot2::theme_classic() + ggplot2::theme(
             axis.text.x = ggplot2::element_text
-            (angle = 90, hjust = 1, vjust = 1))
+            (angle = 90, hjust = 1, vjust = 1)
+        )
         pl <- pl + ggplot2::coord_flip()
 
         ## print plot
@@ -1606,9 +1755,12 @@ plotCellProximityFeatSpot <- function(gobject,
         ## save plot
         if (save_plot == TRUE) {
             do.call(
-                "all_plots_save_function", 
-                c(list(gobject = gobject, plot_object = pl, 
-                        default_save_name = default_save_name), save_param))
+                "all_plots_save_function",
+                c(list(
+                    gobject = gobject, plot_object = pl,
+                    default_save_name = default_save_name
+                ), save_param)
+            )
         }
 
         ## return plot
@@ -1624,15 +1776,18 @@ plotCellProximityFeatSpot <- function(gobject,
 
         pl <- ggplot2::ggplot()
         pl <- pl + ggplot2::geom_bar(
-            data = complete_part, 
-            ggplot2::aes(x = cell_type, fill = int_cell_type))
+            data = complete_part,
+            ggplot2::aes(x = cell_type, fill = int_cell_type)
+        )
         if (!is.null(cell_color_code)) {
             pl <- pl + ggplot2::scale_fill_manual(values = cell_color_code)
         }
         pl <- pl + ggplot2::theme_classic() + ggplot2::theme(
-            axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
+            axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)
+        )
         pl <- pl + ggplot2::labs(
-            x = "", y = "# of features influenced by cell neighborhood")
+            x = "", y = "# of features influenced by cell neighborhood"
+        )
 
 
         ## print plot
@@ -1643,9 +1798,12 @@ plotCellProximityFeatSpot <- function(gobject,
         ## save plot
         if (save_plot == TRUE) {
             do.call(
-                "all_plots_save_function", 
-                c(list(gobject = gobject, plot_object = pl, 
-                        default_save_name = default_save_name), save_param))
+                "all_plots_save_function",
+                c(list(
+                    gobject = gobject, plot_object = pl,
+                    default_save_name = default_save_name
+                ), save_param)
+            )
         }
 
         ## return plot
@@ -1666,14 +1824,18 @@ plotCellProximityFeatSpot <- function(gobject,
         ) +
             ggalluvial::geom_alluvium(aes(fill = cell_type), width = 1 / 12) +
             ggalluvial::geom_stratum(
-                width = 1 / 12, fill = "black", color = "grey") +
+                width = 1 / 12, fill = "black", color = "grey"
+            ) +
             ggplot2::scale_x_discrete(
-                limits = c("cell type", "neighbours"), expand = c(.05, .05)) +
+                limits = c("cell type", "neighbours"), expand = c(.05, .05)
+            ) +
             ggplot2::geom_label(
-                tat = "stratum", label.strata = TRUE, size = 3) +
+                tat = "stratum", label.strata = TRUE, size = 3
+            ) +
             ggplot2::theme_classic() +
             ggplot2::labs(
-                x = "", y = "# of features influenced by cell neighborhood")
+                x = "", y = "# of features influenced by cell neighborhood"
+            )
 
         if (!is.null(cell_color_code)) {
             pl <- pl + ggplot2::scale_fill_manual(values = cell_color_code)
@@ -1689,9 +1851,12 @@ plotCellProximityFeatSpot <- function(gobject,
         ## save plot
         if (save_plot == TRUE) {
             do.call(
-                "all_plots_save_function", 
-                c(list(gobject = gobject, plot_object = pl, 
-                        default_save_name = default_save_name), save_param))
+                "all_plots_save_function",
+                c(list(
+                    gobject = gobject, plot_object = pl,
+                    default_save_name = default_save_name
+                ), save_param)
+            )
         }
 
         ## return plot
@@ -1700,23 +1865,30 @@ plotCellProximityFeatSpot <- function(gobject,
         }
     } else if (method == "dotplot") {
         changed_features <- complete_part[
-            , .N, by = c("cell_type", "int_cell_type")]
+            , .N,
+            by = c("cell_type", "int_cell_type")
+        ]
 
         changed_features[, cell_type := factor(cell_type, unique(cell_type))]
         changed_features[, int_cell_type := factor(
-            int_cell_type, unique(int_cell_type))]
+            int_cell_type, unique(int_cell_type)
+        )]
 
         pl <- ggplot2::ggplot()
         pl <- pl + ggplot2::theme_classic()
         pl <- pl + ggplot2::geom_point(
-            data = changed_features, 
-            ggplot2::aes(x = cell_type, y = int_cell_type, size = N))
+            data = changed_features,
+            ggplot2::aes(x = cell_type, y = int_cell_type, size = N)
+        )
         pl <- pl + ggplot2::scale_size_continuous(
-            guide = guide_legend(title = "# of ICFs"))
+            guide = guide_legend(title = "# of ICFs")
+        )
         pl <- pl + ggplot2::theme(axis.text.x = ggplot2::element_text(
-            angle = 90, vjust = 1, hjust = 1))
+            angle = 90, vjust = 1, hjust = 1
+        ))
         pl <- pl + ggplot2::labs(
-            x = "source cell type", y = "neighbor cell type")
+            x = "source cell type", y = "neighbor cell type"
+        )
 
         ## print plot
         if (show_plot == TRUE) {
@@ -1726,9 +1898,12 @@ plotCellProximityFeatSpot <- function(gobject,
         ## save plot
         if (save_plot == TRUE) {
             do.call(
-                "all_plots_save_function", 
-                c(list(gobject = gobject, plot_object = pl, 
-                        default_save_name = default_save_name), save_param))
+                "all_plots_save_function",
+                c(list(
+                    gobject = gobject, plot_object = pl,
+                    default_save_name = default_save_name
+                ), save_param)
+            )
         }
 
         ## return plot
@@ -1737,17 +1912,21 @@ plotCellProximityFeatSpot <- function(gobject,
         }
     } else if (method == "heatmap") {
         changed_features <- complete_part[
-            , .N, by = c("cell_type", "int_cell_type")]
+            , .N,
+            by = c("cell_type", "int_cell_type")
+        ]
 
         changed_features[, cell_type := factor(cell_type, unique(cell_type))]
         changed_features[, int_cell_type := factor(
-            int_cell_type, unique(int_cell_type))]
+            int_cell_type, unique(int_cell_type)
+        )]
 
         changed_features_d <- data.table::dcast.data.table(
-            changed_features, 
-            cell_type ~ int_cell_type, 
-            value.var = "N", 
-            fill = 0)
+            changed_features,
+            cell_type ~ int_cell_type,
+            value.var = "N",
+            fill = 0
+        )
         changed_features_m <- dt_to_matrix(changed_features_d)
 
         col_fun <- GiottoVisuals::colorRamp2(
@@ -1755,11 +1934,13 @@ plotCellProximityFeatSpot <- function(gobject,
             colors = c("white", "white", "blue", "yellow", "red")
         )
 
-        heatm <- ComplexHeatmap::Heatmap(as.matrix(log2(
-            changed_features_m + 1)),
+        heatm <- ComplexHeatmap::Heatmap(
+            as.matrix(log2(
+                changed_features_m + 1
+            )),
             col = col_fun,
-            row_title = "cell_type", 
-            column_title = "int_cell_type", 
+            row_title = "cell_type",
+            column_title = "int_cell_type",
             heatmap_legend_param = list(title = "log2(# DEGs)")
         )
 
@@ -1771,9 +1952,12 @@ plotCellProximityFeatSpot <- function(gobject,
         ## save plot
         if (save_plot == TRUE) {
             do.call(
-                "all_plots_save_function", 
-                c(list(gobject = gobject, plot_object = heatm, 
-                        default_save_name = default_save_name), save_param))
+                "all_plots_save_function",
+                c(list(
+                    gobject = gobject, plot_object = heatm,
+                    default_save_name = default_save_name
+                ), save_param)
+            )
         }
 
         ## return plot
@@ -1790,7 +1974,7 @@ plotCellProximityFeatSpot <- function(gobject,
 
 #' @title Specific cell-cell communication scores for spot data
 #' @name .specific_CCCScores_spots
-#' @description Specific Cell-Cell communication scores based on spatial 
+#' @description Specific Cell-Cell communication scores based on spatial
 #' expression of interacting cells at spots resolution
 #'
 #' @param gobject giotto object to use
@@ -1804,9 +1988,9 @@ plotCellProximityFeatSpot <- function(gobject,
 #' @param cell_type_2 second cell type
 #' @param feature_set_1 first specific feature set from feature pairs
 #' @param feature_set_2 second specific feature set from feature pairs
-#' @param min_observations minimum number of interactions needed to be 
+#' @param min_observations minimum number of interactions needed to be
 #' considered
-#' @param detailed provide more detailed information 
+#' @param detailed provide more detailed information
 #' (random variance and z-score)
 #' @param adjust_method which method to adjust p-values
 #' @param adjust_target adjust multiple hypotheses at the cell or feature level
@@ -1814,59 +1998,67 @@ plotCellProximityFeatSpot <- function(gobject,
 #' @param seed_number seed number
 #' @param verbose verbose
 #'
-#' @returns Cell-Cell communication scores for feature pairs based on spatial 
+#' @returns Cell-Cell communication scores for feature pairs based on spatial
 #' interaction
-#' @details Statistical framework to identify if pairs of features 
-#' (such as ligand-receptor combinations) are expressed at higher levels than 
-#' expected based on a reshuffled null distribution of feature expression 
+#' @details Statistical framework to identify if pairs of features
+#' (such as ligand-receptor combinations) are expressed at higher levels than
+#' expected based on a reshuffled null distribution of feature expression
 #' values in cells that are spatially in proximity to each other.
 #' \itemize{
-#'  \item{LR_comb:}{Pair of ligand and receptor}
-#'  \item{lig_cell_type:}{ cell type to assess expression level of ligand }
-#'  \item{lig_expr:}{ average expressionresidual(observed - DWLS_predicted) of ligand in lig_cell_type }
-#'  \item{ligand:}{ ligand name }
-#'  \item{rec_cell_type:}{ cell type to assess expression level of receptor }
-#'  \item{rec_expr:}{ average expression residual(observed - DWLS_predicted) of receptor in rec_cell_type}
-#'  \item{receptor:}{ receptor name }
-#'  \item{LR_expr:}{ combined average ligand and receptor expression }
-#'  \item{lig_nr:}{ total number of cells from lig_cell_type that spatially interact with cells from rec_cell_type }
-#'  \item{rec_nr:}{ total number of cells from rec_cell_type that spatially interact with cells from lig_cell_type }
-#'  \item{rand_expr:}{ average combined ligand and receptor expression residual from random spatial permutations }
-#'  \item{av_diff:}{ average difference between LR_expr and rand_expr over all random spatial permutations }
-#'  \item{sd_diff:}{ (optional) standard deviation of the difference between LR_expr and rand_expr over all random spatial permutations }
-#'  \item{z_score:}{ (optinal) z-score }
-#'  \item{log2fc:}{ LR_expr - rand_expr }
-#'  \item{pvalue:}{ p-value }
-#'  \item{LR_cell_comb:}{ cell type pair combination }
-#'  \item{p.adj:}{ adjusted p-value }
-#'  \item{PI:}{ significanc score: log2fc * -log10(p.adj) }
+#'  * LR_comb: Pair of ligand and receptor
+#'  * lig_cell_type: cell type to assess expression level of ligand
+#'  * lig_expr: average expression residual (observed - DWLS_predicted) of
+#'  ligand in lig_cell_type
+#'  * ligand: ligand name
+#'  * rec_cell_type: cell type to assess expression level of receptor
+#'  * rec_expr: average expression residual(observed - DWLS_predicted) of
+#'  receptor in rec_cell_type
+#'  * receptor: receptor name
+#'  * LR_expr: combined average ligand and receptor expression
+#'  * lig_nr: total number of cells from lig_cell_type that spatially interact
+#'  with cells from rec_cell_type
+#'  * rec_nr: total number of cells from rec_cell_type that spatially interact
+#'  with cells from lig_cell_type
+#'  * rand_expr: average combined ligand and receptor expression residual from
+#'  random spatial permutations
+#'  * av_diff: average difference between LR_expr and rand_expr over all random
+#'  spatial permutations
+#'  * sd_diff: (optional) standard deviation of the difference between LR_expr
+#'  and rand_expr over all random spatial permutations
+#'  * z_score: (optinal) z-score
+#'  * log2fc: LR_expr - rand_expr
+#'  * pvalue: p-value
+#'  * LR_cell_comb: cell type pair combination
+#'  * p.adj: adjusted p-value
+#'  * PI: significance score: log2fc \* -log10(p.adj)
 #' }
 #' @keywords internal
-.specific_CCCScores_spots <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expr_residual,
-    dwls_values,
-    proximityMat,
-    random_iter = 1000,
-    cell_type_1 = "astrocytes",
-    cell_type_2 = "endothelial",
-    feature_set_1,
-    feature_set_2,
-    min_observations = 2,
-    detailed = FALSE,
-    adjust_method = c(
-        "fdr", "bonferroni", " BH", "holm", "hochberg", "hommel",
-        "BY", "none"
-    ),
-    adjust_target = c("features", "cells"),
-    set_seed = FALSE,
-    seed_number = 1234,
-    verbose = FALSE) {
+.specific_CCCScores_spots <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expr_residual,
+        dwls_values,
+        proximityMat,
+        random_iter = 1000,
+        cell_type_1 = "astrocytes",
+        cell_type_2 = "endothelial",
+        feature_set_1,
+        feature_set_2,
+        min_observations = 2,
+        detailed = FALSE,
+        adjust_method = c(
+            "fdr", "bonferroni", " BH", "holm", "hochberg", "hommel",
+            "BY", "none"
+        ),
+        adjust_target = c("features", "cells"),
+        set_seed = FALSE,
+        seed_number = 1234,
+        verbose = FALSE) {
     # data.table variables
-    from_to <- cell_ID <- lig_cell_type <- rec_cell_type <- lig_nr <- 
+    from_to <- cell_ID <- lig_cell_type <- rec_cell_type <- lig_nr <-
         rec_nr <- rand_expr <- NULL
-    av_diff <- log2fc <- LR_expr <- pvalue <- LR_cell_comb <- p.adj <- 
+    av_diff <- log2fc <- LR_expr <- pvalue <- LR_cell_comb <- p.adj <-
         LR_comb <- PI <- NULL
     sd_diff <- z_score <- ligand <- receptor <- NULL
 
@@ -1881,8 +2073,11 @@ plotCellProximityFeatSpot <- function(gobject,
     cell_direction_1 <- paste0(cell_type_1, "--", cell_type_2)
     cell_direction_2 <- paste0(cell_type_2, "--", cell_type_1)
 
-    if (verbose) print(paste0(
-        "Processing specific CCC Scores: ", cell_direction_1))
+    if (verbose) {
+        print(paste0(
+            "Processing specific CCC Scores: ", cell_direction_1
+        ))
+    }
 
     proxi_1 <- proximityMat[cell_direction_1, ]
     proxi_2 <- proximityMat[cell_direction_2, ]
@@ -1895,7 +2090,7 @@ plotCellProximityFeatSpot <- function(gobject,
     dwls_ct2 <- dwls_values[, cell_type_2]
 
     # make sure that there are sufficient observations
-    if (length(ct1_cell_ids) <= min_observations | 
+    if (length(ct1_cell_ids) <= min_observations |
         length(ct2_cell_ids) <= min_observations) {
         return(NULL)
     } else {
@@ -1943,7 +2138,7 @@ plotCellProximityFeatSpot <- function(gobject,
         all_cell_ids <- colnames(expr_residual)
 
         ## simulations ##
-        for (sim in 1:random_iter) {
+        for (sim in seq_len(random_iter)) {
             if (verbose == TRUE) cat("simulation ", sim, "\n")
 
             # get random ids and subset
@@ -1953,9 +2148,13 @@ plotCellProximityFeatSpot <- function(gobject,
             }
 
             random_ids_1 <- sample(
-                all_cell_ids, size = length(ct1_cell_ids), replace = FALSE)
+                all_cell_ids,
+                size = length(ct1_cell_ids), replace = FALSE
+            )
             random_ids_2 <- sample(
-                all_cell_ids, size = length(ct2_cell_ids), replace = FALSE)
+                all_cell_ids,
+                size = length(ct2_cell_ids), replace = FALSE
+            )
 
             # get feature expression residual for ligand and receptor
             random_expr_res_L <- expr_residual[feature_set_1, random_ids_1]
@@ -1998,7 +2197,9 @@ plotCellProximityFeatSpot <- function(gobject,
         if (detailed == TRUE) {
             av_difference_scores <- rowMeans_flex(total_sum)
             sd_difference_scores <- apply(
-                total_sum, MARGIN = 1, FUN = stats::sd)
+                total_sum,
+                MARGIN = 1, FUN = stats::sd
+            )
 
             comScore[, av_diff := av_difference_scores]
             comScore[, sd_diff := sd_difference_scores]
@@ -2014,19 +2215,24 @@ plotCellProximityFeatSpot <- function(gobject,
 
         if (adjust_target == "features") {
             comScore[, p.adj := stats::p.adjust(
-                pvalue, method = adjust_method), by = .(LR_cell_comb)]
+                pvalue,
+                method = adjust_method
+            ), by = .(LR_cell_comb)]
         } else if (adjust_target == "cells") {
             comScore[, p.adj := stats::p.adjust(
-                pvalue, method = adjust_method), by = .(LR_comb)]
+                pvalue,
+                method = adjust_method
+            ), by = .(LR_comb)]
         }
 
         # get minimum adjusted p.value that is not zero
         all_p.adj <- comScore[["p.adj"]]
         lowest_p.adj <- min(all_p.adj[all_p.adj != 0])
         comScore[, PI := ifelse(
-            p.adj == 0, 
-            log2fc * (-log10(lowest_p.adj)), 
-            log2fc * (-log10(p.adj)))]
+            p.adj == 0,
+            log2fc * (-log10(lowest_p.adj)),
+            log2fc * (-log10(p.adj))
+        )]
 
         return(comScore)
     }
@@ -2036,7 +2242,7 @@ plotCellProximityFeatSpot <- function(gobject,
 
 #' @title spatCellCellcomSpots
 #' @name spatCellCellcomSpots
-#' @description Spatial Cell-Cell communication scores based on spatial 
+#' @description Spatial Cell-Cell communication scores based on spatial
 #' expression of interacting cells at spots resolution
 #'
 #' @param gobject giotto object to use
@@ -2044,18 +2250,18 @@ plotCellProximityFeatSpot <- function(gobject,
 #' @param feat_type feature type (e.g. 'rna')
 #' @param ave_celltype_exp Matrix with average expression per cell type
 #' @param expression_values (e.g. 'normalized', 'scaled', 'custom')
-#' @param spatial_network_name spatial network to use for identifying 
+#' @param spatial_network_name spatial network to use for identifying
 #' interacting cells
 #' @param cluster_column cluster column with cell type information
 #' @param random_iter number of iterations
 #' @param feature_set_1 first specific feature set from feature pairs
 #' @param feature_set_2 second specific feature set from feature pairs
-#' @param min_observations minimum number of interactions needed to be 
+#' @param min_observations minimum number of interactions needed to be
 #' considered
-#' @param detailed provide more detailed information 
+#' @param detailed provide more detailed information
 #' (random variance and z-score)
 #' @param adjust_method which method to adjust p-values
-#' @param adjust_target adjust multiple hypotheses at the cell or feature 
+#' @param adjust_target adjust multiple hypotheses at the cell or feature
 #' level
 #' @param do_parallel run calculations in parallel with mclapply
 #' @param cores number of cores to use if do_parallel = TRUE
@@ -2063,56 +2269,64 @@ plotCellProximityFeatSpot <- function(gobject,
 #' @param seed_number seed number
 #' @param verbose verbose (e.g. 'a little', 'a lot', 'none')
 #'
-#' @returns Cell-Cell communication scores for feature pairs based on spatial 
+#' @returns Cell-Cell communication scores for feature pairs based on spatial
 #' interaction
-#' @details Statistical framework to identify if pairs of features 
-#' (such as ligand-receptor combinations) are expressed at higher levels than 
-#' expected based on a reshuffled null distribution of feature expression 
+#' @details Statistical framework to identify if pairs of features
+#' (such as ligand-receptor combinations) are expressed at higher levels than
+#' expected based on a reshuffled null distribution of feature expression
 #' values in cells that are spatially in proximity to each other.
 #' \itemize{
-#'  \item{LR_comb:}{Pair of ligand and receptor}
-#'  \item{lig_cell_type:}{ cell type to assess expression level of ligand }
-#'  \item{lig_expr:}{ average expression residual(observed - DWLS_predicted) of ligand in lig_cell_type }
-#'  \item{ligand:}{ ligand name }
-#'  \item{rec_cell_type:}{ cell type to assess expression level of receptor }
-#'  \item{rec_expr:}{ average expression residual(observed - DWLS_predicted) of receptor in rec_cell_type}
-#'  \item{receptor:}{ receptor name }
-#'  \item{LR_expr:}{ combined average ligand and receptor expression residual}
-#'  \item{lig_nr:}{ total number of cells from lig_cell_type that spatially interact with cells from rec_cell_type }
-#'  \item{rec_nr:}{ total number of cells from rec_cell_type that spatially interact with cells from lig_cell_type }
-#'  \item{rand_expr:}{ average combined ligand and receptor expression residual from random spatial permutations }
-#'  \item{av_diff:}{ average difference between LR_expr and rand_expr over all random spatial permutations }
-#'  \item{sd_diff:}{ (optional) standard deviation of the difference between LR_expr and rand_expr over all random spatial permutations }
-#'  \item{z_score:}{ (optinal) z-score }
-#'  \item{log2fc:}{ LR_expr - rand_expr }
-#'  \item{pvalue:}{ p-value }
-#'  \item{LR_cell_comb:}{ cell type pair combination }
-#'  \item{p.adj:}{ adjusted p-value }
-#'  \item{PI:}{ significanc score: log2fc * -log10(p.adj) }
+#'  * LR_comb:Pair of ligand and receptor
+#'  * lig_cell_type: cell type to assess expression level of ligand
+#'  * lig_expr: average expression residual(observed - DWLS_predicted) of
+#'  ligand in lig_cell_type
+#'  * ligand: ligand name
+#'  * rec_cell_type: cell type to assess expression level of receptor
+#'  * rec_expr: average expression residual(observed - DWLS_predicted) of
+#'  receptor in rec_cell_type
+#'  * receptor: receptor name
+#'  * LR_expr: combined average ligand and receptor expression residual
+#'  * lig_nr: total number of cells from lig_cell_type that spatially interact
+#'  with cells from rec_cell_type
+#'  * rec_nr: total number of cells from rec_cell_type that spatially interact
+#'  with cells from lig_cell_type
+#'  * rand_expr: average combined ligand and receptor expression residual from
+#'  random spatial permutations
+#'  * av_diff: average difference between LR_expr and rand_expr over all random
+#'  spatial permutations
+#'  * sd_diff: (optional) standard deviation of the difference between LR_expr
+#'  and rand_expr over all random spatial permutations
+#'  * z_score: (optional) z-score
+#'  * log2fc: LR_expr - rand_expr
+#'  * pvalue: p-value
+#'  * LR_cell_comb: cell type pair combination
+#'  * p.adj: adjusted p-value
+#'  * PI: significanc score: log2fc \* -log10(p.adj)
 #' }
 #' @export
-spatCellCellcomSpots <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    ave_celltype_exp,
-    spatial_network_name = "Delaunay_network",
-    cluster_column = "cell_ID",
-    random_iter = 1000,
-    feature_set_1,
-    feature_set_2,
-    min_observations = 2,
-    expression_values = c("normalized", "scaled", "custom"),
-    detailed = FALSE,
-    adjust_method = c(
-        "fdr", "bonferroni", "BH", "holm", "hochberg", "hommel",
-        "BY", "none"
-    ),
-    adjust_target = c("features", "cells"),
-    do_parallel = TRUE,
-    cores = NA,
-    set_seed = TRUE,
-    seed_number = 1234,
-    verbose = c("a little", "a lot", "none")) {
+spatCellCellcomSpots <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        ave_celltype_exp,
+        spatial_network_name = "Delaunay_network",
+        cluster_column = "cell_ID",
+        random_iter = 1000,
+        feature_set_1,
+        feature_set_2,
+        min_observations = 2,
+        expression_values = c("normalized", "scaled", "custom"),
+        detailed = FALSE,
+        adjust_method = c(
+            "fdr", "bonferroni", "BH", "holm", "hochberg", "hommel",
+            "BY", "none"
+        ),
+        adjust_target = c("features", "cells"),
+        do_parallel = TRUE,
+        cores = NA,
+        set_seed = TRUE,
+        seed_number = 1234,
+        verbose = c("a little", "a lot", "none")) {
     # data.table vars
     V1 <- V2 <- LR_cell_comb <- NULL
 
@@ -2138,7 +2352,9 @@ spatCellCellcomSpots <- function(gobject,
 
     # expression data
     values <- match.arg(
-        expression_values, choices = c("normalized", "scaled", "custom"))
+        expression_values,
+        choices = c("normalized", "scaled", "custom")
+    )
     expr_residual <- .cal_expr_residual(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -2156,7 +2372,8 @@ spatCellCellcomSpots <- function(gobject,
 
     # select overlapped spots
     intersect_cell_IDs <- intersect(
-        colnames(expr_residual), colnames(proximityMat))
+        colnames(expr_residual), colnames(proximityMat)
+    )
     expr_residual <- expr_residual[, intersect_cell_IDs]
     proximityMat <- proximityMat[, intersect_cell_IDs]
 
@@ -2174,17 +2391,19 @@ spatCellCellcomSpots <- function(gobject,
 
     # check feature list
     LR_comb <- data.table::data.table(
-        ligand = feature_set_1, receptor = feature_set_2)
+        ligand = feature_set_1, receptor = feature_set_2
+    )
     # check LR pair not captured in giotto object
     LR_out <- LR_comb[!LR_comb$ligand %in% rownames(
-        expr_residual) | !LR_comb$receptor %in% rownames(expr_residual)]
+        expr_residual
+    ) | !LR_comb$receptor %in% rownames(expr_residual)]
 
     if (dim(LR_out)[1] > 0) {
-        message("Ligand or receptor were removed after computing expresion 
+        message("Ligand or receptor were removed after computing expresion
               residual.")
         print(LR_out)
-        LR_comb <- LR_comb[LR_comb$ligand %in% rownames(expr_residual) & 
-                                LR_comb$receptor %in% rownames(expr_residual)]
+        LR_comb <- LR_comb[LR_comb$ligand %in% rownames(expr_residual) &
+            LR_comb$receptor %in% rownames(expr_residual)]
         feature_set_1 <- LR_comb$ligand
         feature_set_2 <- LR_comb$receptor
     }
@@ -2192,50 +2411,56 @@ spatCellCellcomSpots <- function(gobject,
     ## get all combinations between cell types
     combn_DT <- data.table::data.table(LR_cell_comb = rownames(proximityMat))
     combn_DT[, V1 := strsplit(
-        LR_cell_comb, "--")[[1]][1], by = 1:nrow(combn_DT)]
+        LR_cell_comb, "--"
+    )[[1]][1], by = seq_len(nrow(combn_DT))]
     combn_DT[, V2 := strsplit(
-        LR_cell_comb, "--")[[1]][2], by = 1:nrow(combn_DT)]
+        LR_cell_comb, "--"
+    )[[1]][2], by = seq_len(nrow(combn_DT))]
 
     ## parallel option ##
     if (do_parallel == TRUE) {
         savelist <- lapply_flex(
-            X = 1:nrow(combn_DT), cores = cores, fun = function(row) {
-            cell_type_1 <- combn_DT[row][["V1"]]
-            cell_type_2 <- combn_DT[row][["V2"]]
-
-
-            specific_scores <- .specific_CCCScores_spots(
-                gobject = gobject,
-                spat_unit = spat_unit,
-                feat_type = feat_type,
-                expr_residual = expr_residual,
-                dwls_values = dwls_values,
-                proximityMat = proximityMat,
-                random_iter = random_iter,
-                cell_type_1 = cell_type_1,
-                cell_type_2 = cell_type_2,
-                feature_set_1 = feature_set_1,
-                feature_set_2 = feature_set_2,
-                min_observations = min_observations,
-                detailed = detailed,
-                adjust_method = adjust_method,
-                adjust_target = adjust_target,
-                set_seed = set_seed,
-                seed_number = seed_number
-            )
-        })
+            X = seq_len(nrow(combn_DT)), cores = cores, fun = function(row) {
+                cell_type_1 <- combn_DT[row][["V1"]]
+                cell_type_2 <- combn_DT[row][["V2"]]
+
+
+                specific_scores <- .specific_CCCScores_spots(
+                    gobject = gobject,
+                    spat_unit = spat_unit,
+                    feat_type = feat_type,
+                    expr_residual = expr_residual,
+                    dwls_values = dwls_values,
+                    proximityMat = proximityMat,
+                    random_iter = random_iter,
+                    cell_type_1 = cell_type_1,
+                    cell_type_2 = cell_type_2,
+                    feature_set_1 = feature_set_1,
+                    feature_set_2 = feature_set_2,
+                    min_observations = min_observations,
+                    detailed = detailed,
+                    adjust_method = adjust_method,
+                    adjust_target = adjust_target,
+                    set_seed = set_seed,
+                    seed_number = seed_number
+                )
+            }
+        )
     } else {
         ## for loop over all combinations ##
         savelist <- list()
         countdown <- nrow(combn_DT)
 
-        for (row in 1:nrow(combn_DT)) {
+        for (row in seq_len(nrow(combn_DT))) {
             cell_type_1 <- combn_DT[row][["V1"]]
             cell_type_2 <- combn_DT[row][["V2"]]
 
-            if (verbose == "a little" | verbose == "a lot") 
-                cat("PROCESS nr ", countdown, ": ", 
-                    cell_type_1, " and ", cell_type_2)
+            if (verbose == "a little" | verbose == "a lot") {
+                cat(
+                    "PROCESS nr ", countdown, ": ",
+                    cell_type_1, " and ", cell_type_2
+                )
+            }
 
             specific_scores <- .specific_CCCScores_spots(
                 gobject = gobject,
diff --git a/R/spatial_interaction_visuals.R b/R/spatial_interaction_visuals.R
index 136d12c26..15769e7d9 100644
--- a/R/spatial_interaction_visuals.R
+++ b/R/spatial_interaction_visuals.R
@@ -10,40 +10,46 @@
 #' @returns ggplot barplot
 #' @details This function creates a barplot that shows the  spatial proximity
 #'  enrichment or depletion of cell type pairs.
-#'  @examples
-#'  g <- GiottoData::loadGiottoMini("visium")
-#'  
-#'  cellProximityBarplot(gobject = g, 
-#'  CPscore = cellProximityEnrichment(g, cluster_column = "leiden_clus"))
+#' @examples
+#' g <- GiottoData::loadGiottoMini("visium")
+#'
+#' cellProximityBarplot(
+#'     gobject = g,
+#'     CPscore = cellProximityEnrichment(g, cluster_column = "leiden_clus")
+#' )
 #' @export
-cellProximityBarplot <- function(gobject,
-    CPscore,
-    min_orig_ints = 5,
-    min_sim_ints = 5,
-    p_val = 0.05,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "cellProximityBarplot") {
+cellProximityBarplot <- function(
+        gobject,
+        CPscore,
+        min_orig_ints = 5,
+        min_sim_ints = 5,
+        p_val = 0.05,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "cellProximityBarplot") {
     table_mean_results_dc <- CPscore$enrichm_res
 
     ## filter to remove low number of cell-cell proximity interactions ##
 
     # data.table variables
-    original <- simulations <- p_higher_orig <- p_lower_orig <- enrichm <- 
+    original <- simulations <- p_higher_orig <- p_lower_orig <- enrichm <-
         type_int <- unified_int <- NULL
 
     table_mean_results_dc_filter <- table_mean_results_dc[
-        original >= min_orig_ints & simulations >= min_sim_ints, ]
+        original >= min_orig_ints & simulations >= min_sim_ints,
+    ]
     table_mean_results_dc_filter <- table_mean_results_dc_filter[
-        p_higher_orig <= p_val | p_lower_orig <= p_val, ]
+        p_higher_orig <= p_val | p_lower_orig <= p_val,
+    ]
 
     pl <- ggplot2::ggplot()
     pl <- pl + ggplot2::geom_bar(
-        data = table_mean_results_dc_filter, 
-        ggplot2::aes(x = unified_int, y = enrichm, fill = type_int), 
-        stat = "identity", show.legend = FALSE)
+        data = table_mean_results_dc_filter,
+        ggplot2::aes(x = unified_int, y = enrichm, fill = type_int),
+        stat = "identity", show.legend = FALSE
+    )
     pl <- pl + ggplot2::coord_flip()
     pl <- pl + ggplot2::theme_bw()
     pl <- pl + ggplot2::labs(y = "enrichment/depletion")
@@ -51,18 +57,22 @@ cellProximityBarplot <- function(gobject,
 
     bpl <- ggplot2::ggplot()
     bpl <- bpl + ggplot2::geom_bar(
-        data = table_mean_results_dc_filter, 
-        ggplot2::aes(x = unified_int, y = original, fill = type_int), 
-        stat = "identity", show.legend = TRUE)
+        data = table_mean_results_dc_filter,
+        ggplot2::aes(x = unified_int, y = original, fill = type_int),
+        stat = "identity", show.legend = TRUE
+    )
     bpl <- bpl + ggplot2::coord_flip()
     bpl <- bpl + ggplot2::theme_bw() + ggplot2::theme(
-        axis.text.y = element_blank())
+        axis.text.y = element_blank()
+    )
     bpl <- bpl + ggplot2::labs(y = "# of interactions")
     bpl
 
     combo_plot <- cowplot::plot_grid(
-        pl, bpl, ncol = 2, rel_heights = c(1), 
-        rel_widths = c(3, 1.5), align = "h")
+        pl, bpl,
+        ncol = 2, rel_heights = c(1),
+        rel_widths = c(3, 1.5), align = "h"
+    )
 
     # output plot
     return(GiottoVisuals::plot_output_handler(
@@ -85,7 +95,7 @@ cellProximityBarplot <- function(gobject,
 #' @param CPscore CPscore, output from cellProximityEnrichment()
 #' @param scale scale cell-cell proximity interaction scores
 #' @param order_cell_types order cell types based on enrichment correlation
-#' @param color_breaks numerical vector of length 3 to represent min, mean 
+#' @param color_breaks numerical vector of length 3 to represent min, mean
 #' and maximum
 #' @param color_names character color vector of length 3
 #' @returns ggplot heatmap
@@ -93,50 +103,60 @@ cellProximityBarplot <- function(gobject,
 #'  enrichment or depletion of cell type pairs.
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' x <- cellProximityEnrichment(g, cluster_column = "leiden_clus")
-#' 
+#'
 #' cellProximityHeatmap(gobject = g, CPscore = x)
 #' @export
-cellProximityHeatmap <- function(gobject,
-    CPscore,
-    scale = TRUE,
-    order_cell_types = TRUE,
-    color_breaks = NULL,
-    color_names = NULL,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "cellProximityHeatmap") {
+cellProximityHeatmap <- function(
+        gobject,
+        CPscore,
+        scale = TRUE,
+        order_cell_types = TRUE,
+        color_breaks = NULL,
+        color_names = NULL,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "cellProximityHeatmap") {
     enrich_res <- CPscore$enrichm_res
 
     # data.table variables
     first_type <- second_type <- unified_int <- NULL
 
     enrich_res[, first_type := strsplit(
-        x = as.character(unified_int), split = "--")[[1]][1], 
-        by = 1:nrow(enrich_res)]
+        x = as.character(unified_int), split = "--"
+    )[[1]][1],
+    by = seq_len(nrow(enrich_res))
+    ]
     enrich_res[, second_type := strsplit(
-        x = as.character(unified_int), split = "--")[[1]][2], 
-        by = 1:nrow(enrich_res)]
+        x = as.character(unified_int), split = "--"
+    )[[1]][2],
+    by = seq_len(nrow(enrich_res))
+    ]
 
     # create matrix
     enrich_mat <- data.table::dcast.data.table(
-        data = enrich_res, 
-        formula = first_type ~ second_type, 
-        value.var = "enrichm")
+        data = enrich_res,
+        formula = first_type ~ second_type,
+        value.var = "enrichm"
+    )
     matrix_d <- as.matrix(enrich_mat[, -1])
     rownames(matrix_d) <- as.vector(enrich_mat[[1]])
     t_matrix_d <- t_flex(matrix_d)
 
     # fill in NAs based on values in upper and lower matrix triangle
     t_matrix_d[upper.tri(t_matrix_d)][is.na(t_matrix_d[
-        upper.tri(t_matrix_d)])] <- matrix_d[upper.tri(matrix_d)][
-            is.na(t_matrix_d[upper.tri(t_matrix_d)])]
+        upper.tri(t_matrix_d)
+    ])] <- matrix_d[upper.tri(matrix_d)][
+        is.na(t_matrix_d[upper.tri(t_matrix_d)])
+    ]
     t_matrix_d[lower.tri(t_matrix_d)][is.na(t_matrix_d[
-        lower.tri(t_matrix_d)])] <- matrix_d[lower.tri(matrix_d)][
-            is.na(t_matrix_d[lower.tri(t_matrix_d)])]
+        lower.tri(t_matrix_d)
+    ])] <- matrix_d[lower.tri(matrix_d)][
+        is.na(t_matrix_d[lower.tri(t_matrix_d)])
+    ]
     t_matrix_d[is.na(t_matrix_d)] <- 0
     final_matrix <- t_matrix_d
 
@@ -145,7 +165,8 @@ cellProximityHeatmap <- function(gobject,
         final_matrix <- t_flex(scale(t_flex(final_matrix)))
         final_matrix <- t_flex(final_matrix)
         final_matrix[lower.tri(final_matrix)] <- t_flex(final_matrix)[
-            lower.tri(final_matrix)]
+            lower.tri(final_matrix)
+        ]
     }
 
     # order cell types
@@ -171,17 +192,19 @@ cellProximityHeatmap <- function(gobject,
         }
 
         heatm <- ComplexHeatmap::Heatmap(
-            matrix = final_matrix, 
-            cluster_rows = FALSE, 
+            matrix = final_matrix,
+            cluster_rows = FALSE,
             cluster_columns = FALSE,
             col = GiottoVisuals::colorRamp2(
-                breaks = color_breaks, colors = color_names)
+                breaks = color_breaks, colors = color_names
+            )
         )
     } else {
         heatm <- ComplexHeatmap::Heatmap(
-            matrix = final_matrix, 
-            cluster_rows = FALSE, 
-            cluster_columns = FALSE)
+            matrix = final_matrix,
+            cluster_rows = FALSE,
+            cluster_columns = FALSE
+        )
     }
 
     return(plot_output_handler(
@@ -208,9 +231,9 @@ cellProximityHeatmap <- function(gobject,
 #' @param color_depletion color for depleted cell-cell interactions
 #' @param color_enrichment color for enriched cell-cell interactions
 #' @param rescale_edge_weights rescale edge weights (boolean)
-#' @param edge_weight_range_depletion numerical vector of length 2 to rescale 
+#' @param edge_weight_range_depletion numerical vector of length 2 to rescale
 #' depleted edge weights
-#' @param edge_weight_range_enrichment numerical vector of length 2 to rescale 
+#' @param edge_weight_range_enrichment numerical vector of length 2 to rescale
 #' enriched edge weights
 #' @param layout layout algorithm to use to draw nodes and edges
 #' @param only_show_enrichment_edges show only the enriched pairwise scores
@@ -223,31 +246,32 @@ cellProximityHeatmap <- function(gobject,
 #'  enrichment or depletion of cell type pairs.
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' x <- cellProximityEnrichment(g, cluster_column = "leiden_clus")
-#' 
+#'
 #' cellProximityNetwork(gobject = g, CPscore = x)
 #' @export
-cellProximityNetwork <- function(gobject,
-    CPscore,
-    remove_self_edges = FALSE,
-    self_loop_strength = 0.1,
-    color_depletion = "lightgreen",
-    color_enrichment = "red",
-    rescale_edge_weights = TRUE,
-    edge_weight_range_depletion = c(0.1, 1),
-    edge_weight_range_enrichment = c(1, 5),
-    layout = c("Fruchterman", "DrL", "Kamada-Kawai"),
-    only_show_enrichment_edges = FALSE,
-    edge_width_range = c(0.1, 2),
-    node_size = 4,
-    node_color_code = NULL,
-    node_text_size = 6,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "cellProximityNetwork") {
+cellProximityNetwork <- function(
+        gobject,
+        CPscore,
+        remove_self_edges = FALSE,
+        self_loop_strength = 0.1,
+        color_depletion = "lightgreen",
+        color_enrichment = "red",
+        rescale_edge_weights = TRUE,
+        edge_weight_range_depletion = c(0.1, 1),
+        edge_weight_range_enrichment = c(1, 5),
+        layout = c("Fruchterman", "DrL", "Kamada-Kawai"),
+        only_show_enrichment_edges = FALSE,
+        edge_width_range = c(0.1, 2),
+        node_size = 4,
+        node_color_code = NULL,
+        node_text_size = 6,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "cellProximityNetwork") {
     # extract scores
 
     # data.table variables
@@ -255,17 +279,27 @@ cellProximityNetwork <- function(gobject,
 
     CPscores <- CPscore[["enrichm_res"]]
     CPscores[, cell_1 := strsplit(
-        as.character(unified_int), split = "--")[[1]][1], by = 1:nrow(CPscores)]
+        as.character(unified_int),
+        split = "--"
+    )[[1]][1],
+    by = seq_len(nrow(CPscores))
+    ]
     CPscores[, cell_2 := strsplit(
-        as.character(unified_int), split = "--")[[1]][2], by = 1:nrow(CPscores)]
+        as.character(unified_int),
+        split = "--"
+    )[[1]][2],
+    by = seq_len(nrow(CPscores))
+    ]
 
     # create igraph with enrichm as weight edges
     igd <- igraph::graph_from_data_frame(
-        d = CPscores[, c("cell_1", "cell_2", "enrichm")], directed = FALSE)
+        d = CPscores[, c("cell_1", "cell_2", "enrichm")], directed = FALSE
+    )
 
     if (remove_self_edges == TRUE) {
         igd <- igraph::simplify(
-            graph = igd, remove.loops = TRUE, remove.multiple = FALSE)
+            graph = igd, remove.loops = TRUE, remove.multiple = FALSE
+        )
     }
 
     edges_sizes <- igraph::get.edge.attribute(igd, "enrichm")
@@ -275,9 +309,11 @@ cellProximityNetwork <- function(gobject,
     # rescale if wanted
     if (rescale_edge_weights == TRUE) {
         pos_edges_sizes_resc <- scales::rescale(
-            x = post_edges_sizes, to = edge_weight_range_enrichment)
+            x = post_edges_sizes, to = edge_weight_range_enrichment
+        )
         neg_edges_sizes_resc <- scales::rescale(
-            x = neg_edges_sizes, to = edge_weight_range_depletion)
+            x = neg_edges_sizes, to = edge_weight_range_depletion
+        )
         edges_sizes_resc <- c(pos_edges_sizes_resc, neg_edges_sizes_resc)
     } else {
         edges_sizes_resc <- c(post_edges_sizes, neg_edges_sizes)
@@ -298,15 +334,18 @@ cellProximityNetwork <- function(gobject,
         }
     } else {
         layout <- match.arg(
-            arg = layout, choices = c("Fruchterman", "DrL", "Kamada-Kawai"))
+            arg = layout, choices = c("Fruchterman", "DrL", "Kamada-Kawai")
+        )
     }
 
     igd <- igraph::set.edge.attribute(
-        graph = igd, index = igraph::E(igd), name = "color", 
-        value = edges_colors)
+        graph = igd, index = igraph::E(igd), name = "color",
+        value = edges_colors
+    )
     igd <- igraph::set.edge.attribute(
-        graph = igd, index = igraph::E(igd), name = "size", 
-        value = as.numeric(edges_sizes_resc))
+        graph = igd, index = igraph::E(igd), name = "size",
+        value = as.numeric(edges_sizes_resc)
+    )
 
     ## only show attractive edges
     if (only_show_enrichment_edges == TRUE) {
@@ -321,13 +360,16 @@ cellProximityNetwork <- function(gobject,
     ## get coordinates layouts
     if (layout == "Fruchterman") {
         coords <- igraph::layout_with_fr(
-            graph = igd, weights = edges_sizes_resc)
+            graph = igd, weights = edges_sizes_resc
+        )
     } else if (layout == "DrL") {
         coords <- igraph::layout_with_drl(
-            graph = igd, weights = edges_sizes_resc)
+            graph = igd, weights = edges_sizes_resc
+        )
     } else if (layout == "Kamada-Kawai") {
         coords <- igraph::layout_with_kk(
-            graph = igd, weights = edges_sizes_resc)
+            graph = igd, weights = edges_sizes_resc
+        )
     } else {
         stop("Currently no other layouts have been implemented")
     }
@@ -335,25 +377,36 @@ cellProximityNetwork <- function(gobject,
     ## create plot
     gpl <- ggraph::ggraph(graph = igd, layout = coords)
     gpl <- gpl + ggraph::geom_edge_link(
-        ggplot2::aes(color = factor(color), 
-                     edge_width = size, edge_alpha = size), 
-        show.legend = FALSE)
+        ggplot2::aes(
+            color = factor(color),
+            edge_width = size, edge_alpha = size
+        ),
+        show.legend = FALSE
+    )
 
     if (remove_self_edges == FALSE) {
         gpl <- gpl + ggraph::geom_edge_loop(
-            ggplot2::aes(color = factor(color), edge_width = size, 
-                         edge_alpha = size, strength = self_loop_strength), 
-            show.legend = FALSE)
+            ggplot2::aes(
+                color = factor(color), edge_width = size,
+                edge_alpha = size, strength = self_loop_strength
+            ),
+            show.legend = FALSE
+        )
     }
 
     gpl <- gpl + ggraph::scale_edge_color_manual(
-        values = c("enriched" = color_enrichment, "depleted" = color_depletion))
+        values = c("enriched" = color_enrichment, "depleted" = color_depletion)
+    )
     gpl <- gpl + ggraph::scale_edge_width(range = edge_width_range)
     gpl <- gpl + ggraph::scale_edge_alpha(range = c(0.1, 1))
     gpl <- gpl + ggraph::geom_node_text(
-        ggplot2::aes(label = name), repel = TRUE, size = node_text_size)
+        ggplot2::aes(label = name),
+        repel = TRUE, size = node_text_size
+    )
     gpl <- gpl + ggraph::geom_node_point(
-        ggplot2::aes(color = name), size = node_size)
+        ggplot2::aes(color = name),
+        size = node_size
+    )
     if (!is.null(node_color_code)) {
         gpl <- gpl + ggplot2::scale_color_manual(values = node_color_code)
     }
@@ -390,52 +443,59 @@ cellProximityNetwork <- function(gobject,
 NULL
 
 
-#' @describeIn cellProximityVisPlot_internals Visualize 2D cell-cell 
+#' @describeIn cellProximityVisPlot_internals Visualize 2D cell-cell
 #' interactions according to spatial coordinates in ggplot mode
 #' @keywords internal
-.cellProximityVisPlot_2D_ggplot <- function(gobject,
-    interaction_name = NULL,
-    cluster_column = NULL,
-    sdimx = NULL,
-    sdimy = NULL,
-    cell_color = NULL,
-    cell_color_code = NULL,
-    color_as_factor = TRUE,
-    show_other_cells = FALSE,
-    show_network = FALSE,
-    show_other_network = FALSE,
-    network_color = NULL,
-    spatial_network_name = "Delaunay_network",
-    show_grid = FALSE,
-    grid_color = NULL,
-    spatial_grid_name = "spatial_grid",
-    coord_fix_ratio = 1,
-    show_legend = TRUE,
-    point_size_select = 2,
-    point_select_border_col = "black",
-    point_select_border_stroke = 0.05,
-    point_size_other = 1,
-    point_alpha_other = 0.3,
-    point_other_border_col = "lightgrey",
-    point_other_border_stroke = 0.01,
-    ...) {
+.cellProximityVisPlot_2D_ggplot <- function(
+        gobject,
+        interaction_name = NULL,
+        cluster_column = NULL,
+        sdimx = NULL,
+        sdimy = NULL,
+        cell_color = NULL,
+        cell_color_code = NULL,
+        color_as_factor = TRUE,
+        show_other_cells = FALSE,
+        show_network = FALSE,
+        show_other_network = FALSE,
+        network_color = NULL,
+        spatial_network_name = "Delaunay_network",
+        show_grid = FALSE,
+        grid_color = NULL,
+        spatial_grid_name = "spatial_grid",
+        coord_fix_ratio = 1,
+        show_legend = TRUE,
+        point_size_select = 2,
+        point_select_border_col = "black",
+        point_select_border_stroke = 0.05,
+        point_size_other = 1,
+        point_alpha_other = 0.3,
+        point_other_border_col = "lightgrey",
+        point_other_border_stroke = 0.01,
+        ...) {
     # data.table variables
-    unified_int <- sdimx_begin <- sdimy_begin <- sdimx_end <- sdimy_end <- 
+    unified_int <- sdimx_begin <- sdimy_begin <- sdimx_end <- sdimy_end <-
         x_start <- x_end <- NULL
     y_start <- y_end <- cell_ID <- NULL
 
     if (is.null(interaction_name)) {
-        stop("you need to specific at least one interaction name, run 
+        stop("you need to specific at least one interaction name, run
             cellProximityEnrichment")
     }
 
 
-    cell_locations <- getSpatialLocations(gobject = gobject,
-                                          output = "data.table")
-    spatial_grid <- getSpatialGrid(gobject = gobject,
-                                   name = spatial_grid_name)
-    cell_metadata <- getCellMetadata(gobject = gobject,
-                                    output = "data.table")
+    cell_locations <- getSpatialLocations(
+        gobject = gobject,
+        output = "data.table"
+    )
+    spatial_grid <- getSpatialGrid(
+        gobject = gobject,
+        name = spatial_grid_name
+    )
+    cell_metadata <- getCellMetadata(
+        gobject = gobject,
+        output = "data.table"
+    )
 
 
 
@@ -454,7 +514,8 @@ NULL
     if (show_other_cells) {
         CellType <- strsplit(interaction_name, "--")
         all_cell_IDs <- cell_metadata[cell_metadata[[
-            cluster_column]] == CellType[[1]][1] |
+            cluster_column
+        ]] == CellType[[1]][1] |
             cell_metadata[[cluster_column]] == CellType[[1]][2], ]$cell_ID
         other_cell_IDs <- setdiff(all_cell_IDs, cell_IDs_to_keep)
     }
@@ -465,13 +526,15 @@ NULL
         cell_locations_metadata <- cell_locations
     } else {
         cell_locations_metadata <- merge(
-            cell_locations, cell_metadata, by = "cell_ID")
+            cell_locations, cell_metadata,
+            by = "cell_ID"
+        )
     }
 
 
     # first 2 dimensions need to be defined
     if (is.null(sdimx) | is.null(sdimy)) {
-        message("first and second dimenion need to be defined, default is 
+        message("first and second dimenion need to be defined, default is
                 first 2\n")
         sdimx <- "sdimx"
         sdimy <- "sdimy"
@@ -485,15 +548,19 @@ NULL
         if (show_other_network) {
             pl <- pl + ggplot2::geom_segment(
                 data = spatial_network[!unified_int %in% interaction_name],
-                aes(x = sdimx_begin, y = sdimy_begin, xend = sdimx_end, 
-                    yend = sdimy_end),
+                aes(
+                    x = sdimx_begin, y = sdimy_begin, xend = sdimx_end,
+                    yend = sdimy_end
+                ),
                 color = "lightgrey", size = 0.5, alpha = 0.5
             )
         }
         pl <- pl + ggplot2::geom_segment(
             data = spatial_network[unified_int %in% interaction_name],
-            aes(x = sdimx_begin, y = sdimy_begin, xend = sdimx_end, 
-                yend = sdimy_end),
+            aes(
+                x = sdimx_begin, y = sdimy_begin, xend = sdimx_end,
+                yend = sdimy_end
+            ),
             color = network_color, size = 0.5, alpha = 0.5
         )
     }
@@ -501,7 +568,7 @@ NULL
     if (!is.null(spatial_grid) & show_grid == TRUE) {
         if (is.null(grid_color)) grid_color <- "black"
         pl <- pl + ggplot2::geom_rect(
-            data = spatial_grid, 
+            data = spatial_grid,
             aes(xmin = x_start, xmax = x_end, ymin = y_start, ymax = y_end),
             color = grid_color, fill = NA
         )
@@ -511,22 +578,22 @@ NULL
     if (is.null(cell_color)) {
         cell_color <- "lightblue"
         pl <- pl + ggplot2::geom_point(
-            data = cell_locations[!cell_ID %in% cell_IDs_to_keep], 
+            data = cell_locations[!cell_ID %in% cell_IDs_to_keep],
             aes_string(x = sdimx, y = sdimy),
-            show.legend = show_legend, shape = 21, fill = "lightgrey", 
+            show.legend = show_legend, shape = 21, fill = "lightgrey",
             size = point_size_other
         )
         pl <- pl + ggplot2::geom_point(
-            data = cell_locations[cell_ID %in% cell_IDs_to_keep], 
+            data = cell_locations[cell_ID %in% cell_IDs_to_keep],
             aes_string(x = sdimx, y = sdimy),
-            show.legend = show_legend, shape = 21, fill = cell_color, 
+            show.legend = show_legend, shape = 21, fill = cell_color,
             size = point_size_select
         )
         if (show_other_cells) {
             pl <- pl + ggplot2::geom_point(
-                data = cell_locations[cell_ID %in% other_cell_IDs], 
+                data = cell_locations[cell_ID %in% other_cell_IDs],
                 aes_string(x = sdimx, y = sdimy),
-                show.legend = show_legend, shape = 21, fill = cell_color, 
+                show.legend = show_legend, shape = 21, fill = cell_color,
                 alpha = point_alpha_other,
                 size = point_size_select * 0.5
             )
@@ -539,24 +606,24 @@ NULL
             }
 
             pl <- pl + ggplot2::geom_point(
-                data = cell_locations_metadata[!cell_ID %in% cell_IDs_to_keep], 
+                data = cell_locations_metadata[!cell_ID %in% cell_IDs_to_keep],
                 aes_string(x = sdimx, y = sdimy),
                 fill = "lightgrey", shape = 21, size = point_size_other,
-                color = point_other_border_col, 
+                color = point_other_border_col,
                 stroke = point_other_border_stroke
             )
             pl <- pl + ggplot2::geom_point(
-                data = cell_locations_metadata[cell_ID %in% cell_IDs_to_keep], 
+                data = cell_locations_metadata[cell_ID %in% cell_IDs_to_keep],
                 aes_string(x = sdimx, y = sdimy, fill = cell_color),
                 show.legend = show_legend, shape = 21, size = point_size_select,
-                color = point_select_border_col, 
+                color = point_select_border_col,
                 stroke = point_select_border_stroke
             )
             if (show_other_cells) {
                 pl <- pl + ggplot2::geom_point(
                     data = cell_locations_metadata[cell_ID %in% other_cell_IDs],
                     aes_string(x = sdimx, y = sdimy, fill = cell_color),
-                    show.legend = show_legend, shape = 21, 
+                    show.legend = show_legend, shape = 21,
                     alpha = point_alpha_other,
                     size = point_size_select * 0.5
                 )
@@ -569,7 +636,8 @@ NULL
             } else if (color_as_factor == TRUE) {
                 number_colors <- length(unique(factor_data))
                 cell_color_code <- set_default_color_discrete_cell(
-                    instrs = instructions(gobject))(n = number_colors)
+                    instrs = instructions(gobject)
+                )(n = number_colors)
                 names(cell_color_code) <- unique(factor_data)
                 pl <- pl + ggplot2::scale_fill_manual(values = cell_color_code)
             } else if (color_as_factor == FALSE) {
@@ -586,19 +654,20 @@ NULL
         } else {
             pl <- pl + ggplot2::geom_point(
                 data = cell_locations_metadata[
-                    !cell_ID %in% cell_IDs_to_keep], 
+                    !cell_ID %in% cell_IDs_to_keep
+                ],
                 aes_string(x = sdimx, y = sdimy),
-                show.legend = show_legend, shape = 21, 
+                show.legend = show_legend, shape = 21,
                 fill = "lightgrey", size = point_size_other,
-                color = point_other_border_col, 
+                color = point_other_border_col,
                 stroke = point_other_border_stroke
             )
             pl <- pl + ggplot2::geom_point(
-                data = cell_locations_metadata[cell_ID %in% cell_IDs_to_keep], 
+                data = cell_locations_metadata[cell_ID %in% cell_IDs_to_keep],
                 aes_string(x = sdimx, y = sdimy),
-                show.legend = show_legend, shape = 21, fill = cell_color, 
+                show.legend = show_legend, shape = 21, fill = cell_color,
                 size = point_size_select,
-                color = point_select_border_col, 
+                color = point_select_border_col,
                 stroke = point_select_border_stroke
             )
         }
@@ -622,39 +691,40 @@ NULL
 
 
 
-#' @describeIn cellProximityVisPlot_internals Visualize 2D cell-cell 
+#' @describeIn cellProximityVisPlot_internals Visualize 2D cell-cell
 #' interactions according to spatial coordinates in plotly mode
 #' @keywords internal
-.cellProximityVisPlot_2D_plotly <- function(gobject,
-    interaction_name = NULL,
-    cluster_column = NULL,
-    sdimx = NULL,
-    sdimy = NULL,
-    cell_color = NULL,
-    cell_color_code = NULL,
-    color_as_factor = TRUE,
-    show_other_cells = FALSE,
-    show_network = FALSE,
-    show_other_network = FALSE,
-    network_color = NULL,
-    spatial_network_name = "Delaunay_network",
-    show_grid = FALSE,
-    grid_color = NULL,
-    spatial_grid_name = "spatial_grid",
-    show_legend = TRUE,
-    point_size_select = 2,
-    point_size_other = 1,
-    point_alpha_other = 0.3,
-    axis_scale = c("cube", "real", "custom"),
-    custom_ratio = NULL,
-    x_ticks = NULL,
-    y_ticks = NULL,
-    ...) {
+.cellProximityVisPlot_2D_plotly <- function(
+        gobject,
+        interaction_name = NULL,
+        cluster_column = NULL,
+        sdimx = NULL,
+        sdimy = NULL,
+        cell_color = NULL,
+        cell_color_code = NULL,
+        color_as_factor = TRUE,
+        show_other_cells = FALSE,
+        show_network = FALSE,
+        show_other_network = FALSE,
+        network_color = NULL,
+        spatial_network_name = "Delaunay_network",
+        show_grid = FALSE,
+        grid_color = NULL,
+        spatial_grid_name = "spatial_grid",
+        show_legend = TRUE,
+        point_size_select = 2,
+        point_size_other = 1,
+        point_alpha_other = 0.3,
+        axis_scale = c("cube", "real", "custom"),
+        custom_ratio = NULL,
+        x_ticks = NULL,
+        y_ticks = NULL,
+        ...) {
     # data.table variables
     cell_ID <- unified_int <- NULL
 
     if (is.null(interaction_name)) {
-        stop("you need to specific at least one interaction name, run 
+        stop("you need to specific at least one interaction name, run
             cellProximityEnrichment")
     }
 
@@ -665,18 +735,21 @@ NULL
 
 
     spatial_network <- annotateSpatialNetwork(
-        gobject = gobject, 
-        spatial_network_name = spatial_network_name, 
-        cluster_column = cluster_column)
+        gobject = gobject,
+        spatial_network_name = spatial_network_name,
+        cluster_column = cluster_column
+    )
 
     cell_IDs_to_keep <- unique(c(
-        spatial_network[unified_int %in% interaction_name]$to, 
-        spatial_network[unified_int %in% interaction_name]$from))
+        spatial_network[unified_int %in% interaction_name]$to,
+        spatial_network[unified_int %in% interaction_name]$from
+    ))
 
     if (show_other_cells) {
         CellType <- strsplit(interaction_name, "-")
         all_cell_IDs <- cell_metadata[cell_metadata[[
-            cluster_column]] == CellType[[1]][1] |
+            cluster_column
+        ]] == CellType[[1]][1] |
             cell_metadata[[cluster_column]] == CellType[[1]][2], ]$cell_ID
         other_cell_IDs <- setdiff(all_cell_IDs, cell_IDs_to_keep)
     }
@@ -686,7 +759,9 @@ NULL
         cell_locations_metadata <- cell_locations
     } else {
         cell_locations_metadata <- merge(
-            cell_locations, cell_metadata, by = "cell_ID")
+            cell_locations, cell_metadata,
+            by = "cell_ID"
+        )
     }
 
 
@@ -694,7 +769,7 @@ NULL
 
     # first 2 dimensions need to be defined
     if (is.null(sdimx) | is.null(sdimy)) {
-        message("first and second dimenion need to be defined, default is 
+        message("first and second dimenion need to be defined, default is
                 first 2")
         sdimx <- "sdimx"
         sdimy <- "sdimy"
@@ -773,12 +848,15 @@ NULL
         if (cell_color %in% colnames(cell_locations_metadata)) {
             if (is.null(cell_color_code)) {
                 number_colors <- length(unique(cell_locations_metadata[[
-                    cell_color]]))
+                    cell_color
+                ]]))
                 cell_color_code <- set_default_color_discrete_cell(
-                    instrs = instructions(gobject))(n = number_colors)
+                    instrs = instructions(gobject)
+                )(n = number_colors)
             }
             cell_locations_metadata[[cell_color]] <- as.factor(
-                cell_locations_metadata[[cell_color]])
+                cell_locations_metadata[[cell_color]]
+            )
 
             pl <- pl %>% plotly::add_trace(
                 type = "scatter", mode = "markers",
@@ -786,7 +864,8 @@ NULL
                 data = cell_locations_metadata[cell_ID %in% cell_IDs_to_keep],
                 x = ~sdimx, y = ~sdimy,
                 color = cell_locations_metadata[
-                    cell_ID %in% cell_IDs_to_keep][[cell_color]],
+                    cell_ID %in% cell_IDs_to_keep
+                ][[cell_color]],
                 colors = cell_color_code,
                 marker = list(size = point_size_select)
             )
@@ -797,7 +876,8 @@ NULL
                     data = cell_locations_metadata[cell_ID %in% other_cell_IDs],
                     x = ~sdimx, y = ~sdimy,
                     color = cell_locations_metadata[
-                        cell_ID %in% other_cell_IDs][[cell_color]],
+                        cell_ID %in% other_cell_IDs
+                    ][[cell_color]],
                     colors = cell_color_code,
                     opacity = point_alpha_other,
                     marker = list(size = point_size_select * 0.7)
@@ -809,9 +889,10 @@ NULL
                 data = cell_locations_metadata[!cell_ID %in% cell_IDs_to_keep],
                 x = ~sdimx, y = ~sdimy,
                 marker = list(
-                    size = point_size_other, 
-                    color = "lightgray", 
-                    colors = "lightgray"),
+                    size = point_size_other,
+                    color = "lightgray",
+                    colors = "lightgray"
+                ),
                 opacity = point_alpha_other
             )
         } else {
@@ -824,9 +905,10 @@ NULL
             data = cell_locations_metadata[cell_ID %in% cell_IDs_to_keep],
             x = ~sdimx, y = ~sdimy,
             marker = list(
-                size = point_size_select, 
-                color = "lightblue", 
-                colors = "lightblue")
+                size = point_size_select,
+                color = "lightblue",
+                colors = "lightblue"
+            )
         )
         if (show_other_cells) {
             pl <- pl %>% plotly::add_trace(
@@ -835,9 +917,10 @@ NULL
                 x = ~sdimx, y = ~sdimy,
                 name = "selected cells outside network",
                 marker = list(
-                    size = point_size_select * 0.7, 
-                    color = "lightblue", 
-                    colors = "lightblue"),
+                    size = point_size_select * 0.7,
+                    color = "lightblue",
+                    colors = "lightblue"
+                ),
                 opacity = point_alpha_other
             )
         }
@@ -847,9 +930,10 @@ NULL
             data = cell_locations_metadata[!cell_ID %in% cell_IDs_to_keep],
             x = ~sdimx, y = ~sdimy,
             marker = list(
-                size = point_size_other, 
-                color = "lightgray", 
-                colors = "lightgray"),
+                size = point_size_other,
+                color = "lightgray",
+                colors = "lightgray"
+            ),
             opacity = point_alpha_other
         )
     }
@@ -869,41 +953,42 @@ NULL
 }
 
 
-#' @describeIn cellProximityVisPlot_internals Visualize 3D cell-cell 
+#' @describeIn cellProximityVisPlot_internals Visualize 3D cell-cell
 #' interactions according to spatial coordinates in plotly mode
 #' @keywords internal
-.cellProximityVisPlot_3D_plotly <- function(gobject,
-    interaction_name = NULL,
-    cluster_column = NULL,
-    sdimx = NULL,
-    sdimy = NULL,
-    sdimz = NULL,
-    cell_color = NULL,
-    cell_color_code = NULL,
-    color_as_factor = TRUE,
-    show_other_cells = FALSE,
-    show_network = FALSE,
-    show_other_network = FALSE,
-    network_color = NULL,
-    spatial_network_name = "Delaunay_network",
-    show_grid = FALSE,
-    grid_color = NULL,
-    spatial_grid_name = "spatial_grid",
-    show_legend = TRUE,
-    point_size_select = 2,
-    point_size_other = 1,
-    point_alpha_other = 0.5,
-    axis_scale = c("cube", "real", "custom"),
-    custom_ratio = NULL,
-    x_ticks = NULL,
-    y_ticks = NULL,
-    z_ticks = NULL,
-    ...) {
+.cellProximityVisPlot_3D_plotly <- function(
+        gobject,
+        interaction_name = NULL,
+        cluster_column = NULL,
+        sdimx = NULL,
+        sdimy = NULL,
+        sdimz = NULL,
+        cell_color = NULL,
+        cell_color_code = NULL,
+        color_as_factor = TRUE,
+        show_other_cells = FALSE,
+        show_network = FALSE,
+        show_other_network = FALSE,
+        network_color = NULL,
+        spatial_network_name = "Delaunay_network",
+        show_grid = FALSE,
+        grid_color = NULL,
+        spatial_grid_name = "spatial_grid",
+        show_legend = TRUE,
+        point_size_select = 2,
+        point_size_other = 1,
+        point_alpha_other = 0.5,
+        axis_scale = c("cube", "real", "custom"),
+        custom_ratio = NULL,
+        x_ticks = NULL,
+        y_ticks = NULL,
+        z_ticks = NULL,
+        ...) {
     # data.table variables
     cell_ID <- unified_int <- NULL
 
     if (is.null(interaction_name)) {
-        stop("you need to specific at least one interaction name, 
+        stop("you need to specific at least one interaction name,
             run cellProximityEnrichment")
     }
 
@@ -914,18 +999,23 @@ NULL
 
 
     spatial_network <- annotateSpatialNetwork(
-        gobject = gobject, 
-        spatial_network_name = spatial_network_name, 
-        cluster_column = cluster_column)
+        gobject = gobject,
+        spatial_network_name = spatial_network_name,
+        cluster_column = cluster_column
+    )
 
-    cell_IDs_to_keep <- unique(c(spatial_network[
-        unified_int %in% interaction_name]$to, 
-        spatial_network[unified_int %in% interaction_name]$from))
+    cell_IDs_to_keep <- unique(c(
+        spatial_network[
+            unified_int %in% interaction_name
+        ]$to,
+        spatial_network[unified_int %in% interaction_name]$from
+    ))
 
     if (show_other_cells) {
         CellType <- strsplit(interaction_name, "-")
         all_cell_IDs <- cell_metadata[cell_metadata[[
-            cluster_column]] == CellType[[1]][1] |
+            cluster_column
+        ]] == CellType[[1]][1] |
             cell_metadata[[cluster_column]] == CellType[[1]][2], ]$cell_ID
         other_cell_IDs <- setdiff(all_cell_IDs, cell_IDs_to_keep)
     }
@@ -935,7 +1025,9 @@ NULL
         cell_locations_metadata <- cell_locations
     } else {
         cell_locations_metadata <- merge(
-            cell_locations, cell_metadata, by = "cell_ID")
+            cell_locations, cell_metadata,
+            by = "cell_ID"
+        )
     }
 
 
@@ -943,7 +1035,7 @@ NULL
 
     # first 2 dimensions need to be defined
     if (is.null(sdimx) | is.null(sdimy)) {
-        message("first and second dimenion need to be defined, default is 
+        message("first and second dimenion need to be defined, default is
                 first 2")
         sdimx <- "sdimx"
         sdimy <- "sdimy"
@@ -970,21 +1062,26 @@ NULL
         if (cell_color %in% colnames(cell_locations_metadata)) {
             if (is.null(cell_color_code)) {
                 number_colors <- length(unique(cell_locations_metadata[[
-                    cell_color]]))
+                    cell_color
+                ]]))
                 cell_color_code <- set_default_color_discrete_cell(
-                    instrs = instructions(gobject))(n = number_colors)
+                    instrs = instructions(gobject)
+                )(n = number_colors)
             }
             cell_locations_metadata[[cell_color]] <- as.factor(
-                cell_locations_metadata[[cell_color]])
+                cell_locations_metadata[[cell_color]]
+            )
 
             pl <- pl %>%
                 plotly::add_trace(
                     type = "scatter3d", mode = "markers",
                     data = cell_locations_metadata[
-                        cell_ID %in% cell_IDs_to_keep],
+                        cell_ID %in% cell_IDs_to_keep
+                    ],
                     x = ~sdimx, y = ~sdimy, z = ~sdimz,
                     color = cell_locations_metadata[
-                        cell_ID %in% cell_IDs_to_keep][[cell_color]],
+                        cell_ID %in% cell_IDs_to_keep
+                    ][[cell_color]],
                     colors = cell_color_code,
                     marker = list(size = point_size_select)
                 ) %>%
@@ -992,12 +1089,14 @@ NULL
                     type = "scatter3d", mode = "markers",
                     name = "unselected cells",
                     data = cell_locations_metadata[
-                        !cell_ID %in% cell_IDs_to_keep],
+                        !cell_ID %in% cell_IDs_to_keep
+                    ],
                     x = ~sdimx, y = ~sdimy, z = ~sdimz,
                     marker = list(
-                        size = point_size_other, 
-                        color = "lightgray", 
-                        colors = "lightgray"),
+                        size = point_size_other,
+                        color = "lightgray",
+                        colors = "lightgray"
+                    ),
                     opacity = point_alpha_other
                 )
             if (show_other_cells) {
@@ -1007,7 +1106,8 @@ NULL
                     data = cell_locations_metadata[cell_ID %in% other_cell_IDs],
                     x = ~sdimx, y = ~sdimy, z = ~sdimz,
                     color = cell_locations_metadata[
-                        cell_ID %in% other_cell_IDs][[cell_color]],
+                        cell_ID %in% other_cell_IDs
+                    ][[cell_color]],
                     colors = cell_color_code,
                     opacity = point_alpha_other,
                     marker = list(size = point_size_select * 0.7)
@@ -1024,9 +1124,10 @@ NULL
                 data = cell_locations_metadata[cell_ID %in% cell_IDs_to_keep],
                 x = ~sdimx, y = ~sdimy, z = ~sdimz,
                 marker = list(
-                    size = point_size_select, 
-                    color = "lightblue", 
-                    colors = "lightblue")
+                    size = point_size_select,
+                    color = "lightblue",
+                    colors = "lightblue"
+                )
             ) %>%
             plotly::add_trace(
                 type = "scatter3d", mode = "markers",
@@ -1034,9 +1135,10 @@ NULL
                 data = cell_locations_metadata[!cell_ID %in% cell_IDs_to_keep],
                 x = ~sdimx, y = ~sdimy, z = ~sdimz,
                 marker = list(
-                    size = point_size_other, 
-                    color = "lightgray", 
-                    colors = "lightgray"),
+                    size = point_size_other,
+                    color = "lightgray",
+                    colors = "lightgray"
+                ),
                 opacity = point_alpha_other
             )
         if (show_other_cells) {
@@ -1046,9 +1148,10 @@ NULL
                 x = ~sdimx, y = ~sdimy, z = ~sdimz,
                 name = "selected cells outside network",
                 marker = list(
-                    size = point_size_select * 0.7, 
-                    color = "lightblue", 
-                    colors = "lightblue"),
+                    size = point_size_select * 0.7,
+                    color = "lightblue",
+                    colors = "lightblue"
+                ),
                 opacity = point_alpha_other
             )
         }
@@ -1060,18 +1163,18 @@ NULL
         unselect_network <- spatial_network[!unified_int %in% interaction_name]
         select_network <- spatial_network[unified_int %in% interaction_name]
         pl <- pl %>% plotly::add_trace(
-            name = "sptial network", mode = "lines", 
+            name = "sptial network", mode = "lines",
             type = "scatter3d", opacity = 0.5,
             data = plotly_network(select_network),
-            x = ~x, y = ~y, z = ~z, inherit = FALSE, 
+            x = ~x, y = ~y, z = ~z, inherit = FALSE,
             line = list(color = network_color)
         )
         if (show_other_network == TRUE) {
             pl <- pl %>% plotly::add_trace(
-                name = "unselected sptial network", mode = "lines", 
+                name = "unselected sptial network", mode = "lines",
                 type = "scatter3d", opacity = 0.1,
                 data = plotly_network(unselect_network),
-                x = ~x, y = ~y, z = ~z, inherit = FALSE, 
+                x = ~x, y = ~y, z = ~z, inherit = FALSE,
                 line = list(color = "lightgray")
             )
         }
@@ -1093,7 +1196,7 @@ NULL
 
 #' @title cellProximityVisPlot
 #' @name cellProximityVisPlot
-#' @description Visualize cell-cell interactions according to spatial 
+#' @description Visualize cell-cell interactions according to spatial
 #' coordinates
 #' @param gobject giotto object
 #' @param interaction_name cell-cell interaction name
@@ -1134,43 +1237,46 @@ NULL
 #' g <- GiottoData::loadGiottoMini("visium")
 #' g <- createSpatialGrid(g, sdimx_stepsize = 5, sdimy_stepsize = 5)
 #' x <- cellProximityEnrichment(g, cluster_column = "leiden_clus")
-#' 
-#' cellProximityVisPlot(gobject = g, interaction_name = x,
-#' cluster_column = "leiden_clus", sdimx = "sdimx", sdimy = "sdimy")
+#'
+#' cellProximityVisPlot(
+#'     gobject = g, interaction_name = x,
+#'     cluster_column = "leiden_clus", sdimx = "sdimx", sdimy = "sdimy"
+#' )
 #' @export
-cellProximityVisPlot <- function(gobject,
-    interaction_name = NULL,
-    cluster_column = NULL,
-    sdimx = NULL,
-    sdimy = NULL,
-    sdimz = NULL,
-    cell_color = NULL,
-    cell_color_code = NULL,
-    color_as_factor = TRUE,
-    show_other_cells = FALSE,
-    show_network = FALSE,
-    show_other_network = FALSE,
-    network_color = NULL,
-    spatial_network_name = "Delaunay_network",
-    show_grid = FALSE,
-    grid_color = NULL,
-    spatial_grid_name = "spatial_grid",
-    coord_fix_ratio = 1,
-    show_legend = TRUE,
-    point_size_select = 2,
-    point_select_border_col = "black",
-    point_select_border_stroke = 0.05,
-    point_size_other = 1,
-    point_alpha_other = 0.3,
-    point_other_border_col = "lightgrey",
-    point_other_border_stroke = 0.01,
-    axis_scale = c("cube", "real", "custom"),
-    custom_ratio = NULL,
-    x_ticks = NULL,
-    y_ticks = NULL,
-    z_ticks = NULL,
-    plot_method = c("ggplot", "plotly"),
-    ...) {
+cellProximityVisPlot <- function(
+        gobject,
+        interaction_name = NULL,
+        cluster_column = NULL,
+        sdimx = NULL,
+        sdimy = NULL,
+        sdimz = NULL,
+        cell_color = NULL,
+        cell_color_code = NULL,
+        color_as_factor = TRUE,
+        show_other_cells = FALSE,
+        show_network = FALSE,
+        show_other_network = FALSE,
+        network_color = NULL,
+        spatial_network_name = "Delaunay_network",
+        show_grid = FALSE,
+        grid_color = NULL,
+        spatial_grid_name = "spatial_grid",
+        coord_fix_ratio = 1,
+        show_legend = TRUE,
+        point_size_select = 2,
+        point_select_border_col = "black",
+        point_select_border_stroke = 0.05,
+        point_size_other = 1,
+        point_alpha_other = 0.3,
+        point_other_border_col = "lightgrey",
+        point_other_border_stroke = 0.01,
+        axis_scale = c("cube", "real", "custom"),
+        custom_ratio = NULL,
+        x_ticks = NULL,
+        y_ticks = NULL,
+        z_ticks = NULL,
+        plot_method = c("ggplot", "plotly"),
+        ...) {
     ## decide plot method
     plot_method <- match.arg(plot_method, choices = c("ggplot", "plotly"))
     axis_scale <- match.arg(axis_scale, c("cube", "real", "custom"))
@@ -1178,7 +1284,7 @@ cellProximityVisPlot <- function(gobject,
 
     if (plot_method == "ggplot") {
         if (is.null(sdimx) | is.null(sdimy)) {
-            warning("plot_method = ggplot, but spatial dimensions for sdimx 
+            warning("plot_method = ggplot, but spatial dimensions for sdimx
             and sdimy for 2D plotting are not given. \n
             It will default to the 'sdimx' and 'sdimy'")
             sdimx <- "sdimx"
@@ -1186,7 +1292,7 @@ cellProximityVisPlot <- function(gobject,
         }
 
         if (length(c(sdimx, sdimy, sdimz)) == 3) {
-            warning("ggplot is not able to produce 3D plot! Please choose 
+            warning("ggplot is not able to produce 3D plot! Please choose
                     plotly method")
         }
         result <- .cellProximityVisPlot_2D_ggplot(
@@ -1309,7 +1415,7 @@ cellProximityVisPlot <- function(gobject,
 #' @param min_cells minimum number of source cell type
 #' @param min_cells_expr minimum expression level for source cell type
 #' @param min_int_cells minimum number of interacting neighbor cell type
-#' @param min_int_cells_expr minimum expression level for interacting neighbor 
+#' @param min_int_cells_expr minimum expression level for interacting neighbor
 #' cell type
 #' @param min_fdr minimum adjusted p-value
 #' @param min_spat_diff minimum absolute spatial expression difference
@@ -1321,45 +1427,53 @@ cellProximityVisPlot <- function(gobject,
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
 #' icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus")
-#' 
-#' plotCellProximityFeats(gobject = g, icfObject = icfObject,
-#' show_plot = TRUE, save_plot = FALSE, return_plot = FALSE)
+#'
+#' plotCellProximityFeats(
+#'     gobject = g, icfObject = icfObject,
+#'     show_plot = TRUE, save_plot = FALSE, return_plot = FALSE
+#' )
 #' @export
-plotCellProximityFeats <- function(gobject,
-    icfObject,
-    method = c("volcano", "cell_barplot", "cell-cell", "cell_sankey", 
-                "heatmap", "dotplot"),
-    min_cells = 4,
-    min_cells_expr = 1,
-    min_int_cells = 4,
-    min_int_cells_expr = 1,
-    min_fdr = 0.1,
-    min_spat_diff = 0.2,
-    min_log2_fc = 0.2,
-    min_zscore = 2,
-    zscores_column = c("cell_type", "feats"),
-    direction = c("both", "up", "down"),
-    cell_color_code = NULL,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotCellProximityFeats") {
+plotCellProximityFeats <- function(
+        gobject,
+        icfObject,
+        method = c(
+            "volcano", "cell_barplot", "cell-cell", "cell_sankey",
+            "heatmap", "dotplot"
+        ),
+        min_cells = 4,
+        min_cells_expr = 1,
+        min_int_cells = 4,
+        min_int_cells_expr = 1,
+        min_fdr = 0.1,
+        min_spat_diff = 0.2,
+        min_log2_fc = 0.2,
+        min_zscore = 2,
+        zscores_column = c("cell_type", "feats"),
+        direction = c("both", "up", "down"),
+        cell_color_code = NULL,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotCellProximityFeats") {
     if (!"icfObject" %in% class(icfObject)) {
-        stop("icfObject needs to be the output from 
+        stop("icfObject needs to be the output from
             findInteractionChangedFeats() or findICF()")
     }
 
     # print, return and save parameters
     show_plot <- ifelse(
-        is.na(show_plot), 
-        readGiottoInstructions(gobject, param = "show_plot"), show_plot)
+        is.na(show_plot),
+        readGiottoInstructions(gobject, param = "show_plot"), show_plot
+    )
     save_plot <- ifelse(
-        is.na(save_plot), 
-        readGiottoInstructions(gobject, param = "save_plot"), save_plot)
+        is.na(save_plot),
+        readGiottoInstructions(gobject, param = "save_plot"), save_plot
+    )
     return_plot <- ifelse(
-        is.na(return_plot), 
-        readGiottoInstructions(gobject, param = "return_plot"), return_plot)
+        is.na(return_plot),
+        readGiottoInstructions(gobject, param = "return_plot"), return_plot
+    )
 
 
     ## first filter
@@ -1381,9 +1495,12 @@ plotCellProximityFeats <- function(gobject,
 
     ## other parameters
     method <- match.arg(
-        method, 
-        choices = c("volcano", "cell_barplot", "cell-cell", "cell_sankey", 
-                    "heatmap", "dotplot"))
+        method,
+        choices = c(
+            "volcano", "cell_barplot", "cell-cell", "cell_sankey",
+            "heatmap", "dotplot"
+        )
+    )
 
 
     # variables
@@ -1394,14 +1511,19 @@ plotCellProximityFeats <- function(gobject,
         ## volcanoplot
         pl <- ggplot2::ggplot()
         pl <- pl + ggplot2::geom_point(
-            data = complete_part, 
-            ggplot2::aes(x = log2fc, 
-                        y = ifelse(is.infinite(-log10(p.adj)), 
-                                   1000, -log10(p.adj))))
+            data = complete_part,
+            ggplot2::aes(
+                x = log2fc,
+                y = ifelse(is.infinite(-log10(p.adj)),
+                    1000, -log10(p.adj)
+                )
+            )
+        )
         pl <- pl + ggplot2::theme_classic()
         pl <- pl + ggplot2::geom_vline(xintercept = 0, linetype = 2)
         pl <- pl + ggplot2::labs(
-            x = "log2 fold-change", y = "-log10(p.adjusted)")
+            x = "log2 fold-change", y = "-log10(p.adjusted)"
+        )
 
 
         ## print plot
@@ -1412,9 +1534,12 @@ plotCellProximityFeats <- function(gobject,
         ## save plot
         if (save_plot == TRUE) {
             do.call(
-                "all_plots_save_function", 
-                c(list(gobject = gobject, plot_object = pl, 
-                        default_save_name = default_save_name), save_param))
+                "all_plots_save_function",
+                c(list(
+                    gobject = gobject, plot_object = pl,
+                    default_save_name = default_save_name
+                ), save_param)
+            )
         }
 
         ## return plot
@@ -1429,11 +1554,14 @@ plotCellProximityFeats <- function(gobject,
 
         pl <- ggplot2::ggplot()
         pl <- pl + ggplot2::geom_bar(
-            data = complete_part, 
-            ggplot2::aes(x = unif_int, fill = unif_int))
+            data = complete_part,
+            ggplot2::aes(x = unif_int, fill = unif_int)
+        )
         pl <- pl + ggplot2::theme_classic() + ggplot2::theme(
             axis.text.x = ggplot2::element_text(
-                angle = 90, hjust = 1, vjust = 1))
+                angle = 90, hjust = 1, vjust = 1
+            )
+        )
         pl <- pl + ggplot2::coord_flip()
 
         ## print plot
@@ -1444,9 +1572,12 @@ plotCellProximityFeats <- function(gobject,
         ## save plot
         if (save_plot == TRUE) {
             do.call(
-                "all_plots_save_function", 
-                c(list(gobject = gobject, plot_object = pl, 
-                        default_save_name = default_save_name), save_param))
+                "all_plots_save_function",
+                c(list(
+                    gobject = gobject, plot_object = pl,
+                    default_save_name = default_save_name
+                ), save_param)
+            )
         }
 
         ## return plot
@@ -1463,14 +1594,17 @@ plotCellProximityFeats <- function(gobject,
         pl <- ggplot2::ggplot()
         pl <- pl + ggplot2::geom_bar(
             data = complete_part,
-            ggplot2::aes(x = cell_type, fill = int_cell_type))
+            ggplot2::aes(x = cell_type, fill = int_cell_type)
+        )
         if (!is.null(cell_color_code)) {
             pl <- pl + ggplot2::scale_fill_manual(values = cell_color_code)
         }
         pl <- pl + ggplot2::theme_classic() + ggplot2::theme(
-            axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
+            axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)
+        )
         pl <- pl + ggplot2::labs(
-            x = "", y = "# of features influenced by cell neighborhood")
+            x = "", y = "# of features influenced by cell neighborhood"
+        )
 
 
         ## print plot
@@ -1481,9 +1615,12 @@ plotCellProximityFeats <- function(gobject,
         ## save plot
         if (save_plot == TRUE) {
             do.call(
-                "all_plots_save_function", 
-                c(list(gobject = gobject, plot_object = pl, 
-                        default_save_name = default_save_name), save_param))
+                "all_plots_save_function",
+                c(list(
+                    gobject = gobject, plot_object = pl,
+                    default_save_name = default_save_name
+                ), save_param)
+            )
         }
 
         ## return plot
@@ -1503,14 +1640,18 @@ plotCellProximityFeats <- function(gobject,
         ) +
             ggalluvial::geom_alluvium(aes(fill = cell_type), width = 1 / 12) +
             ggalluvial::geom_stratum(
-                width = 1 / 12, fill = "black", color = "grey") +
+                width = 1 / 12, fill = "black", color = "grey"
+            ) +
             ggplot2::scale_x_discrete(
-                limits = c("cell type", "neighbours"), expand = c(.05, .05)) +
+                limits = c("cell type", "neighbours"), expand = c(.05, .05)
+            ) +
             ggplot2::geom_label(
-                stat = "stratum", label.strata = TRUE, size = 3) +
+                stat = "stratum", label.strata = TRUE, size = 3
+            ) +
             ggplot2::theme_classic() +
             ggplot2::labs(
-                x = "", y = "# of features influenced by cell neighborhood")
+                x = "", y = "# of features influenced by cell neighborhood"
+            )
 
         if (!is.null(cell_color_code)) {
             pl <- pl + ggplot2::scale_fill_manual(values = cell_color_code)
@@ -1526,9 +1667,12 @@ plotCellProximityFeats <- function(gobject,
         ## save plot
         if (save_plot == TRUE) {
             do.call(
-                "all_plots_save_function", 
-                c(list(gobject = gobject, plot_object = pl, 
-                        default_save_name = default_save_name), save_param))
+                "all_plots_save_function",
+                c(list(
+                    gobject = gobject, plot_object = pl,
+                    default_save_name = default_save_name
+                ), save_param)
+            )
         }
 
         ## return plot
@@ -1537,23 +1681,30 @@ plotCellProximityFeats <- function(gobject,
         }
     } else if (method == "dotplot") {
         changed_feats <- complete_part[
-            , .N, by = c("cell_type", "int_cell_type")]
+            , .N,
+            by = c("cell_type", "int_cell_type")
+        ]
 
         changed_feats[, cell_type := factor(cell_type, unique(cell_type))]
         changed_feats[, int_cell_type := factor(
-            int_cell_type, unique(int_cell_type))]
+            int_cell_type, unique(int_cell_type)
+        )]
 
         pl <- ggplot2::ggplot()
         pl <- pl + ggplot2::theme_classic()
         pl <- pl + ggplot2::geom_point(
-            data = changed_feats, 
-            ggplot2::aes(x = cell_type, y = int_cell_type, size = N))
+            data = changed_feats,
+            ggplot2::aes(x = cell_type, y = int_cell_type, size = N)
+        )
         pl <- pl + ggplot2::scale_size_continuous(
-            guide = guide_legend(title = "# of ICFs"))
+            guide = guide_legend(title = "# of ICFs")
+        )
         pl <- pl + ggplot2::theme(axis.text.x = ggplot2::element_text(
-            angle = 90, vjust = 1, hjust = 1))
+            angle = 90, vjust = 1, hjust = 1
+        ))
         pl <- pl + ggplot2::labs(
-            x = "source cell type", y = "neighbor cell type")
+            x = "source cell type", y = "neighbor cell type"
+        )
 
         ## print plot
         if (show_plot == TRUE) {
@@ -1563,9 +1714,12 @@ plotCellProximityFeats <- function(gobject,
         ## save plot
         if (save_plot == TRUE) {
             do.call(
-                "all_plots_save_function", 
-                c(list(gobject = gobject, plot_object = pl, 
-                        default_save_name = default_save_name), save_param))
+                "all_plots_save_function",
+                c(list(
+                    gobject = gobject, plot_object = pl,
+                    default_save_name = default_save_name
+                ), save_param)
+            )
         }
 
         ## return plot
@@ -1574,14 +1728,19 @@ plotCellProximityFeats <- function(gobject,
         }
     } else if (method == "heatmap") {
         changed_feats <- complete_part[
-            , .N, by = c("cell_type", "int_cell_type")]
+            , .N,
+            by = c("cell_type", "int_cell_type")
+        ]
 
         changed_feats[, cell_type := factor(cell_type, unique(cell_type))]
         changed_feats[, int_cell_type := factor(
-            int_cell_type, unique(int_cell_type))]
+            int_cell_type, unique(int_cell_type)
+        )]
 
         changed_feats_d <- data.table::dcast.data.table(
-            changed_feats, cell_type ~ int_cell_type, value.var = "N", fill = 0)
+            changed_feats, cell_type ~ int_cell_type,
+            value.var = "N", fill = 0
+        )
         changed_feats_m <- dt_to_matrix(changed_feats_d)
 
         col_fun <- GiottoVisuals::colorRamp2(
@@ -1591,7 +1750,7 @@ plotCellProximityFeats <- function(gobject,
 
         heatm <- ComplexHeatmap::Heatmap(log2(changed_feats_m + 1),
             col = col_fun,
-            row_title = "cell_type", 
+            row_title = "cell_type",
             column_title = "int_cell_type",
             heatmap_legend_param = list(title = "log2(# DEGs)")
         )
@@ -1604,9 +1763,12 @@ plotCellProximityFeats <- function(gobject,
         ## save plot
         if (save_plot == TRUE) {
             do.call(
-                "all_plots_save_function", 
-                c(list(gobject = gobject, plot_object = heatm, 
-                        default_save_name = default_save_name), save_param))
+                "all_plots_save_function",
+                c(list(
+                    gobject = gobject, plot_object = heatm,
+                    default_save_name = default_save_name
+                ), save_param)
+            )
         }
 
         ## return plot
@@ -1639,7 +1801,7 @@ plotCellProximityFeats <- function(gobject,
 #' @param min_cells minimum number of source cell type
 #' @param min_cells_expr minimum expression level for source cell type
 #' @param min_int_cells minimum number of interacting neighbor cell type
-#' @param min_int_cells_expr minimum expression level for interacting neighbor 
+#' @param min_int_cells_expr minimum expression level for interacting neighbor
 #' cell type
 #' @param min_fdr minimum adjusted p-value
 #' @param min_spat_diff minimum absolute spatial expression difference
@@ -1650,32 +1812,39 @@ plotCellProximityFeats <- function(gobject,
 #' @returns plot
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus",
-#' selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
-#' 
-#' plotCPF(gobject = g, icfObject = icfObject, show_plot = TRUE,
-#' save_plot = FALSE, return_plot = FALSE)
+#' icfObject <- findInteractionChangedFeats(g,
+#'     cluster_column = "leiden_clus",
+#'     selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10
+#' )
+#'
+#' plotCPF(
+#'     gobject = g, icfObject = icfObject, show_plot = TRUE,
+#'     save_plot = FALSE, return_plot = FALSE
+#' )
 #' @export
-plotCPF <- function(gobject,
-    icfObject,
-    method = c("volcano", "cell_barplot", "cell-cell", "cell_sankey", 
-                "heatmap", "dotplot"),
-    min_cells = 5,
-    min_cells_expr = 1,
-    min_int_cells = 3,
-    min_int_cells_expr = 1,
-    min_fdr = 0.05,
-    min_spat_diff = 0.2,
-    min_log2_fc = 0.2,
-    min_zscore = 2,
-    zscores_column = c("cell_type", "feats"),
-    direction = c("both", "up", "down"),
-    cell_color_code = NULL,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotCPG") {
+plotCPF <- function(
+        gobject,
+        icfObject,
+        method = c(
+            "volcano", "cell_barplot", "cell-cell", "cell_sankey",
+            "heatmap", "dotplot"
+        ),
+        min_cells = 5,
+        min_cells_expr = 1,
+        min_int_cells = 3,
+        min_int_cells_expr = 1,
+        min_fdr = 0.05,
+        min_spat_diff = 0.2,
+        min_log2_fc = 0.2,
+        min_zscore = 2,
+        zscores_column = c("cell_type", "feats"),
+        direction = c("both", "up", "down"),
+        cell_color_code = NULL,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotCPG") {
     plotCellProximityFeats(
         gobject = gobject,
         icfObject = icfObject,
@@ -1714,30 +1883,35 @@ plotCPF <- function(gobject,
 #' @returns plot
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus",
-#' selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
-#' 
-#' plotInteractionChangedFeats(gobject = g, icfObject = icfObject,
-#' source_type = "1", source_markers = "Ccnd2", 
-#' ICF_feats = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17"))
+#' icfObject <- findInteractionChangedFeats(g,
+#'     cluster_column = "leiden_clus",
+#'     selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10
+#' )
+#'
+#' plotInteractionChangedFeats(
+#'     gobject = g, icfObject = icfObject,
+#'     source_type = "1", source_markers = "Ccnd2",
+#'     ICF_feats = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17")
+#' )
 #' @export
-plotInteractionChangedFeats <- function(gobject,
-    icfObject,
-    source_type,
-    source_markers,
-    ICF_feats,
-    cell_color_code = NULL,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotInteractionChangedFeats") {
+plotInteractionChangedFeats <- function(
+        gobject,
+        icfObject,
+        source_type,
+        source_markers,
+        ICF_feats,
+        cell_color_code = NULL,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotInteractionChangedFeats") {
     # data.table variables
     cell_type <- int_cell_type <- log2fc <- NULL
 
 
     if (!"icfObject" %in% class(icfObject)) {
-        stop("icfObject needs to be the output from 
+        stop("icfObject needs to be the output from
             findInteractionChangedFeats() or findICF()")
     }
 
@@ -1754,22 +1928,28 @@ plotInteractionChangedFeats <- function(gobject,
     if (length(not_detected_feats) > 0) {
         cat(
             "These selected features are not in the icfObject: \n",
-            not_detected_feats)
+            not_detected_feats
+        )
     }
 
     # data.table set column names
     feats <- group <- NULL
 
     tempDT <- ICFscores[feats %in% all_feats][cell_type == source_type][
-        int_cell_type %in% neighbor_types]
+        int_cell_type %in% neighbor_types
+    ]
     tempDT[, feats := factor(feats, levels = detected_feats)]
-    tempDT[, group := names(ICF_feats[ICF_feats == feats]), by = 1:nrow(tempDT)]
+    tempDT[, group := names(ICF_feats[ICF_feats == feats]),
+        by = seq_len(nrow(tempDT))
+    ]
 
 
     if (is.null(cell_color_code)) {
         mycolors <- set_default_color_discrete_cell(
-            instrs = instructions(gobject))(n = length(unique(
-                tempDT$int_cell_type)))
+            instrs = instructions(gobject)
+        )(n = length(unique(
+            tempDT$int_cell_type
+        )))
         names(mycolors) <- unique(tempDT$int_cell_type)
     } else {
         mycolors <- cell_color_code
@@ -1779,17 +1959,20 @@ plotInteractionChangedFeats <- function(gobject,
     pl <- ggplot2::ggplot()
     pl <- pl + ggplot2::theme_classic() + ggplot2::theme(
         axis.text.x = ggplot2::element_text(
-            size = 14, angle = 45, vjust = 1, hjust = 1),
+            size = 14, angle = 45, vjust = 1, hjust = 1
+        ),
         axis.text.y = ggplot2::element_text(size = 14),
         axis.title = ggplot2::element_text(size = 14)
     )
     pl <- pl + ggplot2::geom_bar(
-        data = tempDT, 
-        ggplot2::aes(x = feats, y = log2fc, fill = int_cell_type), 
-        stat = "identity", position = ggplot2::position_dodge())
+        data = tempDT,
+        ggplot2::aes(x = feats, y = log2fc, fill = int_cell_type),
+        stat = "identity", position = ggplot2::position_dodge()
+    )
     pl <- pl + ggplot2::scale_fill_manual(values = mycolors)
     pl <- pl + ggplot2::labs(x = "", title = paste0(
-        "fold-change z-scores in ", source_type))
+        "fold-change z-scores in ", source_type
+    ))
 
     # output plot
     return(GiottoVisuals::plot_output_handler(
@@ -1823,24 +2006,29 @@ plotInteractionChangedFeats <- function(gobject,
 #' @returns plot
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus",
-#' selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
-#' 
-#' plotICF(gobject = g, icfObject = icfObject,
-#' source_type = "1", source_markers = "Ccnd2", 
-#' ICF_feats = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17"))
+#' icfObject <- findInteractionChangedFeats(g,
+#'     cluster_column = "leiden_clus",
+#'     selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10
+#' )
+#'
+#' plotICF(
+#'     gobject = g, icfObject = icfObject,
+#'     source_type = "1", source_markers = "Ccnd2",
+#'     ICF_feats = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17")
+#' )
 #' @export
-plotICF <- function(gobject,
-    icfObject,
-    source_type,
-    source_markers,
-    ICF_feats,
-    cell_color_code = NULL,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotICF") {
+plotICF <- function(
+        gobject,
+        icfObject,
+        source_type,
+        source_markers,
+        ICF_feats,
+        cell_color_code = NULL,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotICF") {
     plotInteractionChangedFeats(
         gobject = gobject,
         icfObject = icfObject,
@@ -1881,58 +2069,64 @@ plotICF <- function(gobject,
 #' @returns ggplot
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' g_icf <- findInteractionChangedFeats(g, 
-#' cluster_column = "leiden_clus", 
-#' selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
-#' 
+#'
+#' g_icf <- findInteractionChangedFeats(g,
+#'     cluster_column = "leiden_clus",
+#'     selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10
+#' )
+#'
 #' combIcfObject <- combineInteractionChangedFeats(g_icf)
-#' 
-#' plotCombineInteractionChangedFeats(gobject = g, 
-#' combIcfObject = combIcfObject, 
-#' selected_feat_to_feat = c("Btbd17--Ccnd2", "Btbd17--Gna12"), 
-#' selected_interactions = "1--8")
+#'
+#' plotCombineInteractionChangedFeats(
+#'     gobject = g,
+#'     combIcfObject = combIcfObject,
+#'     selected_feat_to_feat = c("Btbd17--Ccnd2", "Btbd17--Gna12"),
+#'     selected_interactions = "1--8"
+#' )
 #' @export
-plotCombineInteractionChangedFeats <- function(gobject,
-    combIcfObject,
-    selected_interactions = NULL,
-    selected_feat_to_feat = NULL,
-    detail_plot = TRUE,
-    simple_plot = FALSE,
-    simple_plot_facet = c("interaction", "feats"),
-    facet_scales = "fixed",
-    facet_ncol = length(selected_feat_to_feat),
-    facet_nrow = length(selected_interactions),
-    colors = c("#9932CC", "#FF8C00"),
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotCombineICF") {
+plotCombineInteractionChangedFeats <- function(
+        gobject,
+        combIcfObject,
+        selected_interactions = NULL,
+        selected_feat_to_feat = NULL,
+        detail_plot = TRUE,
+        simple_plot = FALSE,
+        simple_plot_facet = c("interaction", "feats"),
+        facet_scales = "fixed",
+        facet_ncol = length(selected_feat_to_feat),
+        facet_nrow = length(selected_interactions),
+        colors = c("#9932CC", "#FF8C00"),
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotCombineICF") {
     ## check validity
     if (!"combIcfObject" %in% class(combIcfObject)) {
-        stop("combIcfObject needs to be the output from 
+        stop("combIcfObject needs to be the output from
             combineInteractionChangedFeats() or combineICF()")
     }
     combIcfscore <- copy(combIcfObject[["combICFscores"]])
 
     if (is.null(selected_interactions) | is.null(selected_feat_to_feat)) {
-        stop("You need to provide a selection of cell-cell interactions and 
+        stop("You need to provide a selection of cell-cell interactions and
             features-features to plot")
     }
 
 
     # data.table variables
-    unif_feat_feat <- unif_int <- other_2 <- sel_2 <- other_1 <- sel_1 <- 
+    unif_feat_feat <- unif_int <- other_2 <- sel_2 <- other_1 <- sel_1 <-
         cols <- NULL
 
 
-    subDT <- combIcfscore[unif_feat_feat %in% selected_feat_to_feat & 
-                            unif_int %in% selected_interactions]
+    subDT <- combIcfscore[unif_feat_feat %in% selected_feat_to_feat &
+        unif_int %in% selected_interactions]
 
     # order interactions and feat-to-feat according to input
     subDT[, unif_feat_feat := factor(
-        unif_feat_feat, levels = selected_feat_to_feat)]
+        unif_feat_feat,
+        levels = selected_feat_to_feat
+    )]
     subDT[, unif_int := factor(unif_int, levels = selected_interactions)]
 
     if (simple_plot == FALSE) {
@@ -1941,31 +2135,37 @@ plotCombineInteractionChangedFeats <- function(gobject,
 
         if (detail_plot == TRUE) {
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = 0, y = other_2, colour = "other cell expression"), 
-                shape = 1)
+                data = subDT,
+                aes(x = 0, y = other_2, colour = "other cell expression"),
+                shape = 1
+            )
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = 0, y = sel_2, colour = "selected cell expression"), 
-                shape = 1)
+                data = subDT,
+                aes(x = 0, y = sel_2, colour = "selected cell expression"),
+                shape = 1
+            )
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = other_1, y = 0, colour = "other cell expression"), 
-                shape = 1)
+                data = subDT,
+                aes(x = other_1, y = 0, colour = "other cell expression"),
+                shape = 1
+            )
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = sel_1, y = 0, colour = "selected cell expression"), 
-                shape = 1)
+                data = subDT,
+                aes(x = sel_1, y = 0, colour = "selected cell expression"),
+                shape = 1
+            )
         }
 
         pl <- pl + ggplot2::geom_point(
-            data = subDT, 
-            aes(x = other_1, y = other_2, colour = "other cell expression"), 
-            size = 2)
+            data = subDT,
+            aes(x = other_1, y = other_2, colour = "other cell expression"),
+            size = 2
+        )
         pl <- pl + ggplot2::geom_point(
-            data = subDT, 
-            aes(x = sel_1, y = sel_2, colour = "selected cell expression"), 
-            size = 2)
+            data = subDT,
+            aes(x = sel_1, y = sel_2, colour = "selected cell expression"),
+            size = 2
+        )
         pl <- pl + ggplot2::geom_segment(data = subDT, aes(
             x = other_1, xend = sel_1,
             y = other_2, yend = sel_2
@@ -1975,14 +2175,16 @@ plotCombineInteractionChangedFeats <- function(gobject,
             y = paste(subDT$feats_2, subDT$cell_type_2, sep = " in ")
         )
         pl <- pl + ggplot2::scale_colour_manual(
-            name = "expression source", values = colors)
+            name = "expression source", values = colors
+        )
         pl <- pl + ggplot2::facet_wrap(~ unif_feat_feat + unif_int,
             nrow = facet_nrow, ncol = facet_ncol,
             scales = facet_scales
         )
     } else {
         simple_plot_facet <- match.arg(
-            arg = simple_plot_facet, choices = c("interaction", "feats"))
+            arg = simple_plot_facet, choices = c("interaction", "feats")
+        )
 
         if (simple_plot_facet == "interaction") {
             pl <- ggplot2::ggplot()
@@ -1992,15 +2194,22 @@ plotCombineInteractionChangedFeats <- function(gobject,
                 y = unif_feat_feat, yend = unif_feat_feat
             ), linetype = 2)
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = sum(c(other_1, other_2)), y = unif_feat_feat, 
-                    colour = "other cell expression"))
+                data = subDT,
+                aes(
+                    x = sum(c(other_1, other_2)), y = unif_feat_feat,
+                    colour = "other cell expression"
+                )
+            )
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = sum(c(sel_1, sel_2)), y = unif_feat_feat, 
-                    colour = "selected cell expression"))
+                data = subDT,
+                aes(
+                    x = sum(c(sel_1, sel_2)), y = unif_feat_feat,
+                    colour = "selected cell expression"
+                )
+            )
             pl <- pl + ggplot2::scale_colour_manual(
-                name = "expression source", values = cols)
+                name = "expression source", values = cols
+            )
             pl <- pl + ggplot2::facet_wrap(~unif_int, scales = facet_scales)
             pl <- pl + ggplot2::labs(x = "interactions", y = "feat-feat")
         } else {
@@ -2011,17 +2220,26 @@ plotCombineInteractionChangedFeats <- function(gobject,
                 y = unif_int, yend = unif_int
             ), linetype = 2)
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = sum(c(other_1, other_2)), y = unif_int, 
-                    colour = "other cell expression"))
+                data = subDT,
+                aes(
+                    x = sum(c(other_1, other_2)), y = unif_int,
+                    colour = "other cell expression"
+                )
+            )
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = sum(c(sel_1, sel_2)), y = unif_int, 
-                    colour = "selected cell expression"))
+                data = subDT,
+                aes(
+                    x = sum(c(sel_1, sel_2)), y = unif_int,
+                    colour = "selected cell expression"
+                )
+            )
             pl <- pl + ggplot2::scale_colour_manual(
-                name = "expression source", values = cols)
+                name = "expression source", values = cols
+            )
             pl <- pl + ggplot2::facet_wrap(
-                ~unif_feat_feat, scales = facet_scales)
+                ~unif_feat_feat,
+                scales = facet_scales
+            )
             pl <- pl + ggplot2::labs(x = "feat-feat", y = "interactions")
         }
     }
@@ -2063,33 +2281,37 @@ plotCombineInteractionChangedFeats <- function(gobject,
 #' @returns ggplot
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' g_icf <- findInteractionChangedFeats(g, 
-#' cluster_column = "leiden_clus", 
-#' selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
-#' 
+#'
+#' g_icf <- findInteractionChangedFeats(g,
+#'     cluster_column = "leiden_clus",
+#'     selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10
+#' )
+#'
 #' combIcfObject <- combineInteractionChangedFeats(g_icf)
-#' 
-#' plotCombineICF(gobject = g, combIcfObject = combIcfObject, 
-#' selected_feat_to_feat = c("Btbd17--Ccnd2", "Btbd17--Gna12"), 
-#' selected_interactions = "1--8")
+#'
+#' plotCombineICF(
+#'     gobject = g, combIcfObject = combIcfObject,
+#'     selected_feat_to_feat = c("Btbd17--Ccnd2", "Btbd17--Gna12"),
+#'     selected_interactions = "1--8"
+#' )
 #' @export
-plotCombineICF <- function(gobject,
-    combIcfObject,
-    selected_interactions = NULL,
-    selected_feat_to_feat = NULL,
-    detail_plot = TRUE,
-    simple_plot = FALSE,
-    simple_plot_facet = c("interaction", "feats"),
-    facet_scales = "fixed",
-    facet_ncol = length(selected_feat_to_feat),
-    facet_nrow = length(selected_interactions),
-    colors = c("#9932CC", "#FF8C00"),
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotCombineICF") {
+plotCombineICF <- function(
+        gobject,
+        combIcfObject,
+        selected_interactions = NULL,
+        selected_feat_to_feat = NULL,
+        detail_plot = TRUE,
+        simple_plot = FALSE,
+        simple_plot_facet = c("interaction", "feats"),
+        facet_scales = "fixed",
+        facet_ncol = length(selected_feat_to_feat),
+        facet_nrow = length(selected_interactions),
+        colors = c("#9932CC", "#FF8C00"),
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotCombineICF") {
     plotCombineInteractionChangedFeats(
         gobject = gobject,
         combIcfObject = combIcfObject,
@@ -2126,13 +2348,13 @@ plotCombineICF <- function(gobject,
 
 #' @title plotCombineCellCellCommunication
 #' @name plotCombineCellCellCommunication
-#' @description Create visualization for combined (pairwise) cell proximity 
+#' @description Create visualization for combined (pairwise) cell proximity
 #' gene scores
 #' @inheritParams data_access_params
 #' @inheritParams plot_output_params
 #' @param combCCcom combined communcation scores, output from combCCcom()
 #' @param selected_LR selected ligand-receptor pair
-#' @param selected_cell_LR selected cell-cell interaction pair for 
+#' @param selected_cell_LR selected cell-cell interaction pair for
 #' ligand-receptor pair
 #' @param detail_plot show detailed info in both interacting cell types
 #' @param simple_plot show a simplified plot
@@ -2144,49 +2366,59 @@ plotCombineICF <- function(gobject,
 #' @returns ggplot
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' comScores <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-#' feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"), 
-#' feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17"))
-#' 
-#' exprCC <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-#' feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik")
-#' spatialCC <- spatCellCellcom(gobject = g, cluster_column = "leiden_clus",
-#' feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot")
-#' 
+#'
+#' comScores <- exprCellCellcom(g,
+#'     cluster_column = "leiden_clus",
+#'     feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"),
+#'     feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17")
+#' )
+#'
+#' exprCC <- exprCellCellcom(g,
+#'     cluster_column = "leiden_clus",
+#'     feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik"
+#' )
+#' spatialCC <- spatCellCellcom(
+#'     gobject = g, cluster_column = "leiden_clus",
+#'     feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot"
+#' )
+#'
 #' combCCcom <- combCCcom(spatialCC = spatialCC, exprCC = exprCC)
-#' 
-#' plotCombineCellCellCommunication(gobject = g, combCCcom = combCCcom,
-#' selected_LR = c("Gm19935-9630013A20Rik"), selected_cell_LR = c("1--1"))
+#'
+#' plotCombineCellCellCommunication(
+#'     gobject = g, combCCcom = combCCcom,
+#'     selected_LR = c("Gm19935-9630013A20Rik"), selected_cell_LR = c("1--1")
+#' )
 #' @export
-plotCombineCellCellCommunication <- function(gobject,
-    combCCcom,
-    selected_LR = NULL,
-    selected_cell_LR = NULL,
-    detail_plot = TRUE,
-    simple_plot = FALSE,
-    simple_plot_facet = c("interaction", "genes"),
-    facet_scales = "fixed",
-    facet_ncol = length(selected_LR),
-    facet_nrow = length(selected_cell_LR),
-    colors = c("#9932CC", "#FF8C00"),
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotCombineCellCellCommunication") {
+plotCombineCellCellCommunication <- function(
+        gobject,
+        combCCcom,
+        selected_LR = NULL,
+        selected_cell_LR = NULL,
+        detail_plot = TRUE,
+        simple_plot = FALSE,
+        simple_plot_facet = c("interaction", "genes"),
+        facet_scales = "fixed",
+        facet_ncol = length(selected_LR),
+        facet_nrow = length(selected_cell_LR),
+        colors = c("#9932CC", "#FF8C00"),
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotCombineCellCellCommunication") {
     # data.table variables
-    LR_comb <- LR_cell_comb <- lig_expr <- lig_expr_spat <- rec_expr <- 
+    LR_comb <- LR_cell_comb <- lig_expr <- lig_expr_spat <- rec_expr <-
         rec_expr_spat <- LR_expr <- LR_expr_spat <- NULL
 
     ## check validity
     if (is.null(selected_cell_LR) | is.null(selected_LR)) {
-        stop("You need to provide a selection of cell-cell interactions 
+        stop("You need to provide a selection of cell-cell interactions
             and genes-genes to plot")
     }
 
     subDT <- combCCcom[
-        LR_comb %in% selected_LR & LR_cell_comb %in% selected_cell_LR]
+        LR_comb %in% selected_LR & LR_cell_comb %in% selected_cell_LR
+    ]
 
     # order interactions and gene-to-gene according to input
     subDT[, LR_comb := factor(LR_comb, levels = selected_LR)]
@@ -2198,31 +2430,43 @@ plotCombineCellCellCommunication <- function(gobject,
 
         if (detail_plot == TRUE) {
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = 0, y = lig_expr, colour = "overall cell expression"), 
-                shape = 1)
+                data = subDT,
+                aes(x = 0, y = lig_expr, colour = "overall cell expression"),
+                shape = 1
+            )
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = 0, y = lig_expr_spat, 
-                    colour = "spatial cell expression"), shape = 1)
+                data = subDT,
+                aes(
+                    x = 0, y = lig_expr_spat,
+                    colour = "spatial cell expression"
+                ), shape = 1
+            )
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = rec_expr, y = 0, colour = "overall cell expression"), 
-                shape = 1)
+                data = subDT,
+                aes(x = rec_expr, y = 0, colour = "overall cell expression"),
+                shape = 1
+            )
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = rec_expr_spat, y = 0,
-                    colour = "spatial cell expression"), shape = 1)
+                data = subDT,
+                aes(
+                    x = rec_expr_spat, y = 0,
+                    colour = "spatial cell expression"
+                ), shape = 1
+            )
         }
 
         pl <- pl + ggplot2::geom_point(
-            data = subDT, 
+            data = subDT,
             aes(x = rec_expr, y = lig_expr, colour = "overall cell expression"),
-            size = 2)
+            size = 2
+        )
         pl <- pl + ggplot2::geom_point(
-            data = subDT, 
-            aes(x = rec_expr_spat, y = lig_expr_spat, 
-                colour = "spatial cell expression"), size = 2)
+            data = subDT,
+            aes(
+                x = rec_expr_spat, y = lig_expr_spat,
+                colour = "spatial cell expression"
+            ), size = 2
+        )
         pl <- pl + ggplot2::geom_segment(data = subDT, aes(
             x = rec_expr, xend = rec_expr_spat,
             y = lig_expr, yend = lig_expr_spat
@@ -2232,14 +2476,16 @@ plotCombineCellCellCommunication <- function(gobject,
             y = paste(subDT$ligand, subDT$lig_cell_type, sep = " in ")
         )
         pl <- pl + ggplot2::scale_colour_manual(
-            name = "expression source", values = colors)
+            name = "expression source", values = colors
+        )
         pl <- pl + ggplot2::facet_wrap(~ LR_comb + LR_cell_comb,
             nrow = facet_nrow, ncol = facet_ncol,
             scales = facet_scales
         )
     } else {
         simple_plot_facet <- match.arg(
-            arg = simple_plot_facet, choices = c("interaction", "genes"))
+            arg = simple_plot_facet, choices = c("interaction", "genes")
+        )
 
         if (simple_plot_facet == "interaction") {
             pl <- ggplot2::ggplot()
@@ -2249,15 +2495,22 @@ plotCombineCellCellCommunication <- function(gobject,
                 y = LR_comb, yend = LR_comb
             ), linetype = 2)
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = LR_expr, y = LR_comb, 
-                    colour = "overall cell expression"))
+                data = subDT,
+                aes(
+                    x = LR_expr, y = LR_comb,
+                    colour = "overall cell expression"
+                )
+            )
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = LR_expr_spat, y = LR_comb, 
-                    colour = "spatial cell expression"))
+                data = subDT,
+                aes(
+                    x = LR_expr_spat, y = LR_comb,
+                    colour = "spatial cell expression"
+                )
+            )
             pl <- pl + ggplot2::scale_colour_manual(
-                name = "expression source", values = colors)
+                name = "expression source", values = colors
+            )
             pl <- pl + ggplot2::facet_wrap(~LR_cell_comb, scales = "fixed")
             pl <- pl + ggplot2::labs(x = "interactions", y = "gene-gene")
             pl
@@ -2269,15 +2522,22 @@ plotCombineCellCellCommunication <- function(gobject,
                 y = LR_cell_comb, yend = LR_cell_comb
             ), linetype = 2)
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = LR_expr, y = LR_cell_comb, 
-                    colour = "overall cell expression"))
+                data = subDT,
+                aes(
+                    x = LR_expr, y = LR_cell_comb,
+                    colour = "overall cell expression"
+                )
+            )
             pl <- pl + ggplot2::geom_point(
-                data = subDT, 
-                aes(x = LR_expr_spat, y = LR_cell_comb, 
-                    colour = "spatial cell expression"))
+                data = subDT,
+                aes(
+                    x = LR_expr_spat, y = LR_cell_comb,
+                    colour = "spatial cell expression"
+                )
+            )
             pl <- pl + ggplot2::scale_colour_manual(
-                name = "expression source", values = colors)
+                name = "expression source", values = colors
+            )
             pl <- pl + ggplot2::facet_wrap(~LR_comb, scales = facet_scales)
             pl <- pl + ggplot2::labs(x = "gene-gene", y = "interactions")
         }
@@ -2300,13 +2560,13 @@ plotCombineCellCellCommunication <- function(gobject,
 
 #' @title plotCombineCCcom
 #' @name plotCombineCCcom
-#' @description Create visualization for combined (pairwise) cell proximity 
+#' @description Create visualization for combined (pairwise) cell proximity
 #' gene scores
 #' @inheritParams data_access_params
 #' @inheritParams plot_output_params
 #' @param combCCcom combined communcation scores, output from combCCcom()
 #' @param selected_LR selected ligand-receptor pair
-#' @param selected_cell_LR selected cell-cell interaction pair for 
+#' @param selected_cell_LR selected cell-cell interaction pair for
 #' ligand-receptor pair
 #' @param detail_plot show detailed info in both interacting cell types
 #' @param simple_plot show a simplified plot
@@ -2318,37 +2578,46 @@ plotCombineCellCellCommunication <- function(gobject,
 #' @returns ggplot
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' comScores <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-#' feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"), 
-#' feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17"))
-#' 
-#' exprCC <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-#' feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik")
-#' spatialCC <- spatCellCellcom(gobject = g, cluster_column = "leiden_clus",
-#' feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot")
-#' 
+#'
+#' comScores <- exprCellCellcom(g,
+#'     cluster_column = "leiden_clus",
+#'     feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"),
+#'     feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17")
+#' )
+#'
+#' exprCC <- exprCellCellcom(g,
+#'     cluster_column = "leiden_clus",
+#'     feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik"
+#' )
+#' spatialCC <- spatCellCellcom(
+#'     gobject = g, cluster_column = "leiden_clus",
+#'     feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot"
+#' )
+#'
 #' combCCcom <- combCCcom(spatialCC = spatialCC, exprCC = exprCC)
-#' 
-#' plotCombineCCcom(gobject = g, combCCcom = combCCcom,
-#' selected_LR = c("Gm19935-9630013A20Rik"), selected_cell_LR = c("1--1"))
+#'
+#' plotCombineCCcom(
+#'     gobject = g, combCCcom = combCCcom,
+#'     selected_LR = c("Gm19935-9630013A20Rik"), selected_cell_LR = c("1--1")
+#' )
 #' @export
-plotCombineCCcom <- function(gobject,
-    combCCcom,
-    selected_LR = NULL,
-    selected_cell_LR = NULL,
-    detail_plot = TRUE,
-    simple_plot = FALSE,
-    simple_plot_facet = c("interaction", "genes"),
-    facet_scales = "fixed",
-    facet_ncol = length(selected_LR),
-    facet_nrow = length(selected_cell_LR),
-    colors = c("#9932CC", "#FF8C00"),
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotCombineCCcom") {
+plotCombineCCcom <- function(
+        gobject,
+        combCCcom,
+        selected_LR = NULL,
+        selected_cell_LR = NULL,
+        detail_plot = TRUE,
+        simple_plot = FALSE,
+        simple_plot_facet = c("interaction", "genes"),
+        facet_scales = "fixed",
+        facet_ncol = length(selected_LR),
+        facet_nrow = length(selected_cell_LR),
+        colors = c("#9932CC", "#FF8C00"),
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotCombineCCcom") {
     plotCombineCellCellCommunication(
         gobject = gobject,
         combCCcom = combCCcom,
@@ -2373,15 +2642,15 @@ plotCombineCCcom <- function(gobject,
 
 #' @title plotCCcomHeatmap
 #' @name plotCCcomHeatmap
-#' @description Plots heatmap for ligand-receptor communication scores in 
+#' @description Plots heatmap for ligand-receptor communication scores in
 #' cell-cell interactions
 #' @inheritParams data_access_params
 #' @inheritParams plot_output_params
 #' @inheritParams plot_params
-#' @param comScores communinication scores from \code{\link{exprCellCellcom}} 
+#' @param comScores communinication scores from \code{\link{exprCellCellcom}}
 #' or \code{\link{spatCellCellcom}}
 #' @param selected_LR selected ligand-receptor combinations
-#' @param selected_cell_LR selected cell-cell combinations for ligand-receptor 
+#' @param selected_cell_LR selected cell-cell combinations for ligand-receptor
 #' combinations
 #' @param show_LR_names show ligand-receptor names
 #' @param show_cell_LR_names show cell-cell names
@@ -2391,33 +2660,40 @@ plotCombineCCcom <- function(gobject,
 #' @returns ggplot
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' comScores <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-#' feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"), 
-#' feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17"))
-#' 
+#'
+#' comScores <- exprCellCellcom(g,
+#'     cluster_column = "leiden_clus",
+#'     feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"),
+#'     feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17")
+#' )
+#'
 #' plotCCcomHeatmap(gobject = g, comScores = comScores, show_plot = TRUE)
 #' @export
-plotCCcomHeatmap <- function(gobject,
-    comScores,
-    selected_LR = NULL,
-    selected_cell_LR = NULL,
-    show_LR_names = TRUE,
-    show_cell_LR_names = TRUE,
-    show = c("PI", "LR_expr", "log2fc"),
-    cor_method = c("pearson", "kendall", "spearman"),
-    aggl_method = c("ward.D", "ward.D2", "single", "complete", "average", 
-                    "mcquitty", "median", "centroid"),
-    gradient_color = NULL,
-    gradient_style = c("divergent", "sequential"),
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotCCcomHeatmap") {
+plotCCcomHeatmap <- function(
+        gobject,
+        comScores,
+        selected_LR = NULL,
+        selected_cell_LR = NULL,
+        show_LR_names = TRUE,
+        show_cell_LR_names = TRUE,
+        show = c("PI", "LR_expr", "log2fc"),
+        cor_method = c("pearson", "kendall", "spearman"),
+        aggl_method = c(
+            "ward.D", "ward.D2", "single", "complete", "average",
+            "mcquitty", "median", "centroid"
+        ),
+        gradient_color = NULL,
+        gradient_style = c("divergent", "sequential"),
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotCCcomHeatmap") {
     # get parameters
     cor_method <- match.arg(
-        cor_method, choices = c("pearson", "kendall", "spearman"))
+        cor_method,
+        choices = c("pearson", "kendall", "spearman")
+    )
     aggl_method <- match.arg(aggl_method, choices = c(
         "ward.D", "ward.D2", "single", "complete",
         "average", "mcquitty", "median", "centroid"
@@ -2429,8 +2705,8 @@ plotCCcomHeatmap <- function(gobject,
 
     # plot method
     if (!is.null(selected_LR) & !is.null(selected_cell_LR)) {
-        selDT <- comScores[LR_comb %in% selected_LR & LR_cell_comb %in% 
-                                selected_cell_LR]
+        selDT <- comScores[LR_comb %in% selected_LR & LR_cell_comb %in%
+            selected_cell_LR]
     } else if (!is.null(selected_LR)) {
         selDT <- comScores[LR_comb %in% selected_LR]
     } else if (!is.null(selected_cell_LR)) {
@@ -2442,14 +2718,18 @@ plotCCcomHeatmap <- function(gobject,
     # creat matrix
     show <- match.arg(show, choices = c("PI", "LR_expr", "log2fc"))
     selDT_d <- data.table::dcast.data.table(
-        selDT, LR_cell_comb ~ LR_comb, value.var = show, fill = 0)
+        selDT, LR_cell_comb ~ LR_comb,
+        value.var = show, fill = 0
+    )
     selDT_m <- dt_to_matrix(selDT_d)
 
     ## cells
     corclus_cells_dist <- stats::as.dist(
-        1 - cor_flex(x = t_flex(selDT_m), method = cor_method))
+        1 - cor_flex(x = t_flex(selDT_m), method = cor_method)
+    )
     hclusters_cells <- stats::hclust(
-        d = corclus_cells_dist, method = aggl_method)
+        d = corclus_cells_dist, method = aggl_method
+    )
     clus_names <- rownames(selDT_m)
     names(clus_names) <- seq_along(clus_names)
     clus_sort_names <- clus_names[hclusters_cells$order]
@@ -2457,9 +2737,11 @@ plotCCcomHeatmap <- function(gobject,
 
     ## genes
     corclus_genes_dist <- stats::as.dist(
-        1 - cor_flex(x = selDT_m, method = cor_method))
+        1 - cor_flex(x = selDT_m, method = cor_method)
+    )
     hclusters_genes <- stats::hclust(
-        d = corclus_genes_dist, method = aggl_method)
+        d = corclus_genes_dist, method = aggl_method
+    )
     clus_names <- colnames(selDT_m)
     names(clus_names) <- seq_along(clus_names)
     clus_sort_names <- clus_names[hclusters_genes$order]
@@ -2513,19 +2795,19 @@ plotCCcomHeatmap <- function(gobject,
 
 #' @title plotCCcomDotplot
 #' @name plotCCcomDotplot
-#' @description Plots dotplot for ligand-receptor communication scores in 
+#' @description Plots dotplot for ligand-receptor communication scores in
 #' cell-cell interactions
 #' @inheritParams data_access_params
 #' @inheritParams plot_output_params
 #' @inheritParams plot_params
-#' @param comScores communication scores from \code{\link{exprCellCellcom}} 
+#' @param comScores communication scores from \code{\link{exprCellCellcom}}
 #' or \code{\link{spatCellCellcom}}
 #' @param selected_LR selected ligand-receptor combinations
-#' @param selected_cell_LR selected cell-cell combinations for ligand-receptor 
+#' @param selected_cell_LR selected cell-cell combinations for ligand-receptor
 #' combinations
 #' @param show_LR_names show ligand-receptor names
 #' @param show_cell_LR_names show cell-cell names
-#' @param cluster_on values to use for clustering of cell-cell and 
+#' @param cluster_on values to use for clustering of cell-cell and
 #' ligand-receptor pairs
 #' @param cor_method correlation method used for clustering
 #' @param aggl_method agglomeration method used by hclust
@@ -2534,33 +2816,40 @@ plotCCcomHeatmap <- function(gobject,
 #' @returns ggplot
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' comScores <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-#' feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"), 
-#' feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17"))
-#' 
+#'
+#' comScores <- exprCellCellcom(g,
+#'     cluster_column = "leiden_clus",
+#'     feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"),
+#'     feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17")
+#' )
+#'
 #' plotCCcomDotplot(gobject = g, comScores = comScores, show_plot = TRUE)
 #' @export
-plotCCcomDotplot <- function(gobject,
-    comScores,
-    selected_LR = NULL,
-    selected_cell_LR = NULL,
-    show_LR_names = TRUE,
-    show_cell_LR_names = TRUE,
-    cluster_on = c("PI", "LR_expr", "log2fc"),
-    cor_method = c("pearson", "kendall", "spearman"),
-    aggl_method = c("ward.D", "ward.D2", "single", "complete", "average", 
-                    "mcquitty", "median", "centroid"),
-    dot_color_gradient = NULL,
-    gradient_style = c("divergent", "sequential"),
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotCCcomDotplot") {
+plotCCcomDotplot <- function(
+        gobject,
+        comScores,
+        selected_LR = NULL,
+        selected_cell_LR = NULL,
+        show_LR_names = TRUE,
+        show_cell_LR_names = TRUE,
+        cluster_on = c("PI", "LR_expr", "log2fc"),
+        cor_method = c("pearson", "kendall", "spearman"),
+        aggl_method = c(
+            "ward.D", "ward.D2", "single", "complete", "average",
+            "mcquitty", "median", "centroid"
+        ),
+        dot_color_gradient = NULL,
+        gradient_style = c("divergent", "sequential"),
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotCCcomDotplot") {
     # get parameters
     cor_method <- match.arg(
-        cor_method, choices = c("pearson", "kendall", "spearman"))
+        cor_method,
+        choices = c("pearson", "kendall", "spearman")
+    )
     aggl_method <- match.arg(aggl_method, choices = c(
         "ward.D", "ward.D2", "single", "complete",
         "average", "mcquitty", "median", "centroid"
@@ -2572,8 +2861,8 @@ plotCCcomDotplot <- function(gobject,
 
     # plot method
     if (!is.null(selected_LR) & !is.null(selected_cell_LR)) {
-        selDT <- comScores[LR_comb %in% selected_LR & LR_cell_comb %in% 
-                                selected_cell_LR]
+        selDT <- comScores[LR_comb %in% selected_LR & LR_cell_comb %in%
+            selected_cell_LR]
     } else if (!is.null(selected_LR)) {
         selDT <- comScores[LR_comb %in% selected_LR]
     } else if (!is.null(selected_cell_LR)) {
@@ -2585,27 +2874,37 @@ plotCCcomDotplot <- function(gobject,
     # creat matrix
     cluster_on <- match.arg(cluster_on, choices = c("PI", "LR_expr", "log2fc"))
     selDT_d <- data.table::dcast.data.table(
-        selDT, LR_cell_comb ~ LR_comb, value.var = cluster_on, fill = 0)
+        selDT, LR_cell_comb ~ LR_comb,
+        value.var = cluster_on, fill = 0
+    )
     selDT_m <- dt_to_matrix(selDT_d)
 
     # remove zero variance
     sd_rows <- apply(selDT_m, 1, sd)
     sd_rows_zero <- names(sd_rows[sd_rows == 0])
-    if (length(sd_rows_zero) > 0) selDT_m <- selDT_m[
-        !rownames(selDT_m) %in% sd_rows_zero, ]
+    if (length(sd_rows_zero) > 0) {
+        selDT_m <- selDT_m[
+            !rownames(selDT_m) %in% sd_rows_zero,
+        ]
+    }
 
     sd_cols <- apply(selDT_m, 2, sd)
     sd_cols_zero <- names(sd_cols[sd_cols == 0])
-    if (length(sd_cols_zero) > 0) selDT_m <- selDT_m[
-        , !colnames(selDT_m) %in% sd_cols_zero]
+    if (length(sd_cols_zero) > 0) {
+        selDT_m <- selDT_m[
+            , !colnames(selDT_m) %in% sd_cols_zero
+        ]
+    }
 
 
 
     ## cells
     corclus_cells_dist <- stats::as.dist(
-        1 - cor_flex(x = t_flex(selDT_m), method = cor_method))
+        1 - cor_flex(x = t_flex(selDT_m), method = cor_method)
+    )
     hclusters_cells <- stats::hclust(
-        d = corclus_cells_dist, method = aggl_method)
+        d = corclus_cells_dist, method = aggl_method
+    )
     clus_names <- rownames(selDT_m)
     names(clus_names) <- seq_along(clus_names)
     clus_sort_names <- clus_names[hclusters_cells$order]
@@ -2613,9 +2912,11 @@ plotCCcomDotplot <- function(gobject,
 
     ## genes
     corclus_genes_dist <- stats::as.dist(
-        1 - cor_flex(x = selDT_m, method = cor_method))
+        1 - cor_flex(x = selDT_m, method = cor_method)
+    )
     hclusters_genes <- stats::hclust(
-        d = corclus_genes_dist, method = aggl_method)
+        d = corclus_genes_dist, method = aggl_method
+    )
     clus_names <- colnames(selDT_m)
     names(clus_names) <- seq_along(clus_names)
     clus_sort_names <- clus_names[hclusters_genes$order]
@@ -2675,7 +2976,7 @@ plotCCcomDotplot <- function(gobject,
 
 #' @title plotRankSpatvsExpr
 #' @name plotRankSpatvsExpr
-#' @description Plots dotplot to compare ligand-receptor rankings from 
+#' @description Plots dotplot to compare ligand-receptor rankings from
 #' spatial and expression information
 #' @inheritParams data_access_params
 #' @inheritParams plot_output_params
@@ -2691,39 +2992,44 @@ plotCCcomDotplot <- function(gobject,
 #' @param size_range size ranges of dotplot
 #' @param xlims x-limits, numerical vector of 2
 #' @param ylims y-limits, numerical vector of 2
-#' @param selected_ranks numerical vector, will be used to print out the 
+#' @param selected_ranks numerical vector, will be used to print out the
 #' percentage of top spatial ranks are recovered
 #' @returns ggplot
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' exprCC <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-#' feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik")
-#' spatialCC <- spatCellCellcom(gobject = g, cluster_column = "leiden_clus",
-#' feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot",
-#' random_iter = 10)
-#' 
+#'
+#' exprCC <- exprCellCellcom(g,
+#'     cluster_column = "leiden_clus",
+#'     feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik"
+#' )
+#' spatialCC <- spatCellCellcom(
+#'     gobject = g, cluster_column = "leiden_clus",
+#'     feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot",
+#'     random_iter = 10
+#' )
+#'
 #' combCC <- combCCcom(spatialCC = spatialCC, exprCC = exprCC)
-#' 
+#'
 #' plotRankSpatvsExpr(gobject = g, combCC = combCC)
 #' @export
-plotRankSpatvsExpr <- function(gobject,
-    combCC,
-    expr_rnk_column = "LR_expr_rnk",
-    spat_rnk_column = "LR_spat_rnk",
-    dot_color_gradient = NULL,
-    midpoint = deprecated(),
-    gradient_midpoint = 10,
-    gradient_style = c("divergent", "sequential"),
-    size_range = c(0.01, 1.5),
-    xlims = NULL,
-    ylims = NULL,
-    selected_ranks = c(1, 10, 20),
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotRankSpatvsExpr") {
+plotRankSpatvsExpr <- function(
+        gobject,
+        combCC,
+        expr_rnk_column = "LR_expr_rnk",
+        spat_rnk_column = "LR_spat_rnk",
+        dot_color_gradient = NULL,
+        midpoint = deprecated(),
+        gradient_midpoint = 10,
+        gradient_style = c("divergent", "sequential"),
+        size_range = c(0.01, 1.5),
+        xlims = NULL,
+        ylims = NULL,
+        selected_ranks = c(1, 10, 20),
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotRankSpatvsExpr") {
     # deprecate
     if (GiottoUtils::is_present(midpoint)) {
         deprecate_warn(
@@ -2742,16 +3048,20 @@ plotRankSpatvsExpr <- function(gobject,
 
     rnk_list <- list()
     spt_list <- list()
-    for (rnk in 1:total_rnks) {
-        mytab <- table(cut(sort(combCC[get(expr_rnk_column) == rnk][[
-            spat_rnk_column]]), breaks = seq(0, total_rnks, 1), 
-            labels = c(1:total_rnks)))
+    for (rnk in seq_len(total_rnks)) {
+        mytab <- table(cut(
+            sort(combCC[get(expr_rnk_column) == rnk][[
+                spat_rnk_column
+            ]]),
+            breaks = seq(0, total_rnks, 1),
+            labels = seq_len(total_rnks)
+        ))
         rnk_list[[rnk]] <- mytab
         spt_list[[rnk]] <- names(mytab)
     }
 
     rnk_res <- data.table::as.data.table(do.call("rbind", rnk_list))
-    rnk_res[, spt_rank := 1:total_rnks]
+    rnk_res[, spt_rank := seq_len(total_rnks)]
 
     rnk_res_m <- data.table::melt.data.table(rnk_res, id.vars = "spt_rank")
     rnk_res_m[, spt_rank := as.numeric(spt_rank)]
@@ -2760,20 +3070,24 @@ plotRankSpatvsExpr <- function(gobject,
     rnk_res_m[, diff := variable - spt_rank]
 
     for (i in selected_ranks) {
-        perc_recovered <- 100 * (sum(rnk_res_m[abs(diff) < i]$value) / 
-                                    sum(rnk_res_m$value))
-        cat("for top ", i, " expression ranks, you recover ", 
-            round(perc_recovered, 2), "% of the highest spatial rank")
+        perc_recovered <- 100 * (sum(rnk_res_m[abs(diff) < i]$value) /
+            sum(rnk_res_m$value))
+        cat(
+            "for top ", i, " expression ranks, you recover ",
+            round(perc_recovered, 2), "% of the highest spatial rank"
+        )
     }
 
 
     # full plot
     pl <- ggplot2::ggplot()
     pl <- pl + ggplot2::theme_classic() + ggplot2::theme(
-        axis.text = element_blank())
+        axis.text = element_blank()
+    )
     pl <- pl + ggplot2::geom_point(
-        data = rnk_res_m, 
-        ggplot2::aes(x = variable, y = spt_rank, size = value, color = value))
+        data = rnk_res_m,
+        ggplot2::aes(x = variable, y = spt_rank, size = value, color = value)
+    )
     pl <- pl + set_default_color_continuous_CCcom_dotplot(
         colors = dot_color_gradient,
         instrs = instructions(gobject),
@@ -2783,7 +3097,8 @@ plotRankSpatvsExpr <- function(gobject,
         guide = guide_legend(title = "")
     )
     pl <- pl + ggplot2::scale_size_continuous(
-        range = size_range, guide = "none")
+        range = size_range, guide = "none"
+    )
     pl <- pl + ggplot2::labs(x = "expression rank", y = "spatial rank")
 
     if (!is.null(xlims)) {
@@ -2811,23 +3126,24 @@ plotRankSpatvsExpr <- function(gobject,
 
 #' @title Create recovery plot
 #' @name .plotRecovery_sub
-#' @description Plots recovery plot to compare ligand-receptor rankings from 
+#' @description Plots recovery plot to compare ligand-receptor rankings from
 #' spatial and expression information
 #' @param combCC combined communinication scores from \code{\link{combCCcom}}
 #' @param first_col first column to use
 #' @param second_col second column to use
 #' @returns ggplot
 #' @keywords internal
-.plotRecovery_sub <- function(combCC,
-    first_col = "LR_expr_rnk",
-    second_col = "LR_spat_rnk") {
+.plotRecovery_sub <- function(
+        combCC,
+        first_col = "LR_expr_rnk",
+        second_col = "LR_spat_rnk") {
     # data.table variables
     concord <- perc <- not_concord <- secondrank <- secondrank_perc <- NULL
 
     mergeDT_filt <- combCC[get(first_col) == 1]
 
     mymat <- matrix(data = NA, nrow = max(combCC[[second_col]]), ncol = 2)
-    for (i in 1:max(combCC[[second_col]])) {
+    for (i in seq_len(max(combCC[[second_col]]))) {
         mergeDT_filt[, concord := ifelse(get(second_col) <= i, "yes", "no")]
         mytable <- table(mergeDT_filt$concord)
 
@@ -2843,7 +3159,7 @@ plotRankSpatvsExpr <- function(gobject,
     mymatDT <- data.table::as.data.table(mymat)
     colnames(mymatDT) <- c("concord", "not_concord")
     mymatDT[, perc := 100 * (concord / (concord + not_concord))]
-    mymatDT[, secondrank := 1:nrow(mymatDT)]
+    mymatDT[, secondrank := seq_len(nrow(mymatDT))]
     mymatDT[, secondrank_perc := (secondrank / max(secondrank)) * 100]
 
     # percentage explained
@@ -2854,8 +3170,9 @@ plotRankSpatvsExpr <- function(gobject,
     pl <- ggplot2::ggplot()
     pl <- pl + ggplot2::theme_classic()
     pl <- pl + ggplot2::geom_point(
-        data = mymatDT, 
-        aes(x = secondrank_perc, y = perc))
+        data = mymatDT,
+        aes(x = secondrank_perc, y = perc)
+    )
     pl <- pl + ggplot2::scale_x_continuous(expand = c(0, 0), limits = c(0, 100))
     pl <- pl + ggplot2::scale_y_continuous(expand = c(0, 0), limits = c(0, 100))
     pl <- pl + ggplot2::geom_abline(slope = 1, intercept = 0, color = "blue")
@@ -2869,7 +3186,7 @@ plotRankSpatvsExpr <- function(gobject,
 
 #' @title plotRecovery
 #' @name plotRecovery
-#' @description Plots recovery plot to compare ligand-receptor rankings from 
+#' @description Plots recovery plot to compare ligand-receptor rankings from
 #' spatial and expression information
 #' @inheritParams data_access_params
 #' @inheritParams plot_output_params
@@ -2880,29 +3197,36 @@ plotRankSpatvsExpr <- function(gobject,
 #' @returns ggplot
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
-#' exprCC <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-#' feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik")
-#' spatialCC <- spatCellCellcom(gobject = g, cluster_column = "leiden_clus",
-#' feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot",
-#' random_iter = 10)
-#' 
+#'
+#' exprCC <- exprCellCellcom(g,
+#'     cluster_column = "leiden_clus",
+#'     feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik"
+#' )
+#' spatialCC <- spatCellCellcom(
+#'     gobject = g, cluster_column = "leiden_clus",
+#'     feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot",
+#'     random_iter = 10
+#' )
+#'
 #' combCC <- combCCcom(spatialCC = spatialCC, exprCC = exprCC)
-#' 
+#'
 #' plotRecovery(gobject = g, combCC = combCC)
 #' @export
-plotRecovery <- function(gobject,
-    combCC,
-    expr_rnk_column = "exprPI_rnk",
-    spat_rnk_column = "spatPI_rnk",
-    ground_truth = c("spatial", "expression"),
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "plotRecovery") {
+plotRecovery <- function(
+        gobject,
+        combCC,
+        expr_rnk_column = "exprPI_rnk",
+        spat_rnk_column = "spatPI_rnk",
+        ground_truth = c("spatial", "expression"),
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "plotRecovery") {
     ground_truth <- match.arg(
-        ground_truth, choices = c("spatial", "expression"))
+        ground_truth,
+        choices = c("spatial", "expression")
+    )
 
 
     if (ground_truth == "spatial") {
@@ -2912,8 +3236,9 @@ plotRecovery <- function(gobject,
             second_col = expr_rnk_column
         )
         pl <- pl + ggplot2::labs(
-            x = "% expression rank included", 
-            y = "% highest spatial rank recovered")
+            x = "% expression rank included",
+            y = "% highest spatial rank recovered"
+        )
     } else if (ground_truth == "expression") {
         pl <- .plotRecovery_sub(
             combCC = combCC,
@@ -2921,8 +3246,9 @@ plotRecovery <- function(gobject,
             second_col = spat_rnk_column
         )
         pl <- pl + ggplot2::labs(
-            x = "% spatial rank included", 
-            y = "% highest expression rank recovered")
+            x = "% spatial rank included",
+            y = "% highest expression rank recovered"
+        )
     }
 
     return(plot_output_handler(
@@ -2950,7 +3276,7 @@ plotRecovery <- function(gobject,
 
 #' @title cellProximitySpatPlot2D
 #' @name cellProximitySpatPlot2D
-#' @description Visualize 2D cell-cell interactions according to spatial 
+#' @description Visualize 2D cell-cell interactions according to spatial
 #' coordinates in ggplot mode
 #' @inheritParams data_access_params
 #' @inheritParams plot_output_params
@@ -2983,45 +3309,48 @@ plotRecovery <- function(gobject,
 #' g <- GiottoData::loadGiottoMini("visium")
 #' g <- createSpatialGrid(g, sdimx_stepsize = 5, sdimy_stepsize = 5)
 #' x <- cellProximityEnrichment(g, cluster_column = "leiden_clus")
-#' 
-#' cellProximitySpatPlot2D(gobject = g, cluster_column = "leiden_clus",
-#' interaction_name = x)
+#'
+#' cellProximitySpatPlot2D(
+#'     gobject = g, cluster_column = "leiden_clus",
+#'     interaction_name = x
+#' )
 #' @export
-cellProximitySpatPlot2D <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    spat_loc_name = NULL,
-    interaction_name = NULL,
-    cluster_column = NULL,
-    sdimx = "sdimx",
-    sdimy = "sdimy",
-    cell_color = NULL,
-    cell_color_code = NULL,
-    color_as_factor = TRUE,
-    show_other_cells = FALSE,
-    show_network = FALSE,
-    show_other_network = FALSE,
-    network_color = NULL,
-    spatial_network_name = "Delaunay_network",
-    show_grid = FALSE,
-    grid_color = NULL,
-    spatial_grid_name = "spatial_grid",
-    coord_fix_ratio = 1,
-    show_legend = TRUE,
-    point_size_select = 2,
-    point_select_border_col = "black",
-    point_select_border_stroke = 0.05,
-    point_size_other = 1,
-    point_alpha_other = 0.3,
-    point_other_border_col = "lightgrey",
-    point_other_border_stroke = 0.01,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "cellProximitySpatPlot2D") {
+cellProximitySpatPlot2D <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        spat_loc_name = NULL,
+        interaction_name = NULL,
+        cluster_column = NULL,
+        sdimx = "sdimx",
+        sdimy = "sdimy",
+        cell_color = NULL,
+        cell_color_code = NULL,
+        color_as_factor = TRUE,
+        show_other_cells = FALSE,
+        show_network = FALSE,
+        show_other_network = FALSE,
+        network_color = NULL,
+        spatial_network_name = "Delaunay_network",
+        show_grid = FALSE,
+        grid_color = NULL,
+        spatial_grid_name = "spatial_grid",
+        coord_fix_ratio = 1,
+        show_legend = TRUE,
+        point_size_select = 2,
+        point_select_border_col = "black",
+        point_select_border_stroke = 0.05,
+        point_size_other = 1,
+        point_alpha_other = 0.3,
+        point_other_border_col = "lightgrey",
+        point_other_border_stroke = 0.01,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "cellProximitySpatPlot2D") {
     if (is.null(interaction_name)) {
-        stop("you need to specific at least one interaction name, run 
+        stop("you need to specific at least one interaction name, run
              cellProximityEnrichment")
     }
 
@@ -3071,7 +3400,7 @@ cellProximitySpatPlot2D <- function(gobject,
 
 
     # data.table variables
-    unified_int <- sdimx_begin <- sdimy_begin <- sdimx_end <- sdimy_end <- 
+    unified_int <- sdimx_begin <- sdimy_begin <- sdimx_end <- sdimy_end <-
         x_start <- x_end <- y_start <- y_end <- cell_ID <- NULL
 
     cell_IDs_to_keep <- unique(c(
@@ -3082,7 +3411,8 @@ cellProximitySpatPlot2D <- function(gobject,
     if (show_other_cells) {
         CellType <- strsplit(interaction_name, "--")
         all_cell_IDs <- cell_metadata[cell_metadata[[
-            cluster_column]] == CellType[[1]][1] |
+            cluster_column
+        ]] == CellType[[1]][1] |
             cell_metadata[[cluster_column]] == CellType[[1]][2], ]$cell_ID
         other_cell_IDs <- setdiff(all_cell_IDs, cell_IDs_to_keep)
     }
@@ -3093,13 +3423,15 @@ cellProximitySpatPlot2D <- function(gobject,
         cell_locations_metadata <- cell_locations
     } else {
         cell_locations_metadata <- merge(
-            cell_locations, cell_metadata, by = "cell_ID")
+            cell_locations, cell_metadata,
+            by = "cell_ID"
+        )
     }
 
 
     # first 2 dimensions need to be defined
     if (is.null(sdimx) | is.null(sdimy)) {
-        message("first and second dimension need to be defined, default is 
+        message("first and second dimension need to be defined, default is
                 first 2")
         sdimx <- "sdimx"
         sdimy <- "sdimy"
@@ -3113,15 +3445,19 @@ cellProximitySpatPlot2D <- function(gobject,
         if (show_other_network) {
             pl <- pl + ggplot2::geom_segment(
                 data = spatial_network[!unified_int %in% interaction_name],
-                aes(x = sdimx_begin, y = sdimy_begin, xend = sdimx_end, 
-                    yend = sdimy_end),
+                aes(
+                    x = sdimx_begin, y = sdimy_begin, xend = sdimx_end,
+                    yend = sdimy_end
+                ),
                 color = "lightgrey", size = 0.5, alpha = 0.5
             )
         }
         pl <- pl + ggplot2::geom_segment(
             data = spatial_network[unified_int %in% interaction_name],
-            aes(x = sdimx_begin, y = sdimy_begin, xend = sdimx_end, 
-                yend = sdimy_end),
+            aes(
+                x = sdimx_begin, y = sdimy_begin, xend = sdimx_end,
+                yend = sdimy_end
+            ),
             color = network_color, size = 0.5, alpha = 0.5
         )
     }
@@ -3129,7 +3465,7 @@ cellProximitySpatPlot2D <- function(gobject,
     if (!is.null(spatial_grid) & show_grid == TRUE) {
         if (is.null(grid_color)) grid_color <- "black"
         pl <- pl + ggplot2::geom_rect(
-            data = spatial_grid, 
+            data = spatial_grid,
             aes(xmin = x_start, xmax = x_end, ymin = y_start, ymax = y_end),
             color = grid_color, fill = NA
         )
@@ -3139,22 +3475,22 @@ cellProximitySpatPlot2D <- function(gobject,
     if (is.null(cell_color)) {
         cell_color <- "lightblue"
         pl <- pl + ggplot2::geom_point(
-            data = cell_locations[!cell_ID %in% cell_IDs_to_keep], 
+            data = cell_locations[!cell_ID %in% cell_IDs_to_keep],
             aes_string(x = sdimx, y = sdimy),
-            show.legend = show_legend, shape = 21, fill = "lightgrey", 
+            show.legend = show_legend, shape = 21, fill = "lightgrey",
             size = point_size_other
         )
         pl <- pl + ggplot2::geom_point(
-            data = cell_locations[cell_ID %in% cell_IDs_to_keep], 
+            data = cell_locations[cell_ID %in% cell_IDs_to_keep],
             aes_string(x = sdimx, y = sdimy),
-            show.legend = show_legend, shape = 21, fill = cell_color, 
+            show.legend = show_legend, shape = 21, fill = cell_color,
             size = point_size_select
         )
         if (show_other_cells) {
             pl <- pl + ggplot2::geom_point(
-                data = cell_locations[cell_ID %in% other_cell_IDs], 
+                data = cell_locations[cell_ID %in% other_cell_IDs],
                 aes_string(x = sdimx, y = sdimy),
-                show.legend = show_legend, shape = 21, fill = cell_color, 
+                show.legend = show_legend, shape = 21, fill = cell_color,
                 alpha = point_alpha_other,
                 size = point_size_select * 0.5
             )
@@ -3170,21 +3506,21 @@ cellProximitySpatPlot2D <- function(gobject,
                 data = cell_locations_metadata[!cell_ID %in% cell_IDs_to_keep],
                 aes_string(x = sdimx, y = sdimy),
                 fill = "lightgrey", shape = 21, size = point_size_other,
-                color = point_other_border_col, 
+                color = point_other_border_col,
                 stroke = point_other_border_stroke
             )
             pl <- pl + ggplot2::geom_point(
-                data = cell_locations_metadata[cell_ID %in% cell_IDs_to_keep], 
+                data = cell_locations_metadata[cell_ID %in% cell_IDs_to_keep],
                 aes_string(x = sdimx, y = sdimy, fill = cell_color),
                 show.legend = show_legend, shape = 21, size = point_size_select,
-                color = point_select_border_col, 
+                color = point_select_border_col,
                 stroke = point_select_border_stroke
             )
             if (show_other_cells) {
                 pl <- pl + ggplot2::geom_point(
                     data = cell_locations_metadata[cell_ID %in% other_cell_IDs],
                     aes_string(x = sdimx, y = sdimy, fill = cell_color),
-                    show.legend = show_legend, shape = 21, 
+                    show.legend = show_legend, shape = 21,
                     alpha = point_alpha_other,
                     size = point_size_select * 0.5
                 )
@@ -3197,7 +3533,8 @@ cellProximitySpatPlot2D <- function(gobject,
             } else if (color_as_factor == TRUE) {
                 number_colors <- length(unique(factor_data))
                 cell_color_code <- set_default_color_discrete_cell(
-                    instrs = instructions(gobject))(n = number_colors)
+                    instrs = instructions(gobject)
+                )(n = number_colors)
                 names(cell_color_code) <- unique(factor_data)
                 pl <- pl + ggplot2::scale_fill_manual(values = cell_color_code)
             } else if (color_as_factor == FALSE) {
@@ -3215,17 +3552,17 @@ cellProximitySpatPlot2D <- function(gobject,
             pl <- pl + ggplot2::geom_point(
                 data = cell_locations_metadata[!cell_ID %in% cell_IDs_to_keep],
                 aes_string(x = sdimx, y = sdimy),
-                show.legend = show_legend, shape = 21, fill = "lightgrey", 
+                show.legend = show_legend, shape = 21, fill = "lightgrey",
                 size = point_size_other,
-                color = point_other_border_col, 
+                color = point_other_border_col,
                 stroke = point_other_border_stroke
             )
             pl <- pl + ggplot2::geom_point(
                 data = cell_locations_metadata[cell_ID %in% cell_IDs_to_keep],
                 aes_string(x = sdimx, y = sdimy),
-                show.legend = show_legend, shape = 21, fill = cell_color, 
+                show.legend = show_legend, shape = 21, fill = cell_color,
                 size = point_size_select,
-                color = point_select_border_col, 
+                color = point_select_border_col,
                 stroke = point_select_border_stroke
             )
         }
@@ -3260,14 +3597,14 @@ cellProximitySpatPlot2D <- function(gobject,
 
 #' @title cellProximitySpatPlot
 #' @name cellProximitySpatPlot
-#' @description Visualize 2D cell-cell interactions according to spatial 
+#' @description Visualize 2D cell-cell interactions according to spatial
 #' coordinates in ggplot mode
 #' @param gobject giotto object
 #' @inheritDotParams cellProximitySpatPlot2D -gobject
 #' @returns ggplot
 #' @details Description of parameters.
 #' @export
-#' @seealso  \code{\link{cellProximitySpatPlot2D}} and 
+#' @seealso  \code{\link{cellProximitySpatPlot2D}} and
 #' \code{\link{cellProximitySpatPlot3D}} for 3D
 cellProximitySpatPlot <- function(gobject, ...) {
     cellProximitySpatPlot2D(gobject = gobject, ...)
@@ -3276,7 +3613,7 @@ cellProximitySpatPlot <- function(gobject, ...) {
 
 #' @title cellProximitySpatPlot3D
 #' @name cellProximitySpatPlot3D
-#' @description Visualize 3D cell-cell interactions according to spatial 
+#' @description Visualize 3D cell-cell interactions according to spatial
 #' coordinates in plotly mode
 #' @inheritParams data_access_params
 #' @inheritParams plot_output_params
@@ -3307,38 +3644,39 @@ cellProximitySpatPlot <- function(gobject, ...) {
 #' @returns plotly
 #' @details Description of parameters.
 #' @export
-cellProximitySpatPlot3D <- function(gobject,
-    interaction_name = NULL,
-    cluster_column = NULL,
-    sdimx = "sdimx",
-    sdimy = "sdimy",
-    sdimz = "sdimz",
-    cell_color = NULL,
-    cell_color_code = NULL,
-    color_as_factor = TRUE,
-    show_other_cells = TRUE,
-    show_network = TRUE,
-    show_other_network = FALSE,
-    network_color = NULL,
-    spatial_network_name = "Delaunay_network",
-    show_grid = FALSE,
-    grid_color = NULL,
-    spatial_grid_name = "spatial_grid",
-    show_legend = TRUE,
-    point_size_select = 4,
-    point_size_other = 2,
-    point_alpha_other = 0.5,
-    axis_scale = c("cube", "real", "custom"),
-    custom_ratio = NULL,
-    x_ticks = NULL,
-    y_ticks = NULL,
-    z_ticks = NULL,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "cellProximitySpatPlot3D",
-    ...) {
+cellProximitySpatPlot3D <- function(
+        gobject,
+        interaction_name = NULL,
+        cluster_column = NULL,
+        sdimx = "sdimx",
+        sdimy = "sdimy",
+        sdimz = "sdimz",
+        cell_color = NULL,
+        cell_color_code = NULL,
+        color_as_factor = TRUE,
+        show_other_cells = TRUE,
+        show_network = TRUE,
+        show_other_network = FALSE,
+        network_color = NULL,
+        spatial_network_name = "Delaunay_network",
+        show_grid = FALSE,
+        grid_color = NULL,
+        spatial_grid_name = "spatial_grid",
+        show_legend = TRUE,
+        point_size_select = 4,
+        point_size_other = 2,
+        point_alpha_other = 0.5,
+        axis_scale = c("cube", "real", "custom"),
+        custom_ratio = NULL,
+        x_ticks = NULL,
+        y_ticks = NULL,
+        z_ticks = NULL,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "cellProximitySpatPlot3D",
+        ...) {
     if (is.null(sdimz)) {
         pl <- .cellProximityVisPlot_2D_plotly(
             gobject = gobject,
diff --git a/R/spdep.R b/R/spdep.R
index d557c0021..6abbe9cad 100644
--- a/R/spdep.R
+++ b/R/spdep.R
@@ -2,12 +2,12 @@
 #'
 #' @param gobject Input a Giotto object.
 #' @param method Specify a method name to compute auto correlation.
-#' Available methods include 
+#' Available methods include
 #' \code{"geary.test", "lee.test", "lm.morantest","moran.test"}.
 #' @param spat_unit spatial unit
 #' @param feat_type feature type
 #' @param expression_values expression values to use, default = normalized
-#' @param spatial_network_to_use spatial network to use, 
+#' @param spatial_network_to_use spatial network to use,
 #' default = spatial_network
 #' @param verbose be verbose
 #' @param return_gobject if FALSE, results are returned as data.table.
@@ -15,17 +15,18 @@
 #' @returns A data table with computed values for each feature.
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' spdepAutoCorr(g)
 #' @export
-spdepAutoCorr <- function(gobject,
-    method = c("geary.test", "lee.test", "lm.morantest", "moran.test"),
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = "normalized",
-    spatial_network_to_use = "spatial_network",
-    return_gobject = FALSE,
-    verbose = FALSE) {
+spdepAutoCorr <- function(
+        gobject,
+        method = c("geary.test", "lee.test", "lm.morantest", "moran.test"),
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = "normalized",
+        spatial_network_to_use = "spatial_network",
+        return_gobject = FALSE,
+        verbose = FALSE) {
     # Check and match the specified method argument
     method <- match.arg(method)
 
@@ -77,8 +78,11 @@ spdepAutoCorr <- function(gobject,
 
     result_list <- list()
     progressr::with_progress({
-        if (step_size > 1) pb <- progressr::progressor(
-            steps = nfeats / step_size)
+        if (step_size > 1) {
+            pb <- progressr::progressor(
+                steps = nfeats / step_size
+            )
+        }
         result_list <- lapply_flex(
             seq_along(feat),
             future.packages = c("data.table", "spdep"),
@@ -91,7 +95,8 @@ spdepAutoCorr <- function(gobject,
                 # Extract the estimated value from the result
                 result_value <- callSpdepVar$estimate[1]
                 temp_dt <- data.table(
-                    feat_ID = feat[feat_value], value = result_value)
+                    feat_ID = feat[feat_value], value = result_value
+                )
                 # increment progress
                 if (exists("pb")) if (feat_value %% step_size == 0) pb()
                 return(temp_dt)
@@ -141,11 +146,11 @@ callSpdep <- function(method, ...) {
 
     # Check if 'method' argument is NULL, if so, stop with an error
     if (is.null(method)) {
-        stop("The 'method' argument has not been provided. Please specify a 
+        stop("The 'method' argument has not been provided. Please specify a
             valid method.")
     }
 
-    # Check if 'method' exists in the 'spdep' package, if not, stop with an 
+    # Check if 'method' exists in the 'spdep' package, if not, stop with an
     # error
     method <- try(eval(get(method, envir = loadNamespace("spdep"))),
         silent = TRUE
@@ -186,7 +191,7 @@ callSpdep <- function(method, ...) {
     if (all(!(names(methodparam)) %in% allArgs)) {
         stop("Invalid or missing parameters.")
     }
-    # A vector of specified arguments that trigger 
+    # A vector of specified arguments that trigger
     # 'spW <- spweights.constants()'
     requiredArgs <- c("n", "n1", "n2", "n3", "nn", "S0", "S1", "S2")
 
@@ -194,7 +199,7 @@ callSpdep <- function(method, ...) {
     if (any(requiredArgs %in% allArgs)) {
         # Obtain arguments from 'spweights.constants'
         spW <- spdep::spweights.constants(listw = methodparam$listw)
-        # Combine user-provided arguments and 'spW', checking only against 
+        # Combine user-provided arguments and 'spW', checking only against
         # 'feats' value
         combinedParams <- append(methodparam, spW)
     } else {
diff --git a/R/variable_genes.R b/R/variable_genes.R
index 39e12568c..c65c0cb89 100644
--- a/R/variable_genes.R
+++ b/R/variable_genes.R
@@ -1,9 +1,10 @@
-.calc_cov_group_hvf <- function(feat_in_cells_detected,
-    nr_expression_groups = 20,
-    zscore_threshold = 1,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL) {
+.calc_cov_group_hvf <- function(
+        feat_in_cells_detected,
+        nr_expression_groups = 20,
+        zscore_threshold = 1,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL) {
     # NSE vars
     cov_group_zscore <- cov <- selected <- mean_expr <- NULL
 
@@ -11,30 +12,36 @@
     prob_sequence <- seq(0, 1, steps)
     prob_sequence[length(prob_sequence)] <- 1
     expr_group_breaks <- stats::quantile(
-        feat_in_cells_detected$mean_expr, probs = prob_sequence)
+        feat_in_cells_detected$mean_expr,
+        probs = prob_sequence
+    )
 
     ## remove zero's from cuts if there are too many and make first group zero
     if (any(duplicated(expr_group_breaks))) {
         m_expr_vector <- feat_in_cells_detected$mean_expr
         expr_group_breaks <- stats::quantile(
-            m_expr_vector[m_expr_vector > 0], probs = prob_sequence)
+            m_expr_vector[m_expr_vector > 0],
+            probs = prob_sequence
+        )
         expr_group_breaks[[1]] <- 0
     }
 
     expr_groups <- cut(
         x = feat_in_cells_detected$mean_expr,
         breaks = expr_group_breaks,
-        labels = paste0("group_", 1:nr_expression_groups),
+        labels = paste0("group_", seq_len(nr_expression_groups)),
         include.lowest = TRUE
     )
     feat_in_cells_detected[, expr_groups := expr_groups]
     feat_in_cells_detected[, cov_group_zscore := scale(cov), by = expr_groups]
     feat_in_cells_detected[, selected := ifelse(
-        cov_group_zscore > zscore_threshold, "yes", "no")]
+        cov_group_zscore > zscore_threshold, "yes", "no"
+    )]
 
     if (any(isTRUE(show_plot), isTRUE(return_plot), isTRUE(save_plot))) {
         pl <- .create_cov_group_hvf_plot(
-            feat_in_cells_detected, nr_expression_groups)
+            feat_in_cells_detected, nr_expression_groups
+        )
 
         return(list(dt = feat_in_cells_detected, pl = pl))
     } else {
@@ -48,11 +55,12 @@
 
 
 
-.calc_cov_loess_hvf <- function(feat_in_cells_detected,
-    difference_in_cov = 0.1,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL) {
+.calc_cov_loess_hvf <- function(
+        feat_in_cells_detected,
+        difference_in_cov = 0.1,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL) {
     # NSE vars
     cov_diff <- pred_cov_feats <- selected <- NULL
 
@@ -61,18 +69,25 @@
     var_col <- "cov"
 
     loess_model_sample <- stats::loess(
-        loess_formula, data = feat_in_cells_detected)
+        loess_formula,
+        data = feat_in_cells_detected
+    )
     feat_in_cells_detected$pred_cov_feats <- stats::predict(
-        loess_model_sample, newdata = feat_in_cells_detected)
-    feat_in_cells_detected[, cov_diff := get(var_col) - pred_cov_feats, 
-                            by = 1:nrow(feat_in_cells_detected)]
+        loess_model_sample,
+        newdata = feat_in_cells_detected
+    )
+    feat_in_cells_detected[, cov_diff := get(var_col) - pred_cov_feats,
+        by = seq_len(nrow(feat_in_cells_detected))
+    ]
     data.table::setorder(feat_in_cells_detected, -cov_diff)
     feat_in_cells_detected[, selected := ifelse(
-        cov_diff > difference_in_cov, "yes", "no")]
+        cov_diff > difference_in_cov, "yes", "no"
+    )]
 
     if (any(isTRUE(show_plot), isTRUE(return_plot), isTRUE(save_plot))) {
         pl <- .create_cov_loess_hvf_plot(
-            feat_in_cells_detected, difference_in_cov, var_col)
+            feat_in_cells_detected, difference_in_cov, var_col
+        )
 
         return(list(dt = feat_in_cells_detected, pl = pl))
     } else {
@@ -82,13 +97,14 @@
 
 
 
-.calc_var_hvf <- function(scaled_matrix,
-    var_threshold = 1.5,
-    var_number = NULL,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    use_parallel = FALSE) {
+.calc_var_hvf <- function(
+        scaled_matrix,
+        var_threshold = 1.5,
+        var_number = NULL,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        use_parallel = FALSE) {
     # NSE vars
     var <- selected <- NULL
 
@@ -96,7 +112,7 @@
         test <- apply(X = scaled_matrix, MARGIN = 1, FUN = function(x) var(x))
     } else {
         test <- future.apply::future_apply(
-            X = scaled_matrix, MARGIN = 1, FUN = function(x) var(x), 
+            X = scaled_matrix, MARGIN = 1, FUN = function(x) var(x),
             future.seed = TRUE
         )
     }
@@ -106,7 +122,7 @@
     dt_res <- data.table::data.table(feats = names(test), var = test)
 
     if (!is.null(var_number) & is.numeric(var_number)) {
-        dt_res[, selected := 1:.N]
+        dt_res[, selected := seq_len(.N)]
         dt_res[, selected := ifelse(selected <= var_number, "yes", "no")]
     } else {
         dt_res[, selected := ifelse(var >= var_threshold, "yes", "no")]
@@ -116,7 +132,7 @@
     if (isTRUE(show_plot) ||
         isTRUE(return_plot) ||
         isTRUE(save_plot)) {
-        dt_res[, rank := 1:.N]
+        dt_res[, rank := seq_len(.N)]
         pl <- .create_calc_var_hvf_plot(dt_res)
 
 
@@ -167,10 +183,9 @@
 }
 
 
-.calc_expr_cov_stats_parallel <- function(
-        expr_values,
-        expression_threshold,
-        cores = GiottoUtils::determine_cores()) {
+.calc_expr_cov_stats_parallel <- function(expr_values,
+    expression_threshold,
+    cores = GiottoUtils::determine_cores()) {
     # NSE vars
     cov <- sd <- mean_expr <- NULL
 
@@ -219,78 +234,81 @@
 #' @param feat_type feature type
 #' @param expression_values expression values to use
 #' @param method method to calculate highly variable features
-#' @param reverse_log_scale reverse log-scale of expression values 
+#' @param reverse_log_scale reverse log-scale of expression values
 #' (default = FALSE)
 #' @param logbase if `reverse_log_scale` is TRUE, which log base was used?
 #' @param expression_threshold expression threshold to consider a gene detected
-#' @param nr_expression_groups (cov_groups) number of expression groups for 
+#' @param nr_expression_groups (cov_groups) number of expression groups for
 #' cov_groups
 #' @param zscore_threshold (cov_groups) zscore to select hvg for cov_groups
 #' @param HVFname name for highly variable features in cell metadata
-#' @param difference_in_cov (cov_loess) minimum difference in coefficient of 
+#' @param difference_in_cov (cov_loess) minimum difference in coefficient of
 #' variance required
-#' @param var_threshold (var_p_resid) variance threshold for features for 
+#' @param var_threshold (var_p_resid) variance threshold for features for
 #' var_p_resid method
-#' @param var_number (var_p_resid) number of top variance features for 
+#' @param var_number (var_p_resid) number of top variance features for
 #' var_p_resid method
-#' @param random_subset random subset to perform HVF detection on. 
+#' @param random_subset random subset to perform HVF detection on.
 #' Passing `NULL` runs HVF on all cells.
 #' @param set_seed logical. whether to set a seed when random_subset is used
 #' @param seed_number seed number to use when random_subset is used
 #' @param show_plot show plot
 #' @param return_plot return ggplot object (overridden by `return_gobject`)
 #' @param save_plot logical. directly save the plot
-#' @param save_param list of saving parameters from 
+#' @param save_param list of saving parameters from
 #' [GiottoVisuals::all_plots_save_function()]
-#' @param default_save_name default save name for saving, don't change, change 
+#' @param default_save_name default save name for saving, don't change, change
 #' save_name in save_param
 #' @param return_gobject boolean: return giotto object (default = TRUE)
-#' @returns giotto object highly variable features appended to feature metadata 
+#' @param verbose be verbose
+#' @returns giotto object highly variable features appended to feature metadata
 #' (`fDataDT()`)
 #' @details
 #' Currently we provide 2 ways to calculate highly variable genes:
 #'
 #' \strong{1. high coeff of variance (COV) within groups: } \cr
-#' First genes are binned (\emph{nr_expression_groups}) into average expression 
-#' groups and the COV for each feature is converted into a z-score within each 
-#' bin. Features with a z-score higher than the threshold 
+#' First genes are binned (\emph{nr_expression_groups}) into average expression
+#' groups and the COV for each feature is converted into a z-score within each
+#' bin. Features with a z-score higher than the threshold
 #' (\emph{zscore_threshold}) are considered highly variable.  \cr
 #'
 #' \strong{2. high COV based on loess regression prediction: } \cr
-#' A predicted COV is calculated for each feature using loess regression 
+#' A predicted COV is calculated for each feature using loess regression
 #' (COV~log(mean expression))
-#' Features that show a higher than predicted COV (\emph{difference_in_cov}) 
+#' Features that show a higher than predicted COV (\emph{difference_in_cov})
 #' are considered highly variable. \cr
 #'
 #' @md
 #' @examples
 #' g <- GiottoData::loadGiottoMini("visium")
-#' 
+#'
 #' calculateHVF(g)
 #' @export
-calculateHVF <- function(gobject,
-    spat_unit = NULL,
-    feat_type = NULL,
-    expression_values = c("normalized", "scaled", "custom"),
-    method = c("cov_groups", "cov_loess", "var_p_resid"),
-    reverse_log_scale = FALSE,
-    logbase = 2,
-    expression_threshold = 0,
-    nr_expression_groups = 20,
-    zscore_threshold = 1.5,
-    HVFname = "hvf",
-    difference_in_cov = 0.1,
-    var_threshold = 1.5,
-    var_number = NULL,
-    random_subset = NULL,
-    set_seed = TRUE,
-    seed_number = 1234,
-    show_plot = NULL,
-    return_plot = NULL,
-    save_plot = NULL,
-    save_param = list(),
-    default_save_name = "HVFplot",
-    return_gobject = TRUE) {
+calculateHVF <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        expression_values = c("normalized", "scaled", "custom"),
+        method = c("cov_groups", "cov_loess", "var_p_resid"),
+        reverse_log_scale = FALSE,
+        logbase = 2,
+        expression_threshold = 0,
+        nr_expression_groups = 20,
+        zscore_threshold = 1.5,
+        HVFname = "hvf",
+        difference_in_cov = 0.1,
+        var_threshold = 1.5,
+        var_number = NULL,
+        random_subset = NULL,
+        set_seed = TRUE,
+        seed_number = 1234,
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "HVFplot",
+        return_gobject = TRUE,
+        verbose = TRUE) {
     # NSE vars
     selected <- feats <- var <- NULL
 
@@ -317,8 +335,9 @@ calculateHVF <- function(gobject,
 
     # expression values to be used
     values <- match.arg(
-        expression_values, 
-        unique(c("normalized", "scaled", "custom", expression_values)))
+        expression_values,
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
     expr_values <- getExpression(
         gobject = gobject,
         spat_unit = spat_unit,
@@ -336,7 +355,9 @@ calculateHVF <- function(gobject,
     if (!is.null(random_subset)) {
         if (isTRUE(set_seed)) set.seed(seed = seed_number)
 
-        random_selection <- sort(sample(1:ncol(expr_values), random_subset))
+        random_selection <- sort(sample(
+            seq_len(ncol(expr_values)), random_subset
+        ))
         expr_values <- expr_values[, random_selection]
 
         if (isTRUE(set_seed)) GiottoUtils::random_seed()
@@ -345,20 +366,25 @@ calculateHVF <- function(gobject,
 
 
     # print, return and save parameters
-    show_plot <- ifelse(is.na(show_plot), 
-                        readGiottoInstructions(gobject, param = "show_plot"), 
-                        show_plot)
-    save_plot <- ifelse(is.na(save_plot), 
-                        readGiottoInstructions(gobject, param = "save_plot"), 
-                        save_plot)
-    return_plot <- ifelse(is.na(return_plot), 
-                        readGiottoInstructions(gobject, param = "return_plot"), 
-                        return_plot)
+    show_plot <- ifelse(is.na(show_plot),
+        readGiottoInstructions(gobject, param = "show_plot"),
+        show_plot
+    )
+    save_plot <- ifelse(is.na(save_plot),
+        readGiottoInstructions(gobject, param = "save_plot"),
+        save_plot
+    )
+    return_plot <- ifelse(is.na(return_plot),
+        readGiottoInstructions(gobject, param = "return_plot"),
+        return_plot
+    )
 
 
     # method to use
     method <- match.arg(
-        method, choices = c("cov_groups", "cov_loess", "var_p_resid"))
+        method,
+        choices = c("cov_groups", "cov_loess", "var_p_resid")
+    )
     # select function to use based on whether future parallelization is planned
     calc_cov_fun <- ifelse(
         use_parallel,
@@ -414,16 +440,19 @@ calculateHVF <- function(gobject,
     ## save plot
     if (isTRUE(save_plot)) {
         do.call(
-            GiottoVisuals::all_plots_save_function, 
-            c(list(gobject = gobject, plot_object = pl, 
-                    default_save_name = default_save_name), save_param))
+            GiottoVisuals::all_plots_save_function,
+            c(list(
+                gobject = gobject, plot_object = pl,
+                default_save_name = default_save_name
+            ), save_param)
+        )
     }
 
     ## return plot
     if (isTRUE(return_plot)) {
         if (isTRUE(return_gobject)) {
             message("return_plot = TRUE and return_gobject = TRUE \n
-                    plot will not be returned to object, but can still be 
+                    plot will not be returned to object, but can still be
                     saved with save_plot = TRUE or manually")
         } else {
             return(pl)
@@ -443,7 +472,10 @@ calculateHVF <- function(gobject,
         column_names_feat_metadata <- colnames(feat_metadata[])
 
         if (HVFname %in% column_names_feat_metadata) {
-            cat(HVFname, " has already been used, will be overwritten")
+            vmsg(
+                .v = verbose, HVFname,
+                " has already been used, will be overwritten"
+            )
             feat_metadata[][, eval(HVFname) := NULL]
 
             ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
@@ -490,8 +522,7 @@ calculateHVF <- function(gobject,
 
 
 # plot generation ####
-.create_cov_group_hvf_plot <- function(
-        feat_in_cells_detected, nr_expression_groups) {
+.create_cov_group_hvf_plot <- function(feat_in_cells_detected, nr_expression_groups) {
     pl <- ggplot2::ggplot()
     pl <- pl + ggplot2::theme_classic() +
         ggplot2::theme(
@@ -499,8 +530,9 @@ calculateHVF <- function(gobject,
             axis.text = ggplot2::element_text(size = 12)
         )
     pl <- pl + ggplot2::geom_point(
-        data = feat_in_cells_detected, 
-        ggplot2::aes_string(x = "mean_expr", y = "cov", color = "selected"))
+        data = feat_in_cells_detected,
+        ggplot2::aes_string(x = "mean_expr", y = "cov", color = "selected")
+    )
     pl <- pl + ggplot2::scale_color_manual(
         values = c(no = "lightgrey", yes = "orange"),
         guide = ggplot2::guide_legend(
@@ -509,7 +541,9 @@ calculateHVF <- function(gobject,
         )
     )
     pl <- pl + ggplot2::facet_wrap(
-        ~expr_groups, ncol = nr_expression_groups, scales = "free_x")
+        ~expr_groups,
+        ncol = nr_expression_groups, scales = "free_x"
+    )
     pl <- pl + ggplot2::theme(
         axis.text.x = ggplot2::element_blank(),
         strip.text = ggplot2::element_text(size = 4)
@@ -519,8 +553,7 @@ calculateHVF <- function(gobject,
 }
 
 
-.create_cov_loess_hvf_plot <- function(
-        feat_in_cells_detected, difference_in_cov, var_col) {
+.create_cov_loess_hvf_plot <- function(feat_in_cells_detected, difference_in_cov, var_col) {
     pl <- ggplot2::ggplot()
     pl <- pl + ggplot2::theme_classic() +
         ggplot2::theme(
@@ -528,17 +561,22 @@ calculateHVF <- function(gobject,
             axis.text = ggplot2::element_text(size = 12)
         )
     pl <- pl + ggplot2::geom_point(
-        data = feat_in_cells_detected, 
-        ggplot2::aes_string(x = "log(mean_expr)", y = var_col, 
-                            color = "selected"))
+        data = feat_in_cells_detected,
+        ggplot2::aes_string(
+            x = "log(mean_expr)", y = var_col,
+            color = "selected"
+        )
+    )
     pl <- pl + ggplot2::geom_line(
-        data = feat_in_cells_detected, 
-        ggplot2::aes_string(x = "log(mean_expr)", y = "pred_cov_feats"), 
-        color = "blue")
+        data = feat_in_cells_detected,
+        ggplot2::aes_string(x = "log(mean_expr)", y = "pred_cov_feats"),
+        color = "blue"
+    )
     hvg_line <- paste0("pred_cov_feats+", difference_in_cov)
     pl <- pl + ggplot2::geom_line(
-        data = feat_in_cells_detected, 
-        ggplot2::aes_string(x = "log(mean_expr)", y = hvg_line), linetype = 2)
+        data = feat_in_cells_detected,
+        ggplot2::aes_string(x = "log(mean_expr)", y = hvg_line), linetype = 2
+    )
     pl <- pl + ggplot2::labs(x = "log(mean expression)", y = var_col)
     pl <- pl + ggplot2::scale_color_manual(
         values = c(no = "lightgrey", yes = "orange"),
@@ -554,7 +592,8 @@ calculateHVF <- function(gobject,
 .create_calc_var_hvf_plot <- function(dt_res) {
     pl <- ggplot2::ggplot()
     pl <- pl + ggplot2::geom_point(
-        data = dt_res, aes_string(x = "rank", y = "var", color = "selected"))
+        data = dt_res, aes_string(x = "rank", y = "var", color = "selected")
+    )
     pl <- pl + ggplot2::scale_x_reverse()
     pl <- pl + ggplot2::theme_classic() + ggplot2::theme(
         axis.title = ggplot2::element_text(size = 14),
diff --git a/R/wnn.R b/R/wnn.R
index af8be2427..588d11888 100644
--- a/R/wnn.R
+++ b/R/wnn.R
@@ -13,22 +13,23 @@
 #' @param w_name_modality_2 name for modality 2 weights
 #' @param verbose be verbose
 #'
-#' @returns A Giotto object with integrated UMAP (integrated.umap) within the 
-#' dimension_reduction slot and Leiden clusters (integrated_leiden_clus) in the 
+#' @returns A Giotto object with integrated UMAP (integrated.umap) within the
+#' dimension_reduction slot and Leiden clusters (integrated_leiden_clus) in the
 #' cellular metadata.
 #' @export
-runWNN <- function(gobject,
-    spat_unit = "cell",
-    modality_1 = "rna",
-    modality_2 = "protein",
-    pca_name_modality_1 = "rna.pca",
-    pca_name_modality_2 = "protein.pca",
-    k = 20,
-    integrated_feat_type = NULL,
-    matrix_result_name = NULL,
-    w_name_modality_1 = NULL,
-    w_name_modality_2 = NULL,
-    verbose = FALSE) {
+runWNN <- function(
+        gobject,
+        spat_unit = "cell",
+        modality_1 = "rna",
+        modality_2 = "protein",
+        pca_name_modality_1 = "rna.pca",
+        pca_name_modality_2 = "protein.pca",
+        k = 20,
+        integrated_feat_type = NULL,
+        matrix_result_name = NULL,
+        w_name_modality_1 = NULL,
+        w_name_modality_2 = NULL,
+        verbose = FALSE) {
     # validate Giotto object
     if (!inherits(gobject, "giotto")) {
         stop("gobject needs to be a giotto object")
@@ -36,9 +37,9 @@ runWNN <- function(gobject,
 
     # validate modalities
     if (!modality_1 %in% names(
-        gobject@dimension_reduction$cells[[spat_unit]]) || 
-        !modality_2 %in% names(gobject@dimension_reduction$cells[[spat_unit]])) 
-        {
+        gobject@dimension_reduction$cells[[spat_unit]]
+    ) ||
+        !modality_2 %in% names(gobject@dimension_reduction$cells[[spat_unit]])) {
         stop(paste(modality_1, "and", modality_2, " pca must exist"))
     }
 
@@ -124,18 +125,24 @@ runWNN <- function(gobject,
 
     ## modality1 modality1
 
-    if (verbose) 
-        message(paste("Calculating low dimensional cell-cell distances for", 
-                    modality_1))
+    if (verbose) {
+        message(paste(
+            "Calculating low dimensional cell-cell distances for",
+            modality_1
+        ))
+    }
 
     all_cell_distances_1_1 <- dist(pca_1)
     all_cell_distances_1_1 <- as.matrix(all_cell_distances_1_1)
 
     ## modality2 modality2
 
-    if (verbose) 
-        message(paste("Calculating low dimensional cell-cell distances for", 
-                    modality_2))
+    if (verbose) {
+        message(paste(
+            "Calculating low dimensional cell-cell distances for",
+            modality_2
+        ))
+    }
 
 
     all_cell_distances_2_2 <- dist(pca_2)
@@ -234,13 +241,15 @@ runWNN <- function(gobject,
 
         if (nrow(jaccard_values == 20)) {
             further_cell_cell_distances <- all_cell_distances_1_1[
-                cell_a, jaccard_values$to]
+                cell_a, jaccard_values$to
+            ]
         } else {
             further_cell_cell_distances <- tail(sort(all_cell_distances_1_1[
-                cell_a, ]), 20)
+                cell_a,
+            ]), 20)
         }
 
-        modality1_sigma_i[cell_a] <- mean(further_cell_cell_distances) 
+        modality1_sigma_i[cell_a] <- mean(further_cell_cell_distances)
         # cell-specific kernel bandwidth.
     }
 
@@ -254,13 +263,15 @@ runWNN <- function(gobject,
 
         if (nrow(jaccard_values == 20)) {
             further_cell_cell_distances <- all_cell_distances_2_2[
-                cell_a, jaccard_values$to]
+                cell_a, jaccard_values$to
+            ]
         } else {
             further_cell_cell_distances <- tail(sort(all_cell_distances_2_2[
-                cell_a, ]), 20)
+                cell_a,
+            ]), 20)
         }
 
-        modality2_sigma_i[cell_a] <- mean(further_cell_cell_distances) 
+        modality2_sigma_i[cell_a] <- mean(further_cell_cell_distances)
         # cell-specific kernel bandwidth.
     }
 
@@ -282,13 +293,13 @@ runWNN <- function(gobject,
         d_modality1_i_modality1_knn1 <- sqrt(sum((
             modality1_i - modality1_knn1)^2))
 
-        difference_distances <- d_modality1_i_modality2_predicted - 
+        difference_distances <- d_modality1_i_modality2_predicted -
             d_modality1_i_modality1_knn1
         max_value <- max(c(difference_distances, 0))
 
         theta_1_1[[cell_a]] <- exp((
-            -max_value) / (modality1_sigma_i[cell_a] - 
-                            d_modality1_i_modality1_knn1))
+            -max_value) / (modality1_sigma_i[cell_a] -
+            d_modality1_i_modality1_knn1))
     }
 
     ## modality2 modality2
@@ -304,13 +315,13 @@ runWNN <- function(gobject,
         d_modality2_i_modality2_knn1 <- sqrt(sum((
             modality2_i - modality2_knn1)^2))
 
-        difference_distances <- d_modality2_i_modality2_predicted - 
+        difference_distances <- d_modality2_i_modality2_predicted -
             d_modality2_i_modality2_knn1
         max_value <- max(c(difference_distances, 0))
 
         theta_modality2_modality2[[cell_a]] <- exp((
-            -max_value) / (modality2_sigma_i[cell_a] - 
-                            d_modality2_i_modality2_knn1))
+            -max_value) / (modality2_sigma_i[cell_a] -
+            d_modality2_i_modality2_knn1))
     }
 
 
@@ -327,13 +338,13 @@ runWNN <- function(gobject,
         d_modality1_i_modality1_knn1 <- sqrt(sum((
             modality1_i - modality1_knn1)^2))
 
-        difference_distances <- d_modality1_i_modality2_predicted - 
+        difference_distances <- d_modality1_i_modality2_predicted -
             d_modality1_i_modality1_knn1
         max_value <- max(c(difference_distances, 0))
 
         theta_modality1_modality2[[cell_a]] <- exp((
-            -max_value) / (modality1_sigma_i[cell_a] - 
-                            d_modality1_i_modality1_knn1))
+            -max_value) / (modality1_sigma_i[cell_a] -
+            d_modality1_i_modality1_knn1))
     }
 
 
@@ -350,13 +361,13 @@ runWNN <- function(gobject,
         d_modality2_i_modality2_knn1 <- sqrt(sum((
             modality2_i - modality2_knn1)^2))
 
-        difference_distances <- d_modality2_i_modality1_predicted - 
+        difference_distances <- d_modality2_i_modality1_predicted -
             d_modality2_i_modality2_knn1
         max_value <- max(c(difference_distances, 0))
 
         theta_modality2_modality1[[cell_a]] <- exp((
-            -max_value) / (modality2_sigma_i[cell_a] - 
-                            d_modality2_i_modality2_knn1))
+            -max_value) / (modality2_sigma_i[cell_a] -
+            d_modality2_i_modality2_knn1))
     }
 
 
@@ -370,7 +381,7 @@ runWNN <- function(gobject,
     ratio_modality1 <- list()
 
     for (cell_a in cell_names) {
-        ratio_modality1[[cell_a]] <- theta_1_1[[cell_a]] / 
+        ratio_modality1[[cell_a]] <- theta_1_1[[cell_a]] /
             (theta_modality1_modality2[[cell_a]] + epsilon)
     }
 
@@ -379,7 +390,7 @@ runWNN <- function(gobject,
     ratio_modality2 <- list()
 
     for (cell_a in cell_names) {
-        ratio_modality2[[cell_a]] <- theta_modality2_modality2[[cell_a]] / 
+        ratio_modality2[[cell_a]] <- theta_modality2_modality2[[cell_a]] /
             (theta_modality2_modality1[[cell_a]] + epsilon)
     }
 
@@ -392,7 +403,7 @@ runWNN <- function(gobject,
     names(w_modality1) <- cell_names
 
     for (cell_a in cell_names) {
-        w_modality1[cell_a] <- exp(ratio_modality1[[cell_a]]) / 
+        w_modality1[cell_a] <- exp(ratio_modality1[[cell_a]]) /
             (exp(ratio_modality1[[cell_a]]) + exp(ratio_modality2[[cell_a]]))
     }
 
@@ -400,7 +411,7 @@ runWNN <- function(gobject,
     names(w_modality2) <- cell_names
 
     for (cell_a in cell_names) {
-        w_modality2[cell_a] <- exp(ratio_modality2[[cell_a]]) / 
+        w_modality2[cell_a] <- exp(ratio_modality2[[cell_a]]) /
             (exp(ratio_modality1[[cell_a]]) + exp(ratio_modality2[[cell_a]]))
     }
 
@@ -421,15 +432,15 @@ runWNN <- function(gobject,
 
     ## theta_modality1
 
-    theta_modality1_cella_cellb <- exp(-1 * (all_cell_distances_1_1 / 
-                                                modality1_sigma_i)**kernelpower)
+    theta_modality1_cella_cellb <- exp(-1 * (all_cell_distances_1_1 /
+        modality1_sigma_i)**kernelpower)
 
     ## theta_modality2
-    theta_modality2_cella_cellb <- exp(-1 * (all_cell_distances_2_2 / 
-                                                modality2_sigma_i)**kernelpower)
+    theta_modality2_cella_cellb <- exp(-1 * (all_cell_distances_2_2 /
+        modality2_sigma_i)**kernelpower)
 
     ## theta_weighted
-    theta_weighted <- w_modality1 * theta_modality1_cella_cellb + 
+    theta_weighted <- w_modality1 * theta_modality1_cella_cellb +
         w_modality2 * theta_modality2_cella_cellb
 
 
@@ -511,18 +522,19 @@ runWNN <- function(gobject,
 #'
 #' @returns A Giotto object with integrated UMAP
 #' @export
-runIntegratedUMAP <- function(gobject,
-    spat_unit = "cell",
-    modality1 = "rna",
-    modality2 = "protein",
-    integrated_feat_type = NULL,
-    integration_method = "WNN",
-    matrix_result_name = "theta_weighted_matrix",
-    k = 20,
-    spread = 5,
-    min_dist = 0.01,
-    force = FALSE,
-    ...) {
+runIntegratedUMAP <- function(
+        gobject,
+        spat_unit = "cell",
+        modality1 = "rna",
+        modality2 = "protein",
+        integrated_feat_type = NULL,
+        integration_method = "WNN",
+        matrix_result_name = "theta_weighted_matrix",
+        k = 20,
+        spread = 5,
+        min_dist = 0.01,
+        force = FALSE,
+        ...) {
     if (is.null(integrated_feat_type)) {
         integrated_feat_type <- paste0(modality1, "_", modality2)
     }
@@ -537,7 +549,8 @@ runIntegratedUMAP <- function(gobject,
     theta_weighted[is.na(theta_weighted)] <- 0
 
     if (is.null(gobject@nn_network[[spat_unit]][[
-        modality1]]$kNN$integrated_kNN) || force == TRUE) {
+        modality1
+    ]]$kNN$integrated_kNN) || force == TRUE) {
         ################# Calculate integrated Nearest Neighbors ###############
 
         message("Calculating integrated Nearest Neighbors")
@@ -545,11 +558,11 @@ runIntegratedUMAP <- function(gobject,
         cell_names <- colnames(theta_weighted)
 
         nn_network <- dbscan::kNN(x = theta_weighted, k = k, sort = TRUE)
-        from <- to <- weight <- distance <- from_cell_ID <- to_cell_ID <- 
+        from <- to <- weight <- distance <- from_cell_ID <- to_cell_ID <-
             shared <- NULL
         nn_network_dt <- data.table::data.table(
             from = rep(
-                1:nrow(nn_network$id),
+                seq_len(nrow(nn_network$id)),
                 k
             ),
             to = as.vector(nn_network$id),
@@ -559,7 +572,8 @@ runIntegratedUMAP <- function(gobject,
         nn_network_dt[, `:=`(from_cell_ID, cell_names[from])]
         nn_network_dt[, `:=`(to_cell_ID, cell_names[to])]
         all_index <- unique(
-            x = c(nn_network_dt$from_cell_ID, nn_network_dt$to_cell_ID))
+            x = c(nn_network_dt$from_cell_ID, nn_network_dt$to_cell_ID)
+        )
 
         ################################ Create igraph #########################
 
@@ -649,7 +663,8 @@ runIntegratedUMAP <- function(gobject,
 
     ## add umap
     gobject@dimension_reduction$cells[[spat_unit]][[modality1]][["umap"]][[
-        "integrated.umap"]] <- list(
+        "integrated.umap"
+    ]] <- list(
         name = "integrated.umap",
         feat_type = modality1,
         spat_unit = spat_unit,
@@ -659,7 +674,8 @@ runIntegratedUMAP <- function(gobject,
     )
 
     gobject@dimension_reduction$cells[[spat_unit]][[modality2]][["umap"]][[
-        "integrated.umap"]] <- list(
+        "integrated.umap"
+    ]] <- list(
         name = "integrated.umap",
         feat_type = modality2,
         spat_unit = spat_unit,
diff --git a/R/zzz.R b/R/zzz.R
index d15b5883c..9cfa6b5af 100644
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -19,4 +19,27 @@
         data.table::setDTthreads(threads = cores)
         options("giotto.check_core" = FALSE)
     }
+
+    # options #
+
+    # GiottoVisuals #
+    # ------------- #
+    # colors continuous
+    init_option("giotto.color_cd_pal", c("blue", "white", "red"))
+    init_option("giotto.color_cs_pal", "viridis")
+    init_option("giotto.color_c_rev", FALSE)
+
+    # colors discrete
+    init_option("giotto.color_d_pal", "distinct")
+    init_option("giotto.color_d_rev", FALSE)
+    init_option("giotto.color_d_strategy", "interpolate")
+
+    # image resampling
+    init_option("giotto.plot_img_max_sample", 5e5)
+    init_option("giotto.plot_img_max_crop", 1e8)
+    init_option("giotto.plot_img_max_resample_scale", 100)
+
+    # GiottoUtils #
+    # ----------- #
+    init_option("giotto.verbose", TRUE)
 }
diff --git a/man/adapt_aspect_ratio.Rd b/man/adapt_aspect_ratio.Rd
index d1f7daa92..e6b170194 100644
--- a/man/adapt_aspect_ratio.Rd
+++ b/man/adapt_aspect_ratio.Rd
@@ -30,7 +30,7 @@ adapt_aspect_ratio(
 numeric
 }
 \description{
-adapt the aspact ratio after inserting cross section mesh grid 
+adapt the aspact ratio after inserting cross section mesh grid
 lines
 }
 \keyword{internal}
diff --git a/man/addCellStatistics.Rd b/man/addCellStatistics.Rd
index 9abfb793b..42707b57f 100644
--- a/man/addCellStatistics.Rd
+++ b/man/addCellStatistics.Rd
@@ -10,7 +10,8 @@ addCellStatistics(
   spat_unit = NULL,
   expression_values = c("normalized", "scaled", "custom"),
   detection_threshold = 0,
-  return_gobject = TRUE
+  return_gobject = TRUE,
+  verbose = TRUE
 )
 }
 \arguments{
@@ -25,6 +26,8 @@ addCellStatistics(
 \item{detection_threshold}{detection threshold to consider a gene detected}
 
 \item{return_gobject}{boolean: return giotto object (default = TRUE)}
+
+\item{verbose}{be verbose}
 }
 \value{
 giotto object if return_gobject = TRUE
diff --git a/man/addFeatStatistics.Rd b/man/addFeatStatistics.Rd
index 84b5f4e91..c532b733b 100644
--- a/man/addFeatStatistics.Rd
+++ b/man/addFeatStatistics.Rd
@@ -10,7 +10,8 @@ addFeatStatistics(
   spat_unit = NULL,
   expression_values = c("normalized", "scaled", "custom"),
   detection_threshold = 0,
-  return_gobject = TRUE
+  return_gobject = TRUE,
+  verbose = TRUE
 )
 }
 \arguments{
@@ -25,6 +26,8 @@ addFeatStatistics(
 \item{detection_threshold}{detection threshold to consider a gene detected}
 
 \item{return_gobject}{boolean: return giotto object (default = TRUE)}
+
+\item{verbose}{be verbose}
 }
 \value{
 giotto object if return_gobject = TRUE
diff --git a/man/addHMRF.Rd b/man/addHMRF.Rd
index 93d5961f6..2e398efcb 100644
--- a/man/addHMRF.Rd
+++ b/man/addHMRF.Rd
@@ -35,3 +35,31 @@ giotto object
 \description{
 Add selected results from doHMRF to the giotto object
 }
+\examples{
+g <- GiottoData::loadGiottoMini("visium")
+spat_genes <- binSpect(g)
+
+output_folder <- file.path(tempdir(), "HMRF")
+if (!file.exists(output_folder)) dir.create(output_folder, recursive = TRUE)
+
+out <- doHMRF(
+    g,
+    spatial_genes = spat_genes[seq_len(20)]$feats,
+    expression_values = "scaled",
+    spatial_network_name = "Delaunay_network",
+    k = 6, betas = c(0, 10, 5),
+    output_folder = output_folder
+)
+
+g <- addHMRF(
+    gobject = g,
+    HMRFoutput = out,
+    k = 6,
+    betas_to_add = 20,
+    hmrf_name = "HMRF"
+)
+
+spatPlot(
+    gobject = g, cell_color = "HMRF_k6_b.20",
+)
+}
diff --git a/man/addHMRF_V2.Rd b/man/addHMRF_V2.Rd
index 33c17f775..4c89f66aa 100644
--- a/man/addHMRF_V2.Rd
+++ b/man/addHMRF_V2.Rd
@@ -20,8 +20,16 @@ giotto object
 function to add HMRF Domain Type to cell meta data
 }
 \details{
-This function appends HMRF domain clusters to corresponding cell meta data 
-for all the beta values, with the given HMRF model names. For example, if 
-name = ‘hmrf1’ and name of result in HMRFoutput is ‘k=8 b=0.00’, the 
+This function appends HMRF domain clusters to corresponding cell meta data
+for all the beta values, with the given HMRF model names. For example, if
+name = ‘hmrf1’ and name of result in HMRFoutput is ‘k=8 b=0.00’, the
 appended cell meta data column will be named with ‘hmrf1 k=8 b=0.00’
 }
+\examples{
+g <- GiottoData::loadGiottoMini("visium")
+g <- binSpect(g, return_gobject = TRUE)
+HMRF_init_obj <- initHMRF_V2(gobject = g, cl.method = "km")
+HMRFoutput <- doHMRF_V2(HMRF_init_obj = HMRF_init_obj, betas = c(0, 5, 2))
+
+addHMRF_V2(gobject = g, HMRFoutput = HMRFoutput)
+}
diff --git a/man/addPolygonCells.Rd b/man/addPolygonCells.Rd
index 9386b5c60..0a3708da8 100644
--- a/man/addPolygonCells.Rd
+++ b/man/addPolygonCells.Rd
@@ -25,15 +25,15 @@ addPolygonCells(
 
 \item{feat_type}{feature name where metadata will be added}
 
-\item{polygons}{polygon names to plot (e.g. 'polygon_1'). If NULL, plots 
+\item{polygons}{polygon names to plot (e.g. 'polygon_1'). If NULL, plots
 all available polygons}
 
-\item{na.label}{polygon label for cells located outside of polygons area. 
+\item{na.label}{polygon label for cells located outside of polygons area.
 Default = "no_polygon"}
 }
 \value{
 A Giotto object with a modified cell_metadata slot that includes the
-polygon name where each cell is located or no_polygon label if the cell is 
+polygon name where each cell is located or no_polygon label if the cell is
 not located within a polygon area
 }
 \description{
@@ -42,17 +42,24 @@ Add corresponding polygon IDs to cell metadata
 \examples{
 ## Plot interactive polygons
 g <- GiottoData::loadGiottoMini("visium")
-my_polygon_coords <- data.frame(poly_ID = rep("polygon1", 3), 
-sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202))
+my_polygon_coords <- data.frame(
+    poly_ID = rep("polygon1", 3),
+    sdimx = c(5477, 5959, 4720),
+    sdimy = c(-4125, -2808, -5202)
+)
 
 ## Add polygon coordinates to Giotto object
-my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords, 
-name = "selections")
+my_giotto_polygons <- createGiottoPolygon(
+    my_polygon_coords,
+    name = "selections"
+)
+
 g <- addGiottoPolygons(
     gobject = g,
     gpolygons = list(my_giotto_polygons)
 )
 
 ## Add polygon IDs to cell metadata
-addPolygonCells(g)
+g <- addPolygonCells(g)
+pDataDT(g)
 }
diff --git a/man/addStatistics.Rd b/man/addStatistics.Rd
index 95df75bda..9fee79234 100644
--- a/man/addStatistics.Rd
+++ b/man/addStatistics.Rd
@@ -10,7 +10,8 @@ addStatistics(
   spat_unit = NULL,
   expression_values = c("normalized", "scaled", "custom"),
   detection_threshold = 0,
-  return_gobject = TRUE
+  return_gobject = TRUE,
+  verbose = TRUE
 )
 }
 \arguments{
@@ -25,6 +26,8 @@ addStatistics(
 \item{detection_threshold}{detection threshold to consider a feature detected}
 
 \item{return_gobject}{boolean: return giotto object (default = TRUE)}
+
+\item{verbose}{be verbose}
 }
 \value{
 giotto object if return_gobject = TRUE, else a list with results
diff --git a/man/binSpect.Rd b/man/binSpect.Rd
index 060f88f7b..996fb5436 100644
--- a/man/binSpect.Rd
+++ b/man/binSpect.Rd
@@ -208,8 +208,6 @@ separately (default)}
 \item{expression_matrix}{expression matrix}
 
 \item{spatial_network}{spatial network in data.table format}
-
-\item{subset_genes}{deprecated, use subset_feats}
 }
 \value{
 data.table with results (see details)
diff --git a/man/calculateHVF.Rd b/man/calculateHVF.Rd
index 9c826f008..d7d3a2de6 100644
--- a/man/calculateHVF.Rd
+++ b/man/calculateHVF.Rd
@@ -27,7 +27,8 @@ calculateHVF(
   save_plot = NULL,
   save_param = list(),
   default_save_name = "HVFplot",
-  return_gobject = TRUE
+  return_gobject = TRUE,
+  verbose = TRUE
 )
 }
 \arguments{
@@ -84,6 +85,8 @@ Passing \code{NULL} runs HVF on all cells.}
 save_name in save_param}
 
 \item{return_gobject}{boolean: return giotto object (default = TRUE)}
+
+\item{verbose}{be verbose}
 }
 \value{
 giotto object highly variable features appended to feature metadata
diff --git a/man/cellProximityBarplot.Rd b/man/cellProximityBarplot.Rd
index aef957080..bebc29ba1 100644
--- a/man/cellProximityBarplot.Rd
+++ b/man/cellProximityBarplot.Rd
@@ -47,9 +47,12 @@ Create barplot from cell-cell proximity scores
 \details{
 This function creates a barplot that shows the  spatial proximity
  enrichment or depletion of cell type pairs.
- @examples
- g <- GiottoData::loadGiottoMini("visium")
- 
- cellProximityBarplot(gobject = g, 
- CPscore = cellProximityEnrichment(g, cluster_column = "leiden_clus"))
+}
+\examples{
+g <- GiottoData::loadGiottoMini("visium")
+
+cellProximityBarplot(
+    gobject = g,
+    CPscore = cellProximityEnrichment(g, cluster_column = "leiden_clus")
+)
 }
diff --git a/man/cellProximityEnrichmentEachSpot.Rd b/man/cellProximityEnrichmentEachSpot.Rd
index 572232bc6..0d269746b 100644
--- a/man/cellProximityEnrichmentEachSpot.Rd
+++ b/man/cellProximityEnrichmentEachSpot.Rd
@@ -24,7 +24,7 @@ cellProximityEnrichmentEachSpot(
 \item{cluster_column}{name of column to use for clusters}
 }
 \value{
-matrix that rownames are cell-cell interaction pairs and colnames 
+matrix that rownames are cell-cell interaction pairs and colnames
 are cell_IDs
 }
 \description{
@@ -33,14 +33,16 @@ interacted spots (observed)
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
-x <- findMarkers_one_vs_all(g, 
-cluster_column = "leiden_clus", min_feats = 20)
+x <- findMarkers_one_vs_all(g,
+    cluster_column = "leiden_clus", min_feats = 20
+)
 sign_gene <- x$feats
 
-sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10), 
-nrow = length(sign_gene))
+sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+    nrow = length(sign_gene)
+)
 rownames(sign_matrix) <- sign_gene
-colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
+colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
 
 g <- runDWLSDeconv(gobject = g, sign_matrix = sign_matrix)
 
diff --git a/man/cellProximityEnrichmentSpots.Rd b/man/cellProximityEnrichmentSpots.Rd
index c1a7898ad..e618dbbc3 100644
--- a/man/cellProximityEnrichmentSpots.Rd
+++ b/man/cellProximityEnrichmentSpots.Rd
@@ -32,10 +32,10 @@ cellProximityEnrichmentSpots(
 
 \item{cells_in_spot}{cell number in each spot}
 
-\item{number_of_simulations}{number of simulations to create expected 
+\item{number_of_simulations}{number of simulations to create expected
 observations}
 
-\item{adjust_method}{method to adjust p.values 
+\item{adjust_method}{method to adjust p.values
 (e.g. "none", "fdr", "bonferroni","BH","holm", "hochberg", "hommel","BY")}
 
 \item{set_seed}{use of seed. Default = TRUE}
@@ -45,34 +45,36 @@ observations}
 \item{verbose}{be verbose}
 }
 \value{
-List of cell Proximity scores (CPscores) in data.table format. 
+List of cell Proximity scores (CPscores) in data.table format.
 The first
-data.table (raw_sim_table) shows the raw observations of both the original 
-and simulated networks. The second data.table (enrichm_res) shows the 
+data.table (raw_sim_table) shows the raw observations of both the original
+and simulated networks. The second data.table (enrichm_res) shows the
 enrichment results.
 }
 \description{
-Compute cell-cell interaction enrichment for spots 
+Compute cell-cell interaction enrichment for spots
 (observed vs expected)
 }
 \details{
-Spatial proximity enrichment or depletion between pairs of cell 
+Spatial proximity enrichment or depletion between pairs of cell
 types is calculated by calculating the observed over the expected frequency
-of cell-cell proximity interactions. The expected frequency is the average 
-frequency calculated from a number of spatial network simulations. Each 
-individual simulation is obtained by reshuffling the cell type labels of 
+of cell-cell proximity interactions. The expected frequency is the average
+frequency calculated from a number of spatial network simulations. Each
+individual simulation is obtained by reshuffling the cell type labels of
 each node (spot) in the spatial network.
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
-x <- findMarkers_one_vs_all(g, 
-cluster_column = "leiden_clus", min_feats = 20)
+x <- findMarkers_one_vs_all(g,
+    cluster_column = "leiden_clus", min_feats = 20
+)
 sign_gene <- x$feats
 
-sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10), 
-nrow = length(sign_gene))
+sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+    nrow = length(sign_gene)
+)
 rownames(sign_matrix) <- sign_gene
-colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
+colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
 
 g <- runDWLSDeconv(gobject = g, sign_matrix = sign_matrix)
 
diff --git a/man/cellProximityHeatmap.Rd b/man/cellProximityHeatmap.Rd
index 9cca766d1..7939e7d52 100644
--- a/man/cellProximityHeatmap.Rd
+++ b/man/cellProximityHeatmap.Rd
@@ -27,7 +27,7 @@ cellProximityHeatmap(
 
 \item{order_cell_types}{order cell types based on enrichment correlation}
 
-\item{color_breaks}{numerical vector of length 3 to represent min, mean 
+\item{color_breaks}{numerical vector of length 3 to represent min, mean
 and maximum}
 
 \item{color_names}{character color vector of length 3}
diff --git a/man/cellProximityNetwork.Rd b/man/cellProximityNetwork.Rd
index faf220369..1a67385bd 100644
--- a/man/cellProximityNetwork.Rd
+++ b/man/cellProximityNetwork.Rd
@@ -42,10 +42,10 @@ cellProximityNetwork(
 
 \item{rescale_edge_weights}{rescale edge weights (boolean)}
 
-\item{edge_weight_range_depletion}{numerical vector of length 2 to rescale 
+\item{edge_weight_range_depletion}{numerical vector of length 2 to rescale
 depleted edge weights}
 
-\item{edge_weight_range_enrichment}{numerical vector of length 2 to rescale 
+\item{edge_weight_range_enrichment}{numerical vector of length 2 to rescale
 enriched edge weights}
 
 \item{layout}{layout algorithm to use to draw nodes and edges}
diff --git a/man/cellProximitySpatPlot.Rd b/man/cellProximitySpatPlot.Rd
index 8b5875886..d6bd596a5 100644
--- a/man/cellProximitySpatPlot.Rd
+++ b/man/cellProximitySpatPlot.Rd
@@ -53,13 +53,13 @@ named vector of colors}
 ggplot
 }
 \description{
-Visualize 2D cell-cell interactions according to spatial 
+Visualize 2D cell-cell interactions according to spatial
 coordinates in ggplot mode
 }
 \details{
 Description of parameters.
 }
 \seealso{
-\code{\link{cellProximitySpatPlot2D}} and 
+\code{\link{cellProximitySpatPlot2D}} and
 \code{\link{cellProximitySpatPlot3D}} for 3D
 }
diff --git a/man/cellProximitySpatPlot2D.Rd b/man/cellProximitySpatPlot2D.Rd
index c3183fa5c..d6fbf5549 100644
--- a/man/cellProximitySpatPlot2D.Rd
+++ b/man/cellProximitySpatPlot2D.Rd
@@ -114,7 +114,7 @@ are used when this is TRUE. continuous colors when FALSE.}
 ggplot
 }
 \description{
-Visualize 2D cell-cell interactions according to spatial 
+Visualize 2D cell-cell interactions according to spatial
 coordinates in ggplot mode
 }
 \details{
@@ -125,6 +125,8 @@ g <- GiottoData::loadGiottoMini("visium")
 g <- createSpatialGrid(g, sdimx_stepsize = 5, sdimy_stepsize = 5)
 x <- cellProximityEnrichment(g, cluster_column = "leiden_clus")
 
-cellProximitySpatPlot2D(gobject = g, cluster_column = "leiden_clus",
-interaction_name = x)
+cellProximitySpatPlot2D(
+    gobject = g, cluster_column = "leiden_clus",
+    interaction_name = x
+)
 }
diff --git a/man/cellProximitySpatPlot3D.Rd b/man/cellProximitySpatPlot3D.Rd
index 7ee4885fb..79c56c1c2 100644
--- a/man/cellProximitySpatPlot3D.Rd
+++ b/man/cellProximitySpatPlot3D.Rd
@@ -111,7 +111,7 @@ are used when this is TRUE. continuous colors when FALSE.}
 plotly
 }
 \description{
-Visualize 3D cell-cell interactions according to spatial 
+Visualize 3D cell-cell interactions according to spatial
 coordinates in plotly mode
 }
 \details{
diff --git a/man/cellProximityVisPlot.Rd b/man/cellProximityVisPlot.Rd
index f095c8629..7b13edc05 100644
--- a/man/cellProximityVisPlot.Rd
+++ b/man/cellProximityVisPlot.Rd
@@ -111,7 +111,7 @@ cellProximityVisPlot(
 ggplot or plotly
 }
 \description{
-Visualize cell-cell interactions according to spatial 
+Visualize cell-cell interactions according to spatial
 coordinates
 }
 \details{
@@ -122,6 +122,8 @@ g <- GiottoData::loadGiottoMini("visium")
 g <- createSpatialGrid(g, sdimx_stepsize = 5, sdimy_stepsize = 5)
 x <- cellProximityEnrichment(g, cluster_column = "leiden_clus")
 
-cellProximityVisPlot(gobject = g, interaction_name = x,
-cluster_column = "leiden_clus", sdimx = "sdimx", sdimy = "sdimy")
+cellProximityVisPlot(
+    gobject = g, interaction_name = x,
+    cluster_column = "leiden_clus", sdimx = "sdimx", sdimy = "sdimy"
+)
 }
diff --git a/man/cellProximityVisPlot_internals.Rd b/man/cellProximityVisPlot_internals.Rd
index 631140393..226436e52 100644
--- a/man/cellProximityVisPlot_internals.Rd
+++ b/man/cellProximityVisPlot_internals.Rd
@@ -102,13 +102,13 @@ Create the plots for `cellProximityVisPlot()`
 }
 \section{Functions}{
 \itemize{
-\item \code{.cellProximityVisPlot_2D_ggplot()}: Visualize 2D cell-cell 
+\item \code{.cellProximityVisPlot_2D_ggplot()}: Visualize 2D cell-cell
 interactions according to spatial coordinates in ggplot mode
 
-\item \code{.cellProximityVisPlot_2D_plotly()}: Visualize 2D cell-cell 
+\item \code{.cellProximityVisPlot_2D_plotly()}: Visualize 2D cell-cell
 interactions according to spatial coordinates in plotly mode
 
-\item \code{.cellProximityVisPlot_3D_plotly()}: Visualize 3D cell-cell 
+\item \code{.cellProximityVisPlot_3D_plotly()}: Visualize 3D cell-cell
 interactions according to spatial coordinates in plotly mode
 
 }}
diff --git a/man/cell_proximity_spots.Rd b/man/cell_proximity_spots.Rd
index aca4dbbd8..b4101159e 100644
--- a/man/cell_proximity_spots.Rd
+++ b/man/cell_proximity_spots.Rd
@@ -16,7 +16,7 @@
 \arguments{
 \item{cell_IDs}{cell_IDs}
 
-\item{dwls_values}{data.table of cell type enrichment in each spot and 
+\item{dwls_values}{data.table of cell type enrichment in each spot and
 multiply by cell number in each spot}
 
 \item{pairs}{data.table of paired spots. Format: cell_ID1, cell_ID2, N}
@@ -34,10 +34,10 @@ external spots
 }
 \section{Functions}{
 \itemize{
-\item \code{.cell_proximity_spots_internal()}: Compute cell-cell interactions observed 
+\item \code{.cell_proximity_spots_internal()}: Compute cell-cell interactions observed
 value inner each spot
 
-\item \code{.cell_proximity_spots_external()}: Compute cell-cell interactions observed 
+\item \code{.cell_proximity_spots_external()}: Compute cell-cell interactions observed
 value for interacted spots
 
 \item \code{.cell_proximity_spots()}: Wrapper function
diff --git a/man/checkAndFixSpatialGenes.Rd b/man/checkAndFixSpatialGenes.Rd
index 366693aa2..4e4908fc0 100644
--- a/man/checkAndFixSpatialGenes.Rd
+++ b/man/checkAndFixSpatialGenes.Rd
@@ -27,13 +27,13 @@ checkAndFixSpatialGenes(
 character
 }
 \description{
-function to check the selected test name for spatial gene set 
+function to check the selected test name for spatial gene set
 in Giotto object
 }
 \details{
-This function checks the user specified test name of spatial gene set in 
+This function checks the user specified test name of spatial gene set in
 Giotto object.
-SilhouetteRank works only with score, and SilhouetteRankTest works only 
+SilhouetteRank works only with score, and SilhouetteRankTest works only
 with pval. Use parameter use_score to specify.
 }
 \keyword{internal}
diff --git a/man/chooseAvailableSpatialGenes.Rd b/man/chooseAvailableSpatialGenes.Rd
index 5b6787069..394498345 100644
--- a/man/chooseAvailableSpatialGenes.Rd
+++ b/man/chooseAvailableSpatialGenes.Rd
@@ -17,14 +17,14 @@ chooseAvailableSpatialGenes(gobject, spat_unit = NULL, feat_type = NULL)
 character
 }
 \description{
-function to find the test name for existing spatial gene sets 
+function to find the test name for existing spatial gene sets
 in Giotto
 }
 \details{
-This function outputs the available test name for existing spatial gene sets 
+This function outputs the available test name for existing spatial gene sets
 in Giotto,
 which could be used in parameter ‘name’ in `filterSpatialGenes`.
-Priorities for showing the spatial gene test names are ‘binSpect’ > 
+Priorities for showing the spatial gene test names are ‘binSpect’ >
 ‘silhouetteRankTest’ > ‘silhouetteRank’.
 }
 \keyword{internal}
diff --git a/man/clusterSpatialCorFeats.Rd b/man/clusterSpatialCorFeats.Rd
index 7c85aeaff..467bb8c49 100644
--- a/man/clusterSpatialCorFeats.Rd
+++ b/man/clusterSpatialCorFeats.Rd
@@ -33,5 +33,7 @@ Cluster based on spatially correlated features
 g <- GiottoData::loadGiottoMini("visium")
 
 clusterSpatialCorFeats(spatCorObject = detectSpatialCorFeats(
-g, method = "network"))
+    g,
+    method = "network"
+))
 }
diff --git a/man/combCCcom.Rd b/man/combCCcom.Rd
index 61d253f98..f31d5799b 100644
--- a/man/combCCcom.Rd
+++ b/man/combCCcom.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{combCCcom}
 \alias{combCCcom}
-\title{combCCcom}
+\title{Combine cell cell communication tables}
 \usage{
 combCCcom(
   spatialCC,
@@ -43,11 +43,20 @@ data.tables
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-exprCC <- exprCellCellcom(g, cluster_column = "leiden_clus",
-feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik")
-spatialCC <- spatCellCellcom(gobject = g, cluster_column = "leiden_clus",
-feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot",
-random_iter = 10)
+exprCC <- exprCellCellcom(g,
+    cluster_column = "leiden_clus",
+    feat_set_1 = "Gm19935",
+    feat_set_2 = "9630013A20Rik"
+)
+
+spatialCC <- spatCellCellcom(gobject = g,
+    cluster_column = "leiden_clus",
+    feat_set_1 = "Gm19935",
+    feat_set_2 = "9630013A20Rik",
+    verbose = "a lot",
+    random_iter = 10
+)
 
-combCCcom(spatialCC = spatialCC, exprCC = exprCC)
+combCC <- combCCcom(spatialCC = spatialCC, exprCC = exprCC)
+force(combCC)
 }
diff --git a/man/combineCPG.Rd b/man/combineCPG.Rd
index 0536ecbdc..f4aeb7a23 100644
--- a/man/combineCPG.Rd
+++ b/man/combineCPG.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{combineCPG}
 \alias{combineCPG}
-\title{combineCPG}
+\title{deprecated}
 \usage{
 combineCPG(...)
 }
@@ -12,11 +12,11 @@ combineCPG(...)
   \describe{
     \item{\code{icfObject}}{ICF (interaction changed feat) score object}
     \item{\code{selected_ints}}{subset of selected cell-cell interactions (optional)}
-    \item{\code{selected_feats}}{subset of selected Feats (optional)}
-    \item{\code{specific_feats_1}}{specific Featset combo
-(need to position match specific_genes_2)}
-    \item{\code{specific_feats_2}}{specific Featset combo
-(need to position match specific_genes_1)}
+    \item{\code{selected_feats}}{subset of selected Features (optional)}
+    \item{\code{specific_feats_1}}{specific Featureset combo
+(need to position match specific_feats_2)}
+    \item{\code{specific_feats_2}}{specific Featureset combo
+(need to position match specific_feats_1)}
     \item{\code{min_cells}}{minimum number of target cell type}
     \item{\code{min_int_cells}}{minimum number of interacting cell type}
     \item{\code{min_fdr}}{minimum adjusted p-value}
diff --git a/man/combineCellProximityGenes.Rd b/man/combineCellProximityGenes.Rd
index 1451edf7c..f1b2aeb6f 100644
--- a/man/combineCellProximityGenes.Rd
+++ b/man/combineCellProximityGenes.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{combineCellProximityGenes}
 \alias{combineCellProximityGenes}
-\title{combineCellProximityGenes}
+\title{deprecated}
 \usage{
 combineCellProximityGenes(...)
 }
diff --git a/man/combineICF.Rd b/man/combineICF.Rd
deleted file mode 100644
index 6cf6d1413..000000000
--- a/man/combineICF.Rd
+++ /dev/null
@@ -1,61 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/spatial_interaction.R
-\name{combineICF}
-\alias{combineICF}
-\title{combineICF}
-\usage{
-combineICF(
-  icfObject,
-  selected_ints = NULL,
-  selected_feats = NULL,
-  specific_feats_1 = NULL,
-  specific_feats_2 = NULL,
-  min_cells = 5,
-  min_int_cells = 3,
-  min_fdr = 0.05,
-  min_spat_diff = 0,
-  min_log2_fc = 0.5,
-  do_parallel = TRUE,
-  verbose = TRUE
-)
-}
-\arguments{
-\item{icfObject}{ICF (interaction changed feat) score object}
-
-\item{selected_ints}{subset of selected cell-cell interactions (optional)}
-
-\item{selected_feats}{subset of selected Feats (optional)}
-
-\item{specific_feats_1}{specific Featset combo
-(need to position match specific_genes_2)}
-
-\item{specific_feats_2}{specific Featset combo
-(need to position match specific_genes_1)}
-
-\item{min_cells}{minimum number of target cell type}
-
-\item{min_int_cells}{minimum number of interacting cell type}
-
-\item{min_fdr}{minimum adjusted p-value}
-
-\item{min_spat_diff}{minimum absolute spatial expression difference}
-
-\item{min_log2_fc}{minimum absolute log2 fold-change}
-
-\item{do_parallel}{run calculations in parallel with mclapply}
-
-\item{verbose}{verbose}
-}
-\value{
-icfObject that contains the filtered differential feats scores
-}
-\description{
-Combine ICF scores in a pairwise manner.
-}
-\examples{
-g <- GiottoData::loadGiottoMini("visium")
-g_icf <- findInteractionChangedFeats(g, cluster_column = "leiden_clus",
-selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
-
-combineICF(g_icf)
-}
diff --git a/man/combineICG.Rd b/man/combineICG.Rd
index 07a793b94..c90ca8c00 100644
--- a/man/combineICG.Rd
+++ b/man/combineICG.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{combineICG}
 \alias{combineICG}
-\title{combineICG}
+\title{deprecated}
 \usage{
 combineICG(...)
 }
@@ -12,11 +12,11 @@ combineICG(...)
   \describe{
     \item{\code{icfObject}}{ICF (interaction changed feat) score object}
     \item{\code{selected_ints}}{subset of selected cell-cell interactions (optional)}
-    \item{\code{selected_feats}}{subset of selected Feats (optional)}
-    \item{\code{specific_feats_1}}{specific Featset combo
-(need to position match specific_genes_2)}
-    \item{\code{specific_feats_2}}{specific Featset combo
-(need to position match specific_genes_1)}
+    \item{\code{selected_feats}}{subset of selected Features (optional)}
+    \item{\code{specific_feats_1}}{specific Featureset combo
+(need to position match specific_feats_2)}
+    \item{\code{specific_feats_2}}{specific Featureset combo
+(need to position match specific_feats_1)}
     \item{\code{min_cells}}{minimum number of target cell type}
     \item{\code{min_int_cells}}{minimum number of interacting cell type}
     \item{\code{min_fdr}}{minimum adjusted p-value}
diff --git a/man/combineInteractionChangedFeats.Rd b/man/combineInteractionChangedFeats.Rd
index 506685ce2..4851333e3 100644
--- a/man/combineInteractionChangedFeats.Rd
+++ b/man/combineInteractionChangedFeats.Rd
@@ -2,6 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{combineInteractionChangedFeats}
 \alias{combineInteractionChangedFeats}
+\alias{combineICF}
 \title{combineInteractionChangedFeats}
 \usage{
 combineInteractionChangedFeats(
@@ -18,6 +19,21 @@ combineInteractionChangedFeats(
   do_parallel = TRUE,
   verbose = TRUE
 )
+
+combineICF(
+  icfObject,
+  selected_ints = NULL,
+  selected_feats = NULL,
+  specific_feats_1 = NULL,
+  specific_feats_2 = NULL,
+  min_cells = 5,
+  min_int_cells = 3,
+  min_fdr = 0.05,
+  min_spat_diff = 0,
+  min_log2_fc = 0.5,
+  do_parallel = TRUE,
+  verbose = TRUE
+)
 }
 \arguments{
 \item{icfObject}{ICF (interaction changed feat) score object}
@@ -47,7 +63,8 @@ combineInteractionChangedFeats(
 \item{verbose}{verbose}
 }
 \value{
-combIcfObject that contains the filtered differential feature scores
+`combIcfObject` that contains the filtered differential feature
+scores
 }
 \description{
 Combine ICF scores in a pairwise manner.
@@ -55,8 +72,12 @@ Combine ICF scores in a pairwise manner.
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 g_icf <- findInteractionChangedFeats(g,
-cluster_column = "leiden_clus",
-selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
+    cluster_column = "leiden_clus",
+    selected_feats = c("Gna12", "Ccnd2", "Btbd17"),
+    nr_permutations = 10
+)
 
-combineInteractionChangedFeats(g_icf)
+cicf <- combineInteractionChangedFeats(g_icf)
+force(cicf)
+combineICF(g_icf) # this is a shortened alias
 }
diff --git a/man/combineInteractionChangedGenes.Rd b/man/combineInteractionChangedGenes.Rd
index 49014ddbf..061a625c4 100644
--- a/man/combineInteractionChangedGenes.Rd
+++ b/man/combineInteractionChangedGenes.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{combineInteractionChangedGenes}
 \alias{combineInteractionChangedGenes}
-\title{combineInteractionChangedGenes}
+\title{deprecated}
 \usage{
 combineInteractionChangedGenes(...)
 }
diff --git a/man/compareCellAbundance.Rd b/man/compareCellAbundance.Rd
index 992341757..0c96076de 100644
--- a/man/compareCellAbundance.Rd
+++ b/man/compareCellAbundance.Rd
@@ -35,12 +35,15 @@ Compare cell types percent per polygon
 \examples{
 ## Plot interactive polygons
 g <- GiottoData::loadGiottoMini("visium")
-my_polygon_coords <- data.frame(poly_ID = rep("polygon1", 3), 
-sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202))
+my_polygon_coords <- data.frame(
+    poly_ID = rep("polygon1", 3),
+    sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202)
+)
 
 ## Add polygon coordinates to Giotto object
-my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords, 
-name = "selections")
+my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords,
+    name = "selections"
+)
 g <- addGiottoPolygons(
     gobject = g,
     gpolygons = list(my_giotto_polygons)
diff --git a/man/comparePolygonExpression.Rd b/man/comparePolygonExpression.Rd
index cff74a382..238091577 100644
--- a/man/comparePolygonExpression.Rd
+++ b/man/comparePolygonExpression.Rd
@@ -26,10 +26,10 @@ comparePolygonExpression(
 
 \item{selected_feats}{vector of selected features to plot}
 
-\item{expression_values}{gene expression values to use 
+\item{expression_values}{gene expression values to use
 ("normalized", "scaled", "custom")}
 
-\item{method}{method to use to detect differentially expressed feats 
+\item{method}{method to use to detect differentially expressed feats
 ("scran", "gini", "mast")}
 
 \item{\dots}{Arguments passed to \link[ComplexHeatmap]{Heatmap}}
@@ -43,12 +43,15 @@ Compare gene expression between polygon areas
 \examples{
 ## Plot interactive polygons
 g <- GiottoData::loadGiottoMini("visium")
-my_polygon_coords <- data.frame(poly_ID = rep("polygon1", 3), 
-sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202))
+my_polygon_coords <- data.frame(
+    poly_ID = rep("polygon1", 3),
+    sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202)
+)
 
 ## Add polygon coordinates to Giotto object
-my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords, 
-name = "selections")
+my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords,
+    name = "selections"
+)
 g <- addGiottoPolygons(
     gobject = g,
     gpolygons = list(my_giotto_polygons)
diff --git a/man/convertEnsemblToGeneSymbol.Rd b/man/convertEnsemblToGeneSymbol.Rd
index 88d106181..ab4148656 100644
--- a/man/convertEnsemblToGeneSymbol.Rd
+++ b/man/convertEnsemblToGeneSymbol.Rd
@@ -15,7 +15,7 @@ convertEnsemblToGeneSymbol(matrix, species = c("mouse", "human"))
 expression matrix with gene symbols as rownames
 }
 \description{
-This function convert ensembl gene IDs from a matrix to 
+This function convert ensembl gene IDs from a matrix to
 official gene symbols
 }
 \details{
diff --git a/man/createArchRProj.Rd b/man/createArchRProj.Rd
index b43e48c6f..dd5971525 100644
--- a/man/createArchRProj.Rd
+++ b/man/createArchRProj.Rd
@@ -25,19 +25,19 @@ These files can be in one of the following formats: (i) scATAC tabix files,
 
 \item{genome}{A string indicating the default genome to be used for all ArchR
 functions. Currently supported values include "hg19","hg38","mm9", and "mm10".
-This value is stored as a global environment variable, not part of the 
+This value is stored as a global environment variable, not part of the
 ArchRProject.
 This can be overwritten on a per-function basis using the given function's
 geneAnnotationand genomeAnnotation parameter. For something other than one of
-the currently supported, see createGeneAnnnotation() and 
+the currently supported, see createGeneAnnnotation() and
 createGenomeAnnnotation()}
 
-\item{createArrowFiles_params}{list of parameters passed to 
+\item{createArrowFiles_params}{list of parameters passed to
 `ArchR::createArrowFiles`}
 
 \item{ArchRProject_params}{list of parameters passed to `ArchR::ArchRProject`}
 
-\item{addIterativeLSI_params}{list of parameters passed to 
+\item{addIterativeLSI_params}{list of parameters passed to
 `ArchR::addIterativeLSI`}
 
 \item{threads}{number of threads to use. Default = `ArchR::getArchRThreads()`}
@@ -47,7 +47,7 @@ createGenomeAnnnotation()}
 \item{verbose}{Default = TRUE}
 }
 \value{
-An ArchR project with GeneScoreMatrix, TileMatrix, and 
+An ArchR project with GeneScoreMatrix, TileMatrix, and
 TileMatrix-based LSI
 }
 \description{
diff --git a/man/createCrossSection.Rd b/man/createCrossSection.Rd
index fea26d186..76df8790e 100644
--- a/man/createCrossSection.Rd
+++ b/man/createCrossSection.Rd
@@ -6,6 +6,7 @@
 \usage{
 createCrossSection(
   gobject,
+  spat_unit = NULL,
   spat_loc_name = "raw",
   name = "cross_section",
   spatial_network_name = "Delaunay_network",
@@ -23,70 +24,75 @@ createCrossSection(
   planeVector1 = NULL,
   planeVector2 = NULL,
   mesh_grid_n = 20,
-  return_gobject = TRUE
+  return_gobject = TRUE,
+  verbose = NULL
 )
 }
 \arguments{
 \item{gobject}{giotto object}
 
+\item{spat_unit}{spatial unit}
+
 \item{spat_loc_name}{name of spatial locations}
 
 \item{name}{name of cress section object. (default = cross_sectino)}
 
-\item{spatial_network_name}{name of spatial network object. 
+\item{spatial_network_name}{name of spatial network object.
 (default = Delaunay_network)}
 
-\item{thickness_unit}{unit of the virtual section thickness. If "cell", 
-average size of the observed cells is used as length unit. If "natural", 
-the unit of cell location coordinates is used.(default = cell)}
+\item{thickness_unit}{unit of the virtual section thickness. If "cell",
+average size of the observed cells is used as length unit. If "natural",
+the unit of cell location coordinates is used. (default = cell)}
 
 \item{slice_thickness}{thickness of slice. default = 2}
 
-\item{cell_distance_estimate_method}{method to estimate average distance 
+\item{cell_distance_estimate_method}{method to estimate average distance
 between neighobring cells. (default = mean)}
 
-\item{extend_ratio}{deciding the span of the cross section meshgrid, as a 
-ratio of extension compared to the borders of the vitural tissue section. 
+\item{extend_ratio}{deciding the span of the cross section meshgrid, as a
+ratio of extension compared to the borders of the vitural tissue section.
 (default = 0.2)}
 
 \item{method}{method to define the cross section plane.
-If equation, the plane is defined by a four element numerical vector 
-(equation) in the form of c(A,B,C,D), corresponding to a plane with 
+If equation, the plane is defined by a four element numerical vector
+(equation) in the form of c(A,B,C,D), corresponding to a plane with
 equation Ax+By+Cz=D.
-If 3 points, the plane is define by the coordinates of 3 points, as given by 
+If 3 points, the plane is define by the coordinates of 3 points, as given by
 point1, point2, and point3.
-If point and norm vector, the plane is defined by the coordinates of one 
-point (point1) in the plane and the coordinates of one norm vector 
+If point and norm vector, the plane is defined by the coordinates of one
+point (point1) in the plane and the coordinates of one norm vector
 (normVector) to the plane.
-If point and two plane vector, the plane is defined by the coordinates of 
-one point (point1) in the plane and the coordinates of two vectors 
+If point and two plane vector, the plane is defined by the coordinates of
+one point (point1) in the plane and the coordinates of two vectors
 (planeVector1, planeVector2) in the plane.
 (default = equation)}
 
-\item{equation}{equation required by method "equation".equations needs to be 
-a numerical vector of length 4, in the form of c(A,B,C,D), which defines 
+\item{equation}{equation required by method "equation".equations needs to be
+a numerical vector of length 4, in the form of c(A,B,C,D), which defines
 plane Ax+By+Cz=D.}
 
-\item{point1}{coordinates of the first point required by method 
+\item{point1}{coordinates of the first point required by method
 "3 points","point and norm vector", and "point and two plane vectors".}
 
 \item{point2}{coordinates of the second point required by method "3 points"}
 
 \item{point3}{coordinates of the third point required by method "3 points"}
 
-\item{normVector}{coordinates of the norm vector required by method 
+\item{normVector}{coordinates of the norm vector required by method
 "point and norm vector"}
 
-\item{planeVector1}{coordinates of the first plane vector required by 
+\item{planeVector1}{coordinates of the first plane vector required by
 method "point and two plane vectors"}
 
-\item{planeVector2}{coordinates of the second plane vector required by 
+\item{planeVector2}{coordinates of the second plane vector required by
 method "point and two plane vectors"}
 
-\item{mesh_grid_n}{numer of meshgrid lines to generate along both directions 
+\item{mesh_grid_n}{numer of meshgrid lines to generate along both directions
 for the cross section plane.}
 
 \item{return_gobject}{boolean: return giotto object (default = TRUE)}
+
+\item{verbose}{be verbose}
 }
 \value{
 giotto object with updated spatial network slot
@@ -95,12 +101,21 @@ giotto object with updated spatial network slot
 Create a virtual 2D cross section.
 }
 \details{
-Creates a virtual 2D cross section object for a given spatial 
-network object. The users need to provide the definition of the cross 
+Creates a virtual 2D cross section object for a given spatial
+network object. The users need to provide the definition of the cross
 section plane (see method).
 }
 \examples{
-g <- GiottoData::loadGiottoMini("visium")
+g <- GiottoData::loadGiottoMini("starmap")
+
+g <- createCrossSection(
+    gobject = g,
+    method = "equation",
+    equation = c(0, 1, 0, 600),
+    extend_ratio = 0.6,
+    name = "new_cs",
+    return_gobject = TRUE
+)
 
-createCrossSection(gobject = g, spatial_network_name = "spatial_network")
+crossSectionPlot(g, name = "new_cs")
 }
diff --git a/man/createGiottoCosMxObject.Rd b/man/createGiottoCosMxObject.Rd
index 5343dde5a..2d0a13235 100644
--- a/man/createGiottoCosMxObject.Rd
+++ b/man/createGiottoCosMxObject.Rd
@@ -20,11 +20,11 @@ createGiottoCosMxObject(
 \item{cosmx_dir}{full path to the exported cosmx directory}
 
 \item{data_to_use}{which type(s) of expression data to build the gobject with
-Default is \code{'all'} information available. \code{'subcellular'} loads 
-the transcript coordinates only. \code{'aggregate'} loads the provided 
+Default is \code{'all'} information available. \code{'subcellular'} loads
+the transcript coordinates only. \code{'aggregate'} loads the provided
 aggregated expression matrix.}
 
-\item{remove_background_polygon}{try to remove background polygon 
+\item{remove_background_polygon}{try to remove background polygon
 (default: FALSE)}
 
 \item{background_algo}{algorithm to remove background polygon}
@@ -49,9 +49,9 @@ Given the path to a CosMx experiment directory, creates a Giotto
 object.
 }
 \details{
-[\strong{Expected Directory}] This function generates a giotto object when 
-given a link to a cosmx output directory. It expects the following items 
-within the directory where the \strong{bolded} portions are what this 
+[\strong{Expected Directory}] This function generates a giotto object when
+given a link to a cosmx output directory. It expects the following items
+within the directory where the \strong{bolded} portions are what this
 function matches against:
 \itemize{
   \item{\strong{CellComposite} (folder of images)}
@@ -66,23 +66,23 @@ function matches against:
 
 [\strong{Workflows}] Workflow to use is accessed through the data_to_use param
 \itemize{
-  \item{'all' - loads and requires subcellular information from tx_file and 
+  \item{'all' - loads and requires subcellular information from tx_file and
   fov_positions_file
-  and also the existing aggregated information 
+  and also the existing aggregated information
   (expression, spatial locations, and metadata)
   from exprMat_file and metadata_file.}
-  \item{'subcellular' - loads and requires subcellular information from 
+  \item{'subcellular' - loads and requires subcellular information from
   tx_file and
   fov_positions_file only.}
-  \item{'aggregate' - loads and requires the existing aggregate information 
-  (expression, spatial locations, and metadata) from exprMat_file and 
+  \item{'aggregate' - loads and requires the existing aggregate information
+  (expression, spatial locations, and metadata) from exprMat_file and
   metadata_file.}
 }
 
-[\strong{Images}] Images in the default CellComposite, CellLabels, 
+[\strong{Images}] Images in the default CellComposite, CellLabels,
 CompartmentLabels, and CellOverlay
-folders will be loaded as giotto largeImage objects in all workflows as 
-long as they are available. Additionally, CellComposite images will be 
+folders will be loaded as giotto largeImage objects in all workflows as
+long as they are available. Additionally, CellComposite images will be
 converted to giotto image objects, making plotting with
 these image objects more responsive when accessing them from a server.
 \code{\link{showGiottoImageNames}} can be used to see the available images.
diff --git a/man/createGiottoMerscopeObject.Rd b/man/createGiottoMerscopeObject.Rd
index d93a7caa5..23722c1de 100644
--- a/man/createGiottoMerscopeObject.Rd
+++ b/man/createGiottoMerscopeObject.Rd
@@ -37,10 +37,10 @@ createGiottoMerscopeObject(
 \arguments{
 \item{merscope_dir}{full path to the exported merscope directory}
 
-\item{data_to_use}{which of either the 'subcellular' or 'aggregate' 
+\item{data_to_use}{which of either the 'subcellular' or 'aggregate'
 information to use for object creation}
 
-\item{FOVs}{which FOVs to use when building the subcellular object. 
+\item{FOVs}{which FOVs to use when building the subcellular object.
 (default is NULL)
 NULL loads all FOVs (very slow)}
 
@@ -66,13 +66,13 @@ provided}
 a giotto object
 }
 \description{
-Given the path to a MERSCOPE experiment directory, creates a 
+Given the path to a MERSCOPE experiment directory, creates a
 Giotto object.
 }
 \details{
-[\strong{Expected Directory}] This function generates a giotto object when 
-given a link to a MERSCOPE output directory. It expects the following items 
-within the directory where the \strong{bolded} portions are what this 
+[\strong{Expected Directory}] This function generates a giotto object when
+given a link to a MERSCOPE output directory. It expects the following items
+within the directory where the \strong{bolded} portions are what this
 function matches against:
 \itemize{
   \item{\strong{cell_boundaries} (folder .hdf5 files)}
@@ -84,10 +84,10 @@ function matches against:
 }
 \section{Functions}{
 \itemize{
-\item \code{.createGiottoMerscopeObject_subcellular()}: Create giotto object with 
+\item \code{.createGiottoMerscopeObject_subcellular()}: Create giotto object with
 'subcellular' workflow
 
-\item \code{.createGiottoMerscopeObject_aggregate()}: Create giotto object with 'aggregate' 
+\item \code{.createGiottoMerscopeObject_aggregate()}: Create giotto object with 'aggregate'
 workflow
 
 }}
diff --git a/man/createGiottoObjectfromArchR.Rd b/man/createGiottoObjectfromArchR.Rd
index 35c8db106..1b7748a2b 100644
--- a/man/createGiottoObjectfromArchR.Rd
+++ b/man/createGiottoObjectfromArchR.Rd
@@ -20,10 +20,10 @@ createGiottoObjectfromArchR(
 
 \item{expression_feat}{Giotto object available features (e.g. atac, rna, ...)}
 
-\item{spatial_locs}{data.table or data.frame with coordinates for cell 
+\item{spatial_locs}{data.table or data.frame with coordinates for cell
 centroids}
 
-\item{sampleNames}{A character vector containing the ArchR project sample 
+\item{sampleNames}{A character vector containing the ArchR project sample
 name}
 
 \item{...}{additional arguments passed to `createGiottoObject`}
diff --git a/man/createGiottoVisiumObject.Rd b/man/createGiottoVisiumObject.Rd
index 6c7c17fae..3229754b9 100644
--- a/man/createGiottoVisiumObject.Rd
+++ b/man/createGiottoVisiumObject.Rd
@@ -39,7 +39,7 @@ createGiottoVisiumObject(
 
 \item{h5_tissue_positions_path}{path to tissue locations (.csv file)}
 
-\item{h5_image_png_path}{path to tissue .png file (optional). Image 
+\item{h5_image_png_path}{path to tissue .png file (optional). Image
 autoscaling looks for matches in the filename for either 'hires' or 'lowres'}
 
 \item{h5_json_scalefactors_path}{path to .json scalefactors (optional)}
@@ -56,15 +56,15 @@ autoscaling looks for matches in the filename for either 'hires' or 'lowres'}
 
 \item{ymin_adj}{deprecated}
 
-\item{instructions}{list of instructions or output result from 
+\item{instructions}{list of instructions or output result from
 \code{\link[GiottoClass]{createGiottoInstructions}}}
 
-\item{expression_matrix_class}{class of expression matrix to use 
+\item{expression_matrix_class}{class of expression matrix to use
 (e.g. 'dgCMatrix', 'DelayedArray')}
 
 \item{h5_file}{optional path to create an on-disk h5 file}
 
-\item{cores}{how many cores or threads to use to read data if paths are 
+\item{cores}{how many cores or threads to use to read data if paths are
 provided}
 
 \item{verbose}{be verbose}
@@ -73,7 +73,7 @@ provided}
 giotto object
 }
 \description{
-Create Giotto object directly from a 10X visium folder. Also 
+Create Giotto object directly from a 10X visium folder. Also
 accepts visium H5 outputs.
 }
 \details{
diff --git a/man/createGiottoXeniumObject.Rd b/man/createGiottoXeniumObject.Rd
index 0fddd0694..e738694d6 100644
--- a/man/createGiottoXeniumObject.Rd
+++ b/man/createGiottoXeniumObject.Rd
@@ -28,24 +28,24 @@ createGiottoXeniumObject(
 \item{load_format}{files formats from which to load the data. Either `csv` or
 `parquet` currently supported.}
 
-\item{h5_expression}{(boolean) whether to load cell_feature_matrix from .h5 
+\item{h5_expression}{(boolean) whether to load cell_feature_matrix from .h5
 file. Default is \code{TRUE}}
 
 \item{h5_gene_ids}{use gene symbols (default) or ensembl ids for the .h5 gene
 expression matrix}
 
-\item{gene_column_index}{which column from the features or genes .tsv file 
+\item{gene_column_index}{which column from the features or genes .tsv file
 to use for row ids}
 
-\item{bounds_to_load}{vector of boundary information to load 
+\item{bounds_to_load}{vector of boundary information to load
 (e.g. \code{'cell'}
 or \code{'nucleus'} by themselves or \code{c('cell', 'nucleus')} to load both
 at the same time.)}
 
-\item{qv_threshold}{Minimum Phred-scaled quality score cutoff to be included 
+\item{qv_threshold}{Minimum Phred-scaled quality score cutoff to be included
 as a subcellular transcript detection (default = 20)}
 
-\item{key_list}{(advanced) list of grep-based keywords to split the 
+\item{key_list}{(advanced) list of grep-based keywords to split the
 subcellular feature detections by feature type. See details}
 
 \item{instructions}{list of instructions or output result
@@ -60,7 +60,7 @@ provided}
 giotto object
 }
 \description{
-Given the path to a Xenium experiment output folder, creates a 
+Given the path to a Xenium experiment output folder, creates a
 Giotto object
 }
 \details{
@@ -68,20 +68,20 @@ Giotto object
 Xenium provides info on feature detections that include more than only the
 Gene Expression specific probes. Additional probes for QC are included:
 \emph{blank codeword}, \emph{negative control codeword}, and
-\emph{negative control probe}. These additional QC probes each occupy and 
-are treated as their own feature types so that they can largely remain 
+\emph{negative control probe}. These additional QC probes each occupy and
+are treated as their own feature types so that they can largely remain
 independent of the gene expression information.
 
 [\strong{key_list}]
 Related to \code{data_to_use = 'subcellular'} workflow only:
-Additional QC probe information is in the subcellular feature detections 
-information and must be separated from the gene expression information 
+Additional QC probe information is in the subcellular feature detections
+information and must be separated from the gene expression information
 during processing.
-The QC probes have prefixes that allow them to be selected from the rest of 
+The QC probes have prefixes that allow them to be selected from the rest of
 the feature IDs.
-Giotto uses a named list of keywords (\code{key_list}) to select these QC 
-probes, with the list names being the names that will be assigned as the 
-feature type of these feature detections. The default list is used when 
+Giotto uses a named list of keywords (\code{key_list}) to select these QC
+probes, with the list names being the names that will be assigned as the
+feature type of these feature detections. The default list is used when
 \code{key_list} = NULL.
 
 Default list:
diff --git a/man/createSpatialGenomicsObject.Rd b/man/createSpatialGenomicsObject.Rd
index 1571bcf4b..98650b81f 100644
--- a/man/createSpatialGenomicsObject.Rd
+++ b/man/createSpatialGenomicsObject.Rd
@@ -9,7 +9,7 @@ createSpatialGenomicsObject(sg_dir = NULL, instructions = NULL)
 \arguments{
 \item{sg_dir}{full path to the exported Spatial Genomics directory}
 
-\item{instructions}{new instructions 
+\item{instructions}{new instructions
 (e.g. result from createGiottoInstructions)}
 }
 \value{
diff --git a/man/create_crossSection_object.Rd b/man/create_crossSection_object.Rd
index dd6bbe9d5..495f0657e 100644
--- a/man/create_crossSection_object.Rd
+++ b/man/create_crossSection_object.Rd
@@ -26,38 +26,38 @@ create_crossSection_object(
 
 \item{method}{method to define the cross section plane.}
 
-\item{thickness_unit}{unit of the virtual section thickness. If "cell", 
-average size of the observed cells is used as length unit. If "natural", 
+\item{thickness_unit}{unit of the virtual section thickness. If "cell",
+average size of the observed cells is used as length unit. If "natural",
 the unit of cell location coordinates is used.(default = cell)}
 
 \item{slice_thickness}{thickness of slice}
 
-\item{cell_distance_estimate_method}{method to estimate average distance 
+\item{cell_distance_estimate_method}{method to estimate average distance
 between neighboring cells. (default = mean)}
 
-\item{extend_ratio}{deciding the span of the cross section meshgrid, as a 
-ratio of extension compared to the borders of the virtual tissue section. 
+\item{extend_ratio}{deciding the span of the cross section meshgrid, as a
+ratio of extension compared to the borders of the virtual tissue section.
 (default = 0.2)}
 
-\item{plane_equation}{a numerical vector of length 4, in the form of 
+\item{plane_equation}{a numerical vector of length 4, in the form of
 c(A,B,C,D), which defines plane Ax+By+Cz=D.}
 
-\item{mesh_grid_n}{number of meshgrid lines to generate along both 
+\item{mesh_grid_n}{number of meshgrid lines to generate along both
 directions for the cross section plane.}
 
 \item{mesh_obj}{object that stores the cross section meshgrid information.}
 
 \item{cell_subset}{cells selected by the cross section}
 
-\item{cell_subset_spatial_locations}{locations of cells selected by the 
+\item{cell_subset_spatial_locations}{locations of cells selected by the
 cross section}
 
-\item{cell_subset_projection_locations}{3D projection coordinates of 
+\item{cell_subset_projection_locations}{3D projection coordinates of
 selected cells onto the cross section plane}
 
 \item{cell_subset_projection_PCA}{pca of projection coordinates}
 
-\item{cell_subset_projection_coords}{2D PCA coordinates of selected cells 
+\item{cell_subset_projection_coords}{2D PCA coordinates of selected cells
 in the cross section plane}
 }
 \value{
diff --git a/man/crossSectionGenePlot.Rd b/man/crossSectionFeatPlot.Rd
similarity index 74%
rename from man/crossSectionGenePlot.Rd
rename to man/crossSectionFeatPlot.Rd
index 1352bab53..d8e939185 100644
--- a/man/crossSectionGenePlot.Rd
+++ b/man/crossSectionFeatPlot.Rd
@@ -1,11 +1,13 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/cross_section.R
-\name{crossSectionGenePlot}
-\alias{crossSectionGenePlot}
-\title{crossSectionGenePlot}
+\name{crossSectionFeatPlot}
+\alias{crossSectionFeatPlot}
+\title{crossSectionFeatPlot}
 \usage{
-crossSectionGenePlot(
+crossSectionFeatPlot(
   gobject = NULL,
+  spat_unit = NULL,
+  feat_type = NULL,
   spat_loc_name = "raw",
   crossSection_obj = NULL,
   name = NULL,
@@ -17,6 +19,10 @@ crossSectionGenePlot(
 \arguments{
 \item{gobject}{giotto object}
 
+\item{spat_unit}{spatial unit}
+
+\item{feat_type}{feature type}
+
 \item{spat_loc_name}{name of spatial locations}
 
 \item{crossSection_obj}{crossSection object}
@@ -34,8 +40,8 @@ change save_name in save_param}
 ggplot
 }
 \description{
-Visualize cells and gene expression in a virtual cross section
-according to spatial coordinates
+Visualize cells and feature expression in a virtual cross
+section according to spatial coordinates
 }
 \details{
 Description of parameters.
diff --git a/man/crossSectionGenePlot3D.Rd b/man/crossSectionFeatPlot3D.Rd
similarity index 67%
rename from man/crossSectionGenePlot3D.Rd
rename to man/crossSectionFeatPlot3D.Rd
index 6c613f91e..8d6339194 100644
--- a/man/crossSectionGenePlot3D.Rd
+++ b/man/crossSectionFeatPlot3D.Rd
@@ -1,14 +1,17 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/cross_section.R
-\name{crossSectionGenePlot3D}
-\alias{crossSectionGenePlot3D}
-\title{crossSectionGenePlot3D}
+\name{crossSectionFeatPlot3D}
+\alias{crossSectionFeatPlot3D}
+\title{crossSectionFeatPlot3D}
 \usage{
-crossSectionGenePlot3D(
+crossSectionFeatPlot3D(
   gobject,
+  spat_unit = NULL,
+  feat_type = NULL,
   crossSection_obj = NULL,
   name = NULL,
   spatial_network_name = "Delaunay_network",
+  show_other_cells = TRUE,
   other_cell_color = alpha("lightgrey", 0),
   default_save_name = "crossSectionGenePlot3D",
   ...
@@ -17,16 +20,22 @@ crossSectionGenePlot3D(
 \arguments{
 \item{gobject}{giotto object}
 
+\item{spat_unit}{spatial unit}
+
+\item{feat_type}{feature type}
+
 \item{crossSection_obj}{cross section object as alternative input. default = NULL.}
 
 \item{name}{name of virtual cross section to use}
 
 \item{spatial_network_name}{name of spatial network to use}
 
-\item{other_cell_color}{color of cells outside the cross section. 
+\item{show_other_cells}{logical. Default = TRUE}
+
+\item{other_cell_color}{color of cells outside the cross section.
 default = transparent.}
 
-\item{default_save_name}{default save name for saving, don't change, change 
+\item{default_save_name}{default save name for saving, don't change, change
 save_name in save_param}
 
 \item{...}{parameters for spatGenePlot3D}
@@ -35,8 +44,8 @@ save_name in save_param}
 ggplot
 }
 \description{
-Visualize cells and gene expression in a virtual cross section 
-according to spatial coordinates
+Visualize cells and feature expression in a virtual cross
+section according to spatial coordinates
 }
 \details{
 Description of parameters.
diff --git a/man/crossSectionPlot.Rd b/man/crossSectionPlot.Rd
index ac4062e83..f7059e680 100644
--- a/man/crossSectionPlot.Rd
+++ b/man/crossSectionPlot.Rd
@@ -6,8 +6,9 @@
 \usage{
 crossSectionPlot(
   gobject,
-  spat_loc_name = "raw",
+  spat_unit = NULL,
   feat_type = NULL,
+  spat_loc_name = "raw",
   crossSection_obj = NULL,
   name = NULL,
   spatial_network_name = "Delaunay_network",
@@ -18,18 +19,20 @@ crossSectionPlot(
 \arguments{
 \item{gobject}{giotto object}
 
-\item{spat_loc_name}{name of spatial locations}
+\item{spat_unit}{spatial unit}
 
 \item{feat_type}{feature type}
 
-\item{crossSection_obj}{cross section object as alternative input. 
+\item{spat_loc_name}{name of spatial locations}
+
+\item{crossSection_obj}{cross section object as alternative input.
 default = NULL.}
 
 \item{name}{name of virtual cross section to use}
 
 \item{spatial_network_name}{name of spatial network to use}
 
-\item{default_save_name}{default save name for saving, don't change, 
+\item{default_save_name}{default save name for saving, don't change,
 change save_name in save_param}
 
 \item{...}{parameters for spatPlot2D}
@@ -38,7 +41,7 @@ change save_name in save_param}
 ggplot
 }
 \description{
-Visualize cells in a virtual cross section according to 
+Visualize cells in a virtual cross section according to
 spatial coordinates
 }
 \details{
diff --git a/man/crossSectionPlot3D.Rd b/man/crossSectionPlot3D.Rd
index efb03fad5..04be6daf5 100644
--- a/man/crossSectionPlot3D.Rd
+++ b/man/crossSectionPlot3D.Rd
@@ -6,6 +6,8 @@
 \usage{
 crossSectionPlot3D(
   gobject,
+  spat_unit = NULL,
+  feat_type = NULL,
   crossSection_obj = NULL,
   name = NULL,
   spatial_network_name = "Delaunay_network",
@@ -18,7 +20,11 @@ crossSectionPlot3D(
 \arguments{
 \item{gobject}{giotto object}
 
-\item{crossSection_obj}{cross section object as alternative input. 
+\item{spat_unit}{spatial unit}
+
+\item{feat_type}{feature type}
+
+\item{crossSection_obj}{cross section object as alternative input.
 default = NULL.}
 
 \item{name}{name of virtual cross section to use}
@@ -27,10 +33,10 @@ default = NULL.}
 
 \item{show_other_cells}{display not selected cells}
 
-\item{other_cell_color}{color of cells outside the cross section. 
+\item{other_cell_color}{color of cells outside the cross section.
 default = transparent.}
 
-\item{default_save_name}{default save name for saving, don't change, 
+\item{default_save_name}{default save name for saving, don't change,
 change save_name in save_param}
 
 \item{...}{parameters for spatPlot3D}
@@ -39,7 +45,7 @@ change save_name in save_param}
 ggplot
 }
 \description{
-Visualize cells in a virtual cross section according to spatial 
+Visualize cells in a virtual cross section according to spatial
 coordinates
 }
 \details{
diff --git a/man/detectSpatialCorFeats.Rd b/man/detectSpatialCorFeats.Rd
index 4cba6edaf..42b69eb39 100644
--- a/man/detectSpatialCorFeats.Rd
+++ b/man/detectSpatialCorFeats.Rd
@@ -2,7 +2,8 @@
 % Please edit documentation in R/spatial_genes.R
 \name{detectSpatialCorFeats}
 \alias{detectSpatialCorFeats}
-\title{detectSpatialCorFeats}
+\alias{detectSpatialCorFeatsMatrix}
+\title{Detect spatially correlated features}
 \usage{
 detectSpatialCorFeats(
   gobject,
@@ -18,6 +19,18 @@ detectSpatialCorFeats(
   min_cells_per_grid = 4,
   cor_method = c("pearson", "kendall", "spearman")
 )
+
+detectSpatialCorFeatsMatrix(
+  expression_matrix,
+  method = c("grid", "network"),
+  spatial_network,
+  spatial_grid,
+  spatial_locs,
+  subset_feats = NULL,
+  network_smoothing = NULL,
+  min_cells_per_grid = 4,
+  cor_method = c("pearson", "kendall", "spearman")
+)
 }
 \arguments{
 \item{gobject}{giotto object}
@@ -32,44 +45,70 @@ detectSpatialCorFeats(
 
 \item{expression_values}{gene expression values to use}
 
-\item{subset_feats}{subset of feats to use}
+\item{subset_feats}{subset of features to use}
 
 \item{spatial_network_name}{name of spatial network to use}
 
-\item{network_smoothing}{smoothing factor beteen 0 and 1
-(default: automatic)}
+\item{network_smoothing}{smoothing factor between 0 and 1
+(has automatic default, see details)}
 
 \item{spatial_grid_name}{name of spatial grid to use}
 
 \item{min_cells_per_grid}{minimum number of cells to consider a grid}
 
 \item{cor_method}{correlation method}
+
+\item{expression_matrix}{provided expression matrix}
+
+\item{spatial_network}{provided spatial network}
+
+\item{spatial_grid}{provided spatial grid}
+
+\item{spatial_locs}{provided spatial locations}
 }
 \value{
-returns a spatial correlation object: "spatCorObject"
+returns a spatial correlation object: \code{spatCorObject}
 }
 \description{
-Detect features that are spatially correlated
+Detect features that are spatially correlated. Functions for
+starting from either a gobject (\code{detectSpatialCorFeats()}) or individual
+pieces of data (\code{detectSpatialCorFeatsMatrix()}) are provided.
 }
 \details{
-For method = network, it expects a fully connected spatial network. You
-can make sure to create a
+For \code{method = network}, it expects a fully connected spatial network.
+You can make sure to create a
 fully connected network by setting minimal_k > 0 in the
- \code{\link{createSpatialNetwork}} function.
-\itemize{
- \item{1. grid-averaging: }{average gene expression values within a predefined spatial grid}
- \item{2. network-averaging: }{smoothens the gene expression matrix by averaging the expression within one cell
- by using the neighbours within the predefined spatial network. b is a smoothening factor
- that defaults to 1 - 1/k, where k is the median number of  k-neighbors in the
- selected spatial network. Setting b = 0 means no smoothing and b = 1 means no contribution
- from its own expression.}
+\code{\link{createSpatialNetwork}} function.
+\enumerate{
+\item \strong{grid-averaging:} average gene expression values within a predefined
+spatial grid
+\item \strong{network-averaging:} smoothens the gene expression matrix by
+averaging the expression within one cell by using the neighbours within
+the predefined spatial network. \eqn{b} is a smoothening factor passed by
+\code{network_smoothing} param that defaults to \eqn{1 - 1/k}, where \eqn{k}
+is the median number of k-neighbors in the selected spatial network.
+Setting \eqn{b = 0} means no smoothing and \eqn{b = 1} means no
+contribution from its own expression.
 }
-The spatCorObject can be further explored with showSpatialCorFeats()
+
+The \code{spatCorObject} can be further explored with \code{showSpatialCorFeats()}
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
+# Perform with data in a gobject
 detectSpatialCorFeats(g, method = "network")
+
+# This analysis can also be performed with data outside of the gobject
+detectSpatialCorFeatsMatrix(
+    expression_matrix = getExpression(
+        g,
+        output = "matrix"
+    ),
+    method = "network",
+    spatial_network = getSpatialNetwork(g, output = "networkDT")
+)
+
 }
 \seealso{
 \code{\link{showSpatialCorFeats}}
diff --git a/man/detectSpatialCorFeatsMatrix.Rd b/man/detectSpatialCorFeatsMatrix.Rd
deleted file mode 100644
index 515557f80..000000000
--- a/man/detectSpatialCorFeatsMatrix.Rd
+++ /dev/null
@@ -1,73 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/spatial_genes.R
-\name{detectSpatialCorFeatsMatrix}
-\alias{detectSpatialCorFeatsMatrix}
-\title{detectSpatialCorFeatsMatrix}
-\usage{
-detectSpatialCorFeatsMatrix(
-  expression_matrix,
-  method = c("grid", "network"),
-  spatial_network,
-  spatial_grid,
-  spatial_locs,
-  subset_feats = NULL,
-  network_smoothing = NULL,
-  min_cells_per_grid = 4,
-  cor_method = c("pearson", "kendall", "spearman")
-)
-}
-\arguments{
-\item{expression_matrix}{provided expression matrix}
-
-\item{method}{method to use for spatial averaging}
-
-\item{spatial_network}{provided spatial network}
-
-\item{spatial_grid}{provided spatial grid}
-
-\item{spatial_locs}{provided spatial locations}
-
-\item{subset_feats}{subset of features to use}
-
-\item{network_smoothing}{smoothing factor beteen 0 and 1
-(has automatic default, see details)}
-
-\item{min_cells_per_grid}{minimum number of cells to consider a grid}
-
-\item{cor_method}{correlation method}
-}
-\value{
-returns a spatial correlation object: \code{spatCorObject}
-}
-\description{
-Detect genes that are spatially correlated
-}
-\details{
-For \code{method = network}, it expects a fully connected spatial network.
-You can make sure to create a
-fully connected network by setting minimal_k > 0 in the
-\code{\link{createSpatialNetwork}} function.
-\enumerate{
-\item \strong{grid-averaging:} average gene expression values within a predefined
-spatial grid
-\item \strong{network-averaging:} smoothens the gene expression matrix by
-averaging the expression within one cell by using the neighbours within
-the predefined spatial network. \eqn{b} is a smoothening factor passed by
-\code{network_smoothing} param that defaults to \eqn{1 - 1/k}, where \eqn{k}
-is the median number of k-neighbors in the selected spatial network.
-Setting \eqn{b = 0} means no smoothing and \eqn{b = 1} means no
-contribution from its own expression.
-}
-
-The \code{spatCorObject} can be further explored with \code{showSpatialCorGenes()}
-}
-\examples{
-g <- GiottoData::loadGiottoMini("visium")
-
-detectSpatialCorFeatsMatrix(expression_matrix = getExpression(
-g, output = "matrix"), method = "network",
-spatial_network = getSpatialNetwork(g, output = "networkDT"))
-}
-\seealso{
-\code{\link{showSpatialCorFeats}}
-}
diff --git a/man/detectSpatialCorGenes.Rd b/man/detectSpatialCorGenes.Rd
deleted file mode 100644
index 6c5a2ce6b..000000000
--- a/man/detectSpatialCorGenes.Rd
+++ /dev/null
@@ -1,74 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/spatial_genes.R
-\name{detectSpatialCorGenes}
-\alias{detectSpatialCorGenes}
-\title{detectSpatialCorGenes}
-\usage{
-detectSpatialCorGenes(
-  gobject,
-  feat_type = NULL,
-  spat_unit = NULL,
-  method = c("grid", "network"),
-  expression_values = c("normalized", "scaled", "custom"),
-  subset_feats = NULL,
-  subset_genes = NULL,
-  spatial_network_name = "Delaunay_network",
-  network_smoothing = NULL,
-  spatial_grid_name = "spatial_grid",
-  min_cells_per_grid = 4,
-  cor_method = c("pearson", "kendall", "spearman")
-)
-}
-\arguments{
-\item{gobject}{giotto object}
-
-\item{feat_type}{feature type}
-
-\item{spat_unit}{spatial unit}
-
-\item{method}{method to use for spatial averaging}
-
-\item{expression_values}{gene expression values to use}
-
-\item{subset_feats}{subset of feats to use}
-
-\item{subset_genes}{deprecated, use \code{subset_feats}}
-
-\item{spatial_network_name}{name of spatial network to use}
-
-\item{network_smoothing}{smoothing factor beteen 0 and 1
-(default: automatic)}
-
-\item{spatial_grid_name}{name of spatial grid to use}
-
-\item{min_cells_per_grid}{minimum number of cells to consider a grid}
-
-\item{cor_method}{correlation method}
-}
-\value{
-returns a spatial correlation object: "spatCorObject"
-}
-\description{
-Detect genes that are spatially correlated
-}
-\details{
-For method = network, it expects a fully connected spatial network. You
-can make sure to create a
-fully connected network by setting minimal_k > 0 in the
-\code{\link{createSpatialNetwork}} function.
-\itemize{
- \item{1. grid-averaging: }{average gene expression values within a
- predefined spatial grid}
- \item{2. network-averaging: }{smoothens the gene expression matrix by
- averaging the expression within one cell
- by using the neighbours within the predefined spatial network. b is a
- smoothening factor that defaults to 1 - 1/k, where k is the median
- number of  k-neighbors in the selected spatial network. Setting b = 0
- means no smoothing and b = 1 means no contribution
- from its own expression.}
-}
-The spatCorObject can be further explored with showSpatialCorGenes()
-}
-\seealso{
-\code{\link{showSpatialCorGenes}}
-}
diff --git a/man/detectSpatialPatterns.Rd b/man/detectSpatialPatterns.Rd
index f45d0592c..0547b4b78 100644
--- a/man/detectSpatialPatterns.Rd
+++ b/man/detectSpatialPatterns.Rd
@@ -43,8 +43,9 @@ in a spatial grid.
 \details{
 Steps to identify spatial patterns:
 \itemize{
-  \item{1. average gene expression for cells within a grid, see createSpatialGrid}
-  \item{2. perform PCA on the average grid expression profiles}
-  \item{3. convert variance of principlal components (PCs) to z-scores and select PCs based on a z-score threshold}
+  * 1. average gene expression for cells within a grid, see createSpatialGrid
+  * 2. perform PCA on the average grid expression profiles
+  * 3. convert variance of principal components (PCs) to z-scores and
+  select PCs based on a z-score threshold
 }
 }
diff --git a/man/doClusterProjection.Rd b/man/doClusterProjection.Rd
index 6ccea3858..7bc3dd27b 100644
--- a/man/doClusterProjection.Rd
+++ b/man/doClusterProjection.Rd
@@ -78,7 +78,9 @@ Giotto object.
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 x <- pDataDT(g)
-g_small <- subsetGiotto(g, cell_ids = sample(x$cell_ID,300))
-doClusterProjection(target_gobject = g, source_gobject = g_small,
-source_cluster_labels = "leiden_clus")
+g_small <- subsetGiotto(g, cell_ids = sample(x$cell_ID, 300))
+doClusterProjection(
+    target_gobject = g, source_gobject = g_small,
+    source_cluster_labels = "leiden_clus"
+)
 }
diff --git a/man/doFeatureSetEnrichment.Rd b/man/doFeatureSetEnrichment.Rd
index 28387127f..359b7d3e5 100644
--- a/man/doFeatureSetEnrichment.Rd
+++ b/man/doFeatureSetEnrichment.Rd
@@ -28,39 +28,39 @@ doFeatureSetEnrichment(
 \item{path_to_GSEA}{path to GSEA command line executable, e.g. gsea-XXX.jar.
 See details (1.) for more information.}
 
-\item{GSEA_dataset}{path to a Human/Mouse collection from GSEA, e.g. 
+\item{GSEA_dataset}{path to a Human/Mouse collection from GSEA, e.g.
 Hallmarks C1. See details (2.) for more information.}
 
-\item{GSEA_ranked_file}{path to .rnk file for GSEA. See details (3.) for 
+\item{GSEA_ranked_file}{path to .rnk file for GSEA. See details (3.) for
 more information}
 
-\item{output_folder}{path to which the GSEA results will be saved. Default 
+\item{output_folder}{path to which the GSEA results will be saved. Default
 is current working directory.}
 
-\item{name_analysis_folder}{default output subdirectory prefix to which 
+\item{name_analysis_folder}{default output subdirectory prefix to which
 results are saved.
-                            Will live within output_folder; equivalent of 
+                            Will live within output_folder; equivalent of
                             "Analysis Name" in GSEA Application.}
 
-\item{collapse}{only 'false' is supported. This will use your dataset as-is, 
+\item{collapse}{only 'false' is supported. This will use your dataset as-is,
 in the original format.}
 
-\item{mode}{option selected in Advanced Field "Collapsing Mode for 
+\item{mode}{option selected in Advanced Field "Collapsing Mode for
 Probe Sets => 1 gene"}
 
 \item{norm}{normalization mode; only meandiv is supported.}
 
 \item{nperm}{number of permutations, default 1000}
 
-\item{scoring_scheme}{Default "weighted", equivalent of 
+\item{scoring_scheme}{Default "weighted", equivalent of
 "enrichment statistic" in GSEA Application}
 
 \item{plot_top_x}{Default 20, number of enrichment plots to produce.}
 
-\item{set_max}{default 500, equivalent to "max size; exclude larger sets" 
+\item{set_max}{default 500, equivalent to "max size; exclude larger sets"
 in Basic Fields in GSEA Application}
 
-\item{set_min}{default 15, equivalent to "min size; exclude smaller sets" 
+\item{set_min}{default 15, equivalent to "min size; exclude smaller sets"
 in Basic Fields in GSEA Application}
 }
 \value{
@@ -74,11 +74,11 @@ NECESSARY PREREQUISITES
 1. download and install the COMMAND line (all platforms) gsea-XXX.jar
 https://www.gsea-msigdb.org/gsea/downloads.jsp
 1.1. download zip file
-1.2. unzip and move to known location 
+1.2. unzip and move to known location
 (e.g. in path/to/your/applications/gsea/GSEA_4.3.2)
 
 2. download the Human and Mouse collections
-https://www.gsea-msigdb.org/gsea/msigdb/index.jsp or zipped folder 
+https://www.gsea-msigdb.org/gsea/msigdb/index.jsp or zipped folder
 https://www.gsea-msigdb.org/gsea/downloads.jsp (all downloaded)
 
 3. create ranked gene lists
diff --git a/man/doGiottoClustree.Rd b/man/doGiottoClustree.Rd
index cc605fabf..ff99d4c6a 100644
--- a/man/doGiottoClustree.Rd
+++ b/man/doGiottoClustree.Rd
@@ -68,8 +68,10 @@ will be returned.
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-doGiottoClustree(gobject = g, res_vector = c(0.5, 0.8), return_plot = FALSE,
-show_plot = FALSE, save_plot = FALSE)
+doGiottoClustree(
+    gobject = g, res_vector = c(0.5, 0.8), return_plot = FALSE,
+    show_plot = FALSE, save_plot = FALSE
+)
 }
 \seealso{
 \code{\link{doLeidenCluster}}
diff --git a/man/doHMRF.Rd b/man/doHMRF.Rd
index 6021d2a19..0e2975699 100644
--- a/man/doHMRF.Rd
+++ b/man/doHMRF.Rd
@@ -43,7 +43,7 @@ doHMRF(
 
 \item{spatial_genes}{spatial genes to use for HMRF}
 
-\item{spatial_dimensions}{select spatial dimensions to use, default is all 
+\item{spatial_dimensions}{select spatial dimensions to use, default is all
 possible dimensions}
 
 \item{dim_reduction_to_use}{use another dimension reduction set as input}
@@ -52,14 +52,14 @@ possible dimensions}
 
 \item{dimensions_to_use}{number of dimensions to use as input}
 
-\item{seed}{seed to fix random number generator 
+\item{seed}{seed to fix random number generator
 (for creating initialization of HMRF) (-1 if no fixing)}
 
 \item{name}{name of HMRF run}
 
 \item{k}{number of HMRF domains}
 
-\item{betas}{betas to test for. three numbers: start_beta, beta_increment, 
+\item{betas}{betas to test for. three numbers: start_beta, beta_increment,
 num_betas e.g. c(0, 2.0, 50)}
 
 \item{tolerance}{tolerance}
@@ -75,7 +75,7 @@ num_betas e.g. c(0, 2.0, 50)}
 \item{overwrite_output}{overwrite output folder}
 }
 \value{
-Creates a directory with results that can be viewed with 
+Creates a directory with results that can be viewed with
 viewHMRFresults
 }
 \description{
@@ -88,6 +88,8 @@ Description of HMRF parameters ...
 g <- GiottoData::loadGiottoMini("visium")
 spat_genes <- binSpect(g)
 
-doHMRF(g, spatial_genes = spat_genes[seq_len(10)]$feats, 
-output_folder = tempdir())
+doHMRF(g,
+    spatial_genes = spat_genes[seq_len(10)]$feats,
+    output_folder = tempdir()
+)
 }
diff --git a/man/doHMRF_V2.Rd b/man/doHMRF_V2.Rd
index c147e6957..d8560a60a 100644
--- a/man/doHMRF_V2.Rd
+++ b/man/doHMRF_V2.Rd
@@ -7,12 +7,12 @@
 doHMRF_V2(HMRF_init_obj, betas = NULL)
 }
 \arguments{
-\item{HMRF_init_obj}{initialization object list returned from initHMRF() 
+\item{HMRF_init_obj}{initialization object list returned from initHMRF()
 function}
 
-\item{betas}{beta value of the HMRF model, controlling the smoothness of 
-clustering. NULL value of beta will provide default values based on feature 
-numbers, otherwise, a vector of three values: initial beta, beta increment, 
+\item{betas}{beta value of the HMRF model, controlling the smoothness of
+clustering. NULL value of beta will provide default values based on feature
+numbers, otherwise, a vector of three values: initial beta, beta increment,
 and number of betas}
 }
 \value{
@@ -22,20 +22,27 @@ HMRF model
 function to run HMRF model
 }
 \details{
-This function will run a HMRF model after initialization of HMRF. Of note 
+This function will run a HMRF model after initialization of HMRF. Of note
 is the beta parameter, the smoothing parameter.
-If the users are interested in selecting results from different smoothness, 
+If the users are interested in selecting results from different smoothness,
 we recommend running a range of betas,
-hence betas specify what this range is. For example, betas=c(0,10,5) will 
+hence betas specify what this range is. For example, betas=c(0,10,5) will
 run for the following betas: 0, 10, 20, 30, 40.
-betas=c(0,5,2) will run for betas: 0, 5, 10. Setting the beta can use the 
+betas=c(0,5,2) will run for betas: 0, 5, 10. Setting the beta can use the
 following guideline:
 If number of features N is 10<N<50, set betas=c(0, 1, 20)
 For 50<N<100, set betas=c(0, 2, 25)
 For 100<N<500, set betas=c(0, 5, 20)
-If user didn't specify the number or sequence of beta, the function will 
+If user didn't specify the number or sequence of beta, the function will
 provide a default value based on the number of features.
-Returns a list of results for betas, spat_unit and feat_type. Result for 
-each beta is a list with probability(normalized or non-normalized), class, 
+Returns a list of results for betas, spat_unit and feat_type. Result for
+each beta is a list with probability(normalized or non-normalized), class,
 and model log-likelihood value.
 }
+\examples{
+g <- GiottoData::loadGiottoMini("visium")
+g <- binSpect(g, return_gobject = TRUE)
+HMRF_init_obj <- initHMRF_V2(gobject = g, cl.method = "km")
+
+doHMRF_V2(HMRF_init_obj = HMRF_init_obj, betas = c(0, 5, 2))
+}
diff --git a/man/doLeidenSubCluster.Rd b/man/doLeidenSubCluster.Rd
deleted file mode 100644
index 51fcb0dd1..000000000
--- a/man/doLeidenSubCluster.Rd
+++ /dev/null
@@ -1,116 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/clustering.R
-\name{doLeidenSubCluster}
-\alias{doLeidenSubCluster}
-\title{doLeidenSubCluster}
-\usage{
-doLeidenSubCluster(
-  gobject,
-  feat_type = NULL,
-  name = "sub_pleiden_clus",
-  cluster_column = NULL,
-  selected_clusters = NULL,
-  hvf_param = list(reverse_log_scale = TRUE, difference_in_cov = 1, expression_values =
-    "normalized"),
-  hvg_param = NULL,
-  hvf_min_perc_cells = 5,
-  hvg_min_perc_cells = NULL,
-  hvf_mean_expr_det = 1,
-  hvg_mean_expr_det = NULL,
-  use_all_feats_as_hvf = FALSE,
-  use_all_genes_as_hvg = NULL,
-  min_nr_of_hvf = 5,
-  min_nr_of_hvg = NULL,
-  pca_param = list(expression_values = "normalized", scale_unit = TRUE),
-  nn_param = list(dimensions_to_use = 1:20),
-  k_neighbors = 10,
-  resolution = 0.5,
-  n_iterations = 500,
-  python_path = NULL,
-  nn_network_to_use = "sNN",
-  network_name = "sNN.pca",
-  return_gobject = TRUE,
-  verbose = TRUE
-)
-}
-\arguments{
-\item{gobject}{giotto object}
-
-\item{feat_type}{feature type}
-
-\item{name}{name for new clustering result}
-
-\item{cluster_column}{cluster column to subcluster}
-
-\item{selected_clusters}{only do subclustering on these clusters}
-
-\item{hvf_param}{parameters for calculateHVf}
-
-\item{hvg_param}{deprecatd, use hvf_param}
-
-\item{hvf_min_perc_cells}{threshold for detection in min percentage of cells}
-
-\item{hvg_min_perc_cells}{deprecated, use hvf_min_perc_cells}
-
-\item{hvf_mean_expr_det}{threshold for mean expression level in cells with
-detection}
-
-\item{hvg_mean_expr_det}{deprecated, use hvf_mean_expr_det}
-
-\item{use_all_feats_as_hvf}{forces all features to be HVF and to be used as
-input for PCA}
-
-\item{use_all_genes_as_hvg}{deprecated, use use_all_feats_as_hvf}
-
-\item{min_nr_of_hvf}{minimum number of HVF, or all features will be used as
-input for PCA}
-
-\item{min_nr_of_hvg}{deprecated, use min_nr_of_hvf}
-
-\item{pca_param}{parameters for runPCA}
-
-\item{nn_param}{parameters for parameters for createNearestNetwork}
-
-\item{k_neighbors}{number of k for createNearestNetwork}
-
-\item{resolution}{resolution of Leiden clustering}
-
-\item{n_iterations}{number of interations to run the Leiden algorithm.}
-
-\item{python_path}{specify specific path to python if required}
-
-\item{nn_network_to_use}{type of NN network to use (kNN vs sNN)}
-
-\item{network_name}{name of NN network to use}
-
-\item{return_gobject}{boolean: return giotto object (default = TRUE)}
-
-\item{verbose}{verbose}
-}
-\value{
-giotto object with new subclusters appended to cell metadata
-}
-\description{
-Further subcluster cells using a NN-network and the Leiden
-algorithm
-}
-\details{
-This function performs subclustering using the Leiden algorithm on
-selected clusters.
-The systematic steps are:
-\itemize{
-  \item{1. subset Giotto object}
-  \item{2. identify highly variable fetures}
-  \item{3. run PCA}
-  \item{4. create nearest neighbouring network}
-  \item{5. do Leiden clustering}
-}
-}
-\examples{
-g <- GiottoData::loadGiottoMini("visium")
-
-doLeidenSubCluster(g, cluster_column = "leiden_clus")
-}
-\seealso{
-\code{\link{doLeidenCluster}}
-}
diff --git a/man/doLouvainSubCluster.Rd b/man/doLouvainSubCluster.Rd
deleted file mode 100644
index 0b2c5daf5..000000000
--- a/man/doLouvainSubCluster.Rd
+++ /dev/null
@@ -1,104 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/clustering.R
-\name{doLouvainSubCluster}
-\alias{doLouvainSubCluster}
-\title{doLouvainSubCluster}
-\usage{
-doLouvainSubCluster(
-  gobject,
-  name = "sub_louvain_clus",
-  version = c("community", "multinet"),
-  cluster_column = NULL,
-  selected_clusters = NULL,
-  hvg_param = list(reverse_log_scale = TRUE, difference_in_cov = 1, expression_values =
-    "normalized"),
-  hvg_min_perc_cells = 5,
-  hvg_mean_expr_det = 1,
-  use_all_genes_as_hvg = FALSE,
-  min_nr_of_hvg = 5,
-  pca_param = list(expression_values = "normalized", scale_unit = TRUE),
-  nn_param = list(dimensions_to_use = 1:20),
-  k_neighbors = 10,
-  resolution = 0.5,
-  gamma = 1,
-  omega = 1,
-  python_path = NULL,
-  nn_network_to_use = "sNN",
-  network_name = "sNN.pca",
-  return_gobject = TRUE,
-  verbose = TRUE
-)
-}
-\arguments{
-\item{gobject}{giotto object}
-
-\item{name}{name for new clustering result}
-
-\item{version}{version of Louvain algorithm to use}
-
-\item{cluster_column}{cluster column to subcluster}
-
-\item{selected_clusters}{only do subclustering on these clusters}
-
-\item{hvg_param}{parameters for calculateHVG}
-
-\item{hvg_min_perc_cells}{threshold for detection in min percentage of cells}
-
-\item{hvg_mean_expr_det}{threshold for mean expression level in cells with
-detection}
-
-\item{use_all_genes_as_hvg}{forces all genes to be HVG and to be used as
-input for PCA}
-
-\item{min_nr_of_hvg}{minimum number of HVG, or all genes will be used as
-input for PCA}
-
-\item{pca_param}{parameters for runPCA}
-
-\item{nn_param}{parameters for parameters for createNearestNetwork}
-
-\item{k_neighbors}{number of k for createNearestNetwork}
-
-\item{resolution}{resolution for community algorithm}
-
-\item{gamma}{gamma}
-
-\item{omega}{omega}
-
-\item{python_path}{specify specific path to python if required}
-
-\item{nn_network_to_use}{type of NN network to use (kNN vs sNN)}
-
-\item{network_name}{name of NN network to use}
-
-\item{return_gobject}{boolean: return giotto object (default = TRUE)}
-
-\item{verbose}{verbose}
-}
-\value{
-giotto object with new subclusters appended to cell metadata
-}
-\description{
-subcluster cells using a NN-network and the Louvain algorithm
-}
-\details{
-This function performs subclustering using the Louvain algorithm on
-selected clusters.
-The systematic steps are:
-\itemize{
-  \item{1. subset Giotto object}
-  \item{2. identify highly variable genes}
-  \item{3. run PCA}
-  \item{4. create nearest neighbouring network}
-  \item{5. do Louvain clustering}
-}
-}
-\examples{
-g <- GiottoData::loadGiottoMini("visium")
-
-doLouvainSubCluster(g, cluster_column = "leiden_clus")
-}
-\seealso{
-\code{\link{.doLouvainCluster_multinet}} and
-\code{\link{.doLouvainCluster_community}}
-}
diff --git a/man/doRandomWalkCluster.Rd b/man/doRandomWalkCluster.Rd
index bf0d36cbb..626be7fd4 100644
--- a/man/doRandomWalkCluster.Rd
+++ b/man/doRandomWalkCluster.Rd
@@ -52,5 +52,6 @@ package in R for more information.
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-doRandomWalkCluster(g)
+g <- doRandomWalkCluster(g)
+pDataDT(g)
 }
diff --git a/man/doScrubletDetect.Rd b/man/doScrubletDetect.Rd
index b6937c579..fb68af241 100644
--- a/man/doScrubletDetect.Rd
+++ b/man/doScrubletDetect.Rd
@@ -54,12 +54,18 @@ into the input \code{giotto} object's metadata and the \code{giotto} object is
 returned.
 }
 \description{
-run \emph{scrublet} doublet detection for raw expression.
+Run \emph{scrublet} doublet detection for raw expression. Intended
+for single cell data
 }
 \examples{
+# Should only be done with single cell data, but this is just a
+# convenient example.
 g <- GiottoData::loadGiottoMini("visium")
 
-doScrubletDetect(g)
+g <- doScrubletDetect(g)
+
+pDataDT(g) # doublet_scores and doublet cols are added
+dimPlot2D(g, cell_color = "doublet_scores", color_as_factor = FALSE)
 }
 \seealso{
 This function wraps the python package scrublet
diff --git a/man/do_permuttest_spot.Rd b/man/do_permuttest_spot.Rd
index 7a70f2866..90ecd1922 100644
--- a/man/do_permuttest_spot.Rd
+++ b/man/do_permuttest_spot.Rd
@@ -71,7 +71,7 @@ Test spot interactions using permutations
 
 \item \code{.do_multi_permuttest_random_spot()}: Calculate multiple random values for spots
 
-\item \code{.do_permuttest_spot()}: Performs permutation test on subsets of a 
+\item \code{.do_permuttest_spot()}: Performs permutation test on subsets of a
 matrix for spots
 
 }}
diff --git a/man/do_spatial_knn_smoothing.Rd b/man/do_spatial_knn_smoothing.Rd
index abeac38db..5e36e7d3f 100644
--- a/man/do_spatial_knn_smoothing.Rd
+++ b/man/do_spatial_knn_smoothing.Rd
@@ -12,15 +12,13 @@ do_spatial_knn_smoothing(
 )
 }
 \arguments{
-\item{subset_feats}{subset of features to use}
-
-\item{b}{smoothing factor beteen 0 and 1 (default: automatic)}
+\item{expression_matrix}{gene expression values to use}
 
-\item{gobject}{giotto object}
+\item{spatial_network}{spatial network to use}
 
-\item{expression_values}{gene expression values to use}
+\item{subset_feats}{subset of features to use}
 
-\item{spatial_network_name}{name of spatial network to use}
+\item{b}{smoothing factor beteen 0 and 1 (default: automatic)}
 }
 \value{
 matrix with smoothened gene expression values based on kNN
diff --git a/man/average_feat_feat_expression_in_groups.Rd b/man/dot-average_feat_feat_expression_in_groups.Rd
similarity index 84%
rename from man/average_feat_feat_expression_in_groups.Rd
rename to man/dot-average_feat_feat_expression_in_groups.Rd
index 191aed4dd..f1590ec8d 100644
--- a/man/average_feat_feat_expression_in_groups.Rd
+++ b/man/dot-average_feat_feat_expression_in_groups.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/spatial_interaction.R
-\name{average_feat_feat_expression_in_groups}
-\alias{average_feat_feat_expression_in_groups}
+\name{.average_feat_feat_expression_in_groups}
+\alias{.average_feat_feat_expression_in_groups}
 \title{average_feat_feat_expression_in_groups}
 \usage{
-average_feat_feat_expression_in_groups(
+.average_feat_feat_expression_in_groups(
   gobject,
   spat_unit = NULL,
   feat_type = NULL,
diff --git a/man/dot-cal_expr_residual.Rd b/man/dot-cal_expr_residual.Rd
index 8003a0d65..82d589803 100644
--- a/man/dot-cal_expr_residual.Rd
+++ b/man/dot-cal_expr_residual.Rd
@@ -19,7 +19,7 @@
 
 \item{feat_type}{feature type (e.g. 'rna')}
 
-\item{expression_values}{expression values to use 
+\item{expression_values}{expression values to use
 (e.g. 'normalized', 'scaled', 'custom')}
 
 \item{ave_celltype_exp}{average expression matrix in cell types}
@@ -28,7 +28,7 @@
 matrix
 }
 \description{
-Calculate feature expression residual 
+Calculate feature expression residual
 (observed_exp - DWLS_predicted)
 }
 \keyword{internal}
diff --git a/man/dot-createGiottoCosMxObject_all.Rd b/man/dot-createGiottoCosMxObject_all.Rd
index 44e70f5d7..40c3a1ed6 100644
--- a/man/dot-createGiottoCosMxObject_all.Rd
+++ b/man/dot-createGiottoCosMxObject_all.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/convenience.R
 \name{.createGiottoCosMxObject_all}
 \alias{.createGiottoCosMxObject_all}
-\title{Load and create a CosMx Giotto object from subcellular and aggregate 
+\title{Load and create a CosMx Giotto object from subcellular and aggregate
 info}
 \usage{
 .createGiottoCosMxObject_all(
@@ -22,7 +22,7 @@ info}
 
 \item{FOVs}{field of views to load (only affects subcellular data and images)}
 
-\item{remove_background_polygon}{try to remove background polygon 
+\item{remove_background_polygon}{try to remove background polygon
 (default: FALSE)}
 
 \item{background_algo}{algorithm to remove background polygon}
@@ -41,13 +41,13 @@ from \code{\link[GiottoClass]{createGiottoInstructions}}}
 giotto object
 }
 \description{
-Load and create a CosMx Giotto object from subcellular and aggregate 
+Load and create a CosMx Giotto object from subcellular and aggregate
 info
 }
 \details{
-Both \emph{subcellular} 
+Both \emph{subcellular}
 (subellular transcript detection information) and
-\emph{aggregate} (aggregated detection count matrices by cell polygon from 
+\emph{aggregate} (aggregated detection count matrices by cell polygon from
 NanoString)
 data will be loaded in. The two will be separated into 'cell' and 'cell_agg'
 spatial units in order to denote the difference in origin of the two.
diff --git a/man/dot-createGiottoCosMxObject_subcellular.Rd b/man/dot-createGiottoCosMxObject_subcellular.Rd
index 17d07ada9..cc5c273b2 100644
--- a/man/dot-createGiottoCosMxObject_subcellular.Rd
+++ b/man/dot-createGiottoCosMxObject_subcellular.Rd
@@ -18,7 +18,7 @@
 \arguments{
 \item{FOVs}{field of views to load (only affects subcellular data and images)}
 
-\item{remove_background_polygon}{try to remove background polygon 
+\item{remove_background_polygon}{try to remove background polygon
 (default: FALSE)}
 
 \item{background_algo}{algorithm to remove background polygon}
diff --git a/man/dot-createGiottoXeniumObject_subcellular.Rd b/man/dot-createGiottoXeniumObject_subcellular.Rd
index 75013fe11..11f6b946b 100644
--- a/man/dot-createGiottoXeniumObject_subcellular.Rd
+++ b/man/dot-createGiottoXeniumObject_subcellular.Rd
@@ -19,7 +19,7 @@
 \item{key_list}{regex-based search keys for feature IDs to allow separation
 into separate giottoPoints objects by feat_type}
 
-\item{qv_threshold}{Minimum Phred-scaled quality score cutoff to be included 
+\item{qv_threshold}{Minimum Phred-scaled quality score cutoff to be included
 as a subcellular transcript detection (default = 20)}
 
 \item{instructions}{list of instructions or output result
diff --git a/man/dot-determine_switch_string_equal.Rd b/man/dot-determine_switch_string_equal.Rd
index 932a9c746..40ecefa0d 100644
--- a/man/dot-determine_switch_string_equal.Rd
+++ b/man/dot-determine_switch_string_equal.Rd
@@ -30,7 +30,7 @@ Where:
    y_m is a cluster number from the resized spatial unit
    n is the number of clusters
 
-Clusters are determined to be corresponding based on % overlap in cell_IDs 
+Clusters are determined to be corresponding based on % overlap in cell_IDs
 in each cluster.
 }
 \keyword{internal}
diff --git a/man/dot-determine_switch_string_unequal.Rd b/man/dot-determine_switch_string_unequal.Rd
index c32b81af6..a1b921233 100644
--- a/man/dot-determine_switch_string_unequal.Rd
+++ b/man/dot-determine_switch_string_unequal.Rd
@@ -18,7 +18,7 @@ switch_str, a vector of corresponding cluster numbers in strings
 Determine switch string unequal
 }
 \details{
-determines how to create a string in the format 
+determines how to create a string in the format
 c("x_1-y_1", "x_2-y_2"..."x_n, y_m")
 Where:
    x_n is a cluster number from the original spatial unit
diff --git a/man/dot-doLouvainSubCluster_community.Rd b/man/dot-doLouvainSubCluster_community.Rd
deleted file mode 100644
index c120d7fa0..000000000
--- a/man/dot-doLouvainSubCluster_community.Rd
+++ /dev/null
@@ -1,91 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/clustering.R
-\name{.doLouvainSubCluster_community}
-\alias{.doLouvainSubCluster_community}
-\title{doLouvainSubCluster community}
-\usage{
-.doLouvainSubCluster_community(
-  gobject,
-  name = "sub_louvain_comm_clus",
-  cluster_column = NULL,
-  selected_clusters = NULL,
-  hvg_param = list(reverse_log_scale = TRUE, difference_in_cov = 1, expression_values =
-    "normalized"),
-  hvg_min_perc_cells = 5,
-  hvg_mean_expr_det = 1,
-  use_all_genes_as_hvg = FALSE,
-  min_nr_of_hvg = 5,
-  pca_param = list(expression_values = "normalized", scale_unit = TRUE),
-  nn_param = list(dimensions_to_use = 1:20),
-  k_neighbors = 10,
-  resolution = 0.5,
-  python_path = NULL,
-  nn_network_to_use = "sNN",
-  network_name = "sNN.pca",
-  return_gobject = TRUE,
-  verbose = TRUE
-)
-}
-\arguments{
-\item{gobject}{giotto object}
-
-\item{name}{name for new clustering result}
-
-\item{cluster_column}{cluster column to subcluster}
-
-\item{selected_clusters}{only do subclustering on these clusters}
-
-\item{hvg_param}{parameters for calculateHVG}
-
-\item{hvg_min_perc_cells}{threshold for detection in min percentage of cells}
-
-\item{hvg_mean_expr_det}{threshold for mean expression level in cells with
-detection}
-
-\item{use_all_genes_as_hvg}{forces all genes to be HVG and to be used as
-input for PCA}
-
-\item{min_nr_of_hvg}{minimum number of HVG, or all genes will be used as
-input for PCA}
-
-\item{pca_param}{parameters for runPCA}
-
-\item{nn_param}{parameters for parameters for createNearestNetwork}
-
-\item{k_neighbors}{number of k for createNearestNetwork}
-
-\item{resolution}{resolution}
-
-\item{python_path}{specify specific path to python if required}
-
-\item{nn_network_to_use}{type of NN network to use (kNN vs sNN)}
-
-\item{network_name}{name of NN network to use}
-
-\item{return_gobject}{Boolean: return giotto object (default = TRUE)}
-
-\item{verbose}{verbose}
-}
-\value{
-giotto object with new subclusters appended to cell metadata
-}
-\description{
-subcluster cells using a NN-network and the Louvain community
-detection algorithm
-}
-\details{
-This function performs subclustering using the Louvain community
-algorithm on selected clusters.
-The systematic steps are:
-\itemize{
-  \item{1. subset Giotto object}
-  \item{2. identify highly variable genes}
-  \item{3. run PCA}
-  \item{4. create nearest neighbouring network}
-  \item{5. do Louvain community clustering}
-}
-}
-\seealso{
-\code{\link{.doLouvainCluster_community}}
-}
-\keyword{internal}
diff --git a/man/dot-doLouvainSubCluster_multinet.Rd b/man/dot-doLouvainSubCluster_multinet.Rd
deleted file mode 100644
index ae36d9afd..000000000
--- a/man/dot-doLouvainSubCluster_multinet.Rd
+++ /dev/null
@@ -1,91 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/clustering.R
-\name{.doLouvainSubCluster_multinet}
-\alias{.doLouvainSubCluster_multinet}
-\title{doLouvainSubCluster multinet}
-\usage{
-.doLouvainSubCluster_multinet(
-  gobject,
-  name = "sub_louvain_mult_clus",
-  cluster_column = NULL,
-  selected_clusters = NULL,
-  hvg_param = list(reverse_log_scale = TRUE, difference_in_cov = 1, expression_values =
-    "normalized"),
-  hvg_min_perc_cells = 5,
-  hvg_mean_expr_det = 1,
-  use_all_genes_as_hvg = FALSE,
-  min_nr_of_hvg = 5,
-  pca_param = list(expression_values = "normalized", scale_unit = TRUE),
-  nn_param = list(dimensions_to_use = 1:20),
-  k_neighbors = 10,
-  gamma = 1,
-  omega = 1,
-  nn_network_to_use = "sNN",
-  network_name = "sNN.pca",
-  return_gobject = TRUE,
-  verbose = TRUE
-)
-}
-\arguments{
-\item{gobject}{giotto object}
-
-\item{name}{name for new clustering result}
-
-\item{cluster_column}{cluster column to subcluster}
-
-\item{selected_clusters}{only do subclustering on these clusters}
-
-\item{hvg_param}{parameters for calculateHVG}
-
-\item{hvg_min_perc_cells}{threshold for detection in min percentage of cells}
-
-\item{hvg_mean_expr_det}{threshold for mean expression level in cells with
-detection}
-
-\item{use_all_genes_as_hvg}{forces all genes to be HVG and to be used as
-input for PCA}
-
-\item{min_nr_of_hvg}{minimum number of HVG, or all genes will be used as
-input for PCA}
-
-\item{pca_param}{parameters for runPCA}
-
-\item{nn_param}{parameters for parameters for createNearestNetwork}
-
-\item{k_neighbors}{number of k for createNearestNetwork}
-
-\item{gamma}{gamma}
-
-\item{omega}{omega}
-
-\item{nn_network_to_use}{type of NN network to use (kNN vs sNN)}
-
-\item{network_name}{name of NN network to use}
-
-\item{return_gobject}{boolean: return giotto object (default = TRUE)}
-
-\item{verbose}{verbose}
-}
-\value{
-giotto object with new subclusters appended to cell metadata
-}
-\description{
-subcluster cells using a NN-network and the Louvain multinet
-detection algorithm
-}
-\details{
-This function performs subclustering using the Louvain multinet
-algorithm on selected clusters.
-The systematic steps are:
-\itemize{
-  \item{1. subset Giotto object}
-  \item{2. identify highly variable genes}
-  \item{3. run PCA}
-  \item{4. create nearest neighbouring network}
-  \item{5. do Louvain multinet clustering}
-}
-}
-\seealso{
-\code{\link{.doLouvainCluster_multinet}}
-}
-\keyword{internal}
diff --git a/man/dot-do_cell_proximity_test_spot.Rd b/man/dot-do_cell_proximity_test_spot.Rd
index 30014e691..905ed7ead 100644
--- a/man/dot-do_cell_proximity_test_spot.Rd
+++ b/man/dot-do_cell_proximity_test_spot.Rd
@@ -23,7 +23,7 @@
 differential test on subsets of a matrix
 }
 \description{
-Performs a selected differential test on subsets of a matrix 
+Performs a selected differential test on subsets of a matrix
 for spots
 }
 \keyword{internal}
diff --git a/man/dot-findICF_per_interaction_spot.Rd b/man/dot-findICF_per_interaction_spot.Rd
index 80f05c241..bb790b054 100644
--- a/man/dot-findICF_per_interaction_spot.Rd
+++ b/man/dot-findICF_per_interaction_spot.Rd
@@ -26,7 +26,7 @@
 data.table
 }
 \description{
-Identifies features that are differentially expressed due to 
+Identifies features that are differentially expressed due to
 proximity to other cell types for spots.
 }
 \keyword{internal}
diff --git a/man/dot-get_img_corners.Rd b/man/dot-get_img_corners.Rd
index 84f5f0225..5b8100883 100644
--- a/man/dot-get_img_corners.Rd
+++ b/man/dot-get_img_corners.Rd
@@ -13,7 +13,7 @@
 data.frame
 }
 \description{
-finds four corner spatial coords of giottoImages or 
+finds four corner spatial coords of giottoImages or
 magick-images
 }
 \keyword{internal}
diff --git a/man/dot-kmeans_arma_subset_binarize.Rd b/man/dot-kmeans_arma_subset_binarize.Rd
index 1ecde0ea0..abbb010ad 100644
--- a/man/dot-kmeans_arma_subset_binarize.Rd
+++ b/man/dot-kmeans_arma_subset_binarize.Rd
@@ -16,7 +16,7 @@
 numeric
 }
 \description{
-create binarized scores from a subsetted vector using 
+create binarized scores from a subsetted vector using
 kmeans_arma
 }
 \keyword{internal}
diff --git a/man/dot-load_cosmx_folder_subcellular.Rd b/man/dot-load_cosmx_folder_subcellular.Rd
index 3f70253c6..e96bc86f8 100644
--- a/man/dot-load_cosmx_folder_subcellular.Rd
+++ b/man/dot-load_cosmx_folder_subcellular.Rd
@@ -19,7 +19,7 @@ list
 }
 \description{
 loads in the feature detections information. Note that the mask
-images are still required for a working subcellular object, and those are 
+images are still required for a working subcellular object, and those are
 loaded in \code{\link{.createGiottoCosMxObject_subcellular}}
 }
 \keyword{internal}
diff --git a/man/dot-plotRecovery_sub.Rd b/man/dot-plotRecovery_sub.Rd
index d6856cabe..ef8fa56f9 100644
--- a/man/dot-plotRecovery_sub.Rd
+++ b/man/dot-plotRecovery_sub.Rd
@@ -21,7 +21,7 @@
 ggplot
 }
 \description{
-Plots recovery plot to compare ligand-receptor rankings from 
+Plots recovery plot to compare ligand-receptor rankings from
 spatial and expression information
 }
 \keyword{internal}
diff --git a/man/dot-read_xenium_folder.Rd b/man/dot-read_xenium_folder.Rd
index 255328100..f526ef2a8 100644
--- a/man/dot-read_xenium_folder.Rd
+++ b/man/dot-read_xenium_folder.Rd
@@ -19,7 +19,7 @@
 \item{data_to_use}{which type(s) of expression data to build the gobject with
 (e.g. default: \strong{'subcellular'}, 'aggregate', or 'all')}
 
-\item{bounds_to_load}{vector of boundary information to load 
+\item{bounds_to_load}{vector of boundary information to load
 (e.g. \code{'cell'}
 or \code{'nucleus'} by themselves or \code{c('cell', 'nucleus')} to load both
 at the same time.)}
@@ -27,7 +27,7 @@ at the same time.)}
 \item{load_format}{files formats from which to load the data. Either `csv` or
 `parquet` currently supported.}
 
-\item{h5_expression}{(boolean) whether to load cell_feature_matrix from .h5 
+\item{h5_expression}{(boolean) whether to load cell_feature_matrix from .h5
 file. Default is \code{TRUE}}
 
 \item{verbose}{be verbose when building Giotto object}
diff --git a/man/dot-rigid_transform_spatial_locations.Rd b/man/dot-rigid_transform_spatial_locations.Rd
index abaf84995..187c02dbb 100644
--- a/man/dot-rigid_transform_spatial_locations.Rd
+++ b/man/dot-rigid_transform_spatial_locations.Rd
@@ -17,7 +17,7 @@
 spatlocs
 }
 \description{
-Performs appropriate transforms to align spatial locations 
+Performs appropriate transforms to align spatial locations
 with registered images.
 }
 \keyword{internal}
diff --git a/man/dot-specific_CCCScores_spots.Rd b/man/dot-specific_CCCScores_spots.Rd
index 1d6268c4d..bd7daf6df 100644
--- a/man/dot-specific_CCCScores_spots.Rd
+++ b/man/dot-specific_CCCScores_spots.Rd
@@ -49,10 +49,10 @@
 
 \item{feature_set_2}{second specific feature set from feature pairs}
 
-\item{min_observations}{minimum number of interactions needed to be 
+\item{min_observations}{minimum number of interactions needed to be
 considered}
 
-\item{detailed}{provide more detailed information 
+\item{detailed}{provide more detailed information
 (random variance and z-score)}
 
 \item{adjust_method}{which method to adjust p-values}
@@ -66,38 +66,45 @@ considered}
 \item{verbose}{verbose}
 }
 \value{
-Cell-Cell communication scores for feature pairs based on spatial 
+Cell-Cell communication scores for feature pairs based on spatial
 interaction
 }
 \description{
-Specific Cell-Cell communication scores based on spatial 
+Specific Cell-Cell communication scores based on spatial
 expression of interacting cells at spots resolution
 }
 \details{
-Statistical framework to identify if pairs of features 
-(such as ligand-receptor combinations) are expressed at higher levels than 
-expected based on a reshuffled null distribution of feature expression 
+Statistical framework to identify if pairs of features
+(such as ligand-receptor combinations) are expressed at higher levels than
+expected based on a reshuffled null distribution of feature expression
 values in cells that are spatially in proximity to each other.
 \itemize{
- \item{LR_comb:}{Pair of ligand and receptor}
- \item{lig_cell_type:}{ cell type to assess expression level of ligand }
- \item{lig_expr:}{ average expressionresidual(observed - DWLS_predicted) of ligand in lig_cell_type }
- \item{ligand:}{ ligand name }
- \item{rec_cell_type:}{ cell type to assess expression level of receptor }
- \item{rec_expr:}{ average expression residual(observed - DWLS_predicted) of receptor in rec_cell_type}
- \item{receptor:}{ receptor name }
- \item{LR_expr:}{ combined average ligand and receptor expression }
- \item{lig_nr:}{ total number of cells from lig_cell_type that spatially interact with cells from rec_cell_type }
- \item{rec_nr:}{ total number of cells from rec_cell_type that spatially interact with cells from lig_cell_type }
- \item{rand_expr:}{ average combined ligand and receptor expression residual from random spatial permutations }
- \item{av_diff:}{ average difference between LR_expr and rand_expr over all random spatial permutations }
- \item{sd_diff:}{ (optional) standard deviation of the difference between LR_expr and rand_expr over all random spatial permutations }
- \item{z_score:}{ (optinal) z-score }
- \item{log2fc:}{ LR_expr - rand_expr }
- \item{pvalue:}{ p-value }
- \item{LR_cell_comb:}{ cell type pair combination }
- \item{p.adj:}{ adjusted p-value }
- \item{PI:}{ significanc score: log2fc * -log10(p.adj) }
+ * LR_comb: Pair of ligand and receptor
+ * lig_cell_type: cell type to assess expression level of ligand
+ * lig_expr: average expression residual (observed - DWLS_predicted) of
+ ligand in lig_cell_type
+ * ligand: ligand name
+ * rec_cell_type: cell type to assess expression level of receptor
+ * rec_expr: average expression residual(observed - DWLS_predicted) of
+ receptor in rec_cell_type
+ * receptor: receptor name
+ * LR_expr: combined average ligand and receptor expression
+ * lig_nr: total number of cells from lig_cell_type that spatially interact
+ with cells from rec_cell_type
+ * rec_nr: total number of cells from rec_cell_type that spatially interact
+ with cells from lig_cell_type
+ * rand_expr: average combined ligand and receptor expression residual from
+ random spatial permutations
+ * av_diff: average difference between LR_expr and rand_expr over all random
+ spatial permutations
+ * sd_diff: (optional) standard deviation of the difference between LR_expr
+ and rand_expr over all random spatial permutations
+ * z_score: (optinal) z-score
+ * log2fc: LR_expr - rand_expr
+ * pvalue: p-value
+ * LR_cell_comb: cell type pair combination
+ * p.adj: adjusted p-value
+ * PI: significance score: log2fc \* -log10(p.adj)
 }
 }
 \keyword{internal}
diff --git a/man/dot-trakem2_rigid_transforms.Rd b/man/dot-trakem2_rigid_transforms.Rd
index bd267ec00..29ea8da33 100644
--- a/man/dot-trakem2_rigid_transforms.Rd
+++ b/man/dot-trakem2_rigid_transforms.Rd
@@ -13,7 +13,7 @@
 rigid registration transformation values
 }
 \description{
-Extract rigid registration transformation values from FIJI 
+Extract rigid registration transformation values from FIJI
 TrakEM2 xml file. Generated through register_virtual_stack_slices.
 }
 \keyword{internal}
diff --git a/man/estimateCellCellDistance.Rd b/man/estimateCellCellDistance.Rd
index 0ce0d3ce4..0c5f1ed6c 100644
--- a/man/estimateCellCellDistance.Rd
+++ b/man/estimateCellCellDistance.Rd
@@ -6,6 +6,7 @@
 \usage{
 estimateCellCellDistance(
   gobject,
+  spat_unit = NULL,
   spatial_network_name = "Delaunay_network",
   method = c("mean", "median")
 )
@@ -13,6 +14,8 @@ estimateCellCellDistance(
 \arguments{
 \item{gobject}{gobject}
 
+\item{spat_unit}{spatial unit}
+
 \item{spatial_network_name}{spatial_network_name}
 
 \item{method}{method}
diff --git a/man/exprCellCellcom.Rd b/man/exprCellCellcom.Rd
index b571651d3..e3d89cef8 100644
--- a/man/exprCellCellcom.Rd
+++ b/man/exprCellCellcom.Rd
@@ -70,6 +70,11 @@ More details will follow soon.
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-exprCellCellcom(g, cluster_column = "leiden_clus",
-feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik")
+res <- exprCellCellcom(g,
+    cluster_column = "leiden_clus",
+    feat_set_1 = "Gm19935",
+    feat_set_2 = "9630013A20Rik"
+)
+
+force(res)
 }
diff --git a/man/featExpDWLS.Rd b/man/featExpDWLS.Rd
index 32daecbc2..9399c4d0b 100644
--- a/man/featExpDWLS.Rd
+++ b/man/featExpDWLS.Rd
@@ -19,6 +19,6 @@ featExpDWLS(gobject, spat_unit = NULL, feat_type = NULL, ave_celltype_exp)
 matrix
 }
 \description{
-Compute predicted feature expression value by spatialDWSL 
+Compute predicted feature expression value by spatialDWSL
 results and average feature expression for cell type
 }
diff --git a/man/filterCPG.Rd b/man/filterCPG.Rd
index db948b69d..5e1173204 100644
--- a/man/filterCPG.Rd
+++ b/man/filterCPG.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{filterCPG}
 \alias{filterCPG}
-\title{filterCPG}
+\title{deprecated}
 \usage{
 filterCPG(...)
 }
@@ -20,7 +20,7 @@ cell type}
     \item{\code{min_spat_diff}}{minimum absolute spatial expression difference}
     \item{\code{min_log2_fc}}{minimum log2 fold-change}
     \item{\code{min_zscore}}{minimum z-score change}
-    \item{\code{zscores_column}}{calculate z-scores over cell types or features}
+    \item{\code{zscores_column}}{calculate z-scores over cell types or genes}
     \item{\code{direction}}{differential expression directions to keep}
   }}
 }
diff --git a/man/filterCellProximityGenes.Rd b/man/filterCellProximityGenes.Rd
index ca4d925ec..957ed386f 100644
--- a/man/filterCellProximityGenes.Rd
+++ b/man/filterCellProximityGenes.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{filterCellProximityGenes}
 \alias{filterCellProximityGenes}
-\title{filterCellProximityGenes}
+\title{deprecated}
 \usage{
 filterCellProximityGenes(...)
 }
diff --git a/man/filterICF.Rd b/man/filterICF.Rd
deleted file mode 100644
index a8698c95e..000000000
--- a/man/filterICF.Rd
+++ /dev/null
@@ -1,57 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/spatial_interaction.R
-\name{filterICF}
-\alias{filterICF}
-\title{filterICF}
-\usage{
-filterICF(
-  icfObject,
-  min_cells = 4,
-  min_cells_expr = 1,
-  min_int_cells = 4,
-  min_int_cells_expr = 1,
-  min_fdr = 0.1,
-  min_spat_diff = 0.2,
-  min_log2_fc = 0.2,
-  min_zscore = 2,
-  zscores_column = c("cell_type", "feats"),
-  direction = c("both", "up", "down")
-)
-}
-\arguments{
-\item{icfObject}{ICF (interaction changed feature) score object}
-
-\item{min_cells}{minimum number of source cell type}
-
-\item{min_cells_expr}{minimum expression level for source cell type}
-
-\item{min_int_cells}{minimum number of interacting neighbor cell type}
-
-\item{min_int_cells_expr}{minimum expression level for interacting neighbor
-cell type}
-
-\item{min_fdr}{minimum adjusted p-value}
-
-\item{min_spat_diff}{minimum absolute spatial expression difference}
-
-\item{min_log2_fc}{minimum log2 fold-change}
-
-\item{min_zscore}{minimum z-score change}
-
-\item{zscores_column}{calculate z-scores over cell types or features}
-
-\item{direction}{differential expression directions to keep}
-}
-\value{
-icfObject that contains the filtered differential feature scores
-}
-\description{
-Filter Interaction Changed Feature scores.
-}
-\examples{
-g <- GiottoData::loadGiottoMini("visium")
-
-g_icf <- findInteractionChangedFeats(g, cluster_column = "leiden_clus")
-
-filterICF(g_icf)
-}
diff --git a/man/filterICFSpot.Rd b/man/filterICFSpot.Rd
index 46e8b851a..75ed8a6a4 100644
--- a/man/filterICFSpot.Rd
+++ b/man/filterICFSpot.Rd
@@ -22,12 +22,12 @@ filterICFSpot(
 
 \item{min_cells}{minimum number of source cell type}
 
-\item{min_cells_expr_resi}{minimum expression residual level for source 
+\item{min_cells_expr_resi}{minimum expression residual level for source
 cell type}
 
 \item{min_int_cells}{minimum number of interacting neighbor cell type}
 
-\item{min_int_cells_expr_resi}{minimum expression residual level for 
+\item{min_int_cells_expr_resi}{minimum expression residual level for
 interacting neighbor cell type}
 
 \item{min_fdr}{minimum adjusted p-value}
diff --git a/man/filterICG.Rd b/man/filterICG.Rd
index 09f9a3da7..d15ade247 100644
--- a/man/filterICG.Rd
+++ b/man/filterICG.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{filterICG}
 \alias{filterICG}
-\title{filterICG}
+\title{deprecated}
 \usage{
 filterICG(...)
 }
@@ -20,7 +20,7 @@ cell type}
     \item{\code{min_spat_diff}}{minimum absolute spatial expression difference}
     \item{\code{min_log2_fc}}{minimum log2 fold-change}
     \item{\code{min_zscore}}{minimum z-score change}
-    \item{\code{zscores_column}}{calculate z-scores over cell types or features}
+    \item{\code{zscores_column}}{calculate z-scores over cell types or genes}
     \item{\code{direction}}{differential expression directions to keep}
   }}
 }
diff --git a/man/filterInteractionChangedFeats.Rd b/man/filterInteractionChangedFeats.Rd
index 0a0f3df16..06998f34d 100644
--- a/man/filterInteractionChangedFeats.Rd
+++ b/man/filterInteractionChangedFeats.Rd
@@ -2,6 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{filterInteractionChangedFeats}
 \alias{filterInteractionChangedFeats}
+\alias{filterICF}
 \title{filterInteractionChangedFeats}
 \usage{
 filterInteractionChangedFeats(
@@ -17,6 +18,20 @@ filterInteractionChangedFeats(
   zscores_column = c("cell_type", "feats"),
   direction = c("both", "up", "down")
 )
+
+filterICF(
+  icfObject,
+  min_cells = 4,
+  min_cells_expr = 1,
+  min_int_cells = 4,
+  min_int_cells_expr = 1,
+  min_fdr = 0.1,
+  min_spat_diff = 0.2,
+  min_log2_fc = 0.2,
+  min_zscore = 2,
+  zscores_column = c("cell_type", "feats"),
+  direction = c("both", "up", "down")
+)
 }
 \arguments{
 \item{icfObject}{ICF (interaction changed feature) score object}
@@ -43,8 +58,28 @@ cell type}
 \item{direction}{differential expression directions to keep}
 }
 \value{
-icfObject that contains the filtered differential feature scores
+\code{icfObject} that contains the filtered differential feature scores
 }
 \description{
 Filter Interaction Changed Feature scores.
 }
+\examples{
+g <- GiottoData::loadGiottoMini("visium")
+
+icf <- findInteractionChangedFeats(g,
+     cluster_column = "leiden_clus",
+     selected_feats = c("Gna12", "Ccnd2", "Btbd17"),
+     nr_permutations = 10
+)
+force(icf)
+force(icf$ICFscores)
+
+icf_filter1 <- filterInteractionChangedFeats(icf, min_cells = 4)
+force(icf_filter1)
+force(icf_filter1$ICFscores)
+
+# filterICF is a simple alias with a shortened name
+icf_filter2 <- filterICF(icf, min_cells = 4)
+force(icf_filter2)
+
+}
diff --git a/man/filterInteractionChangedGenes.Rd b/man/filterInteractionChangedGenes.Rd
index 6de8591cc..236490145 100644
--- a/man/filterInteractionChangedGenes.Rd
+++ b/man/filterInteractionChangedGenes.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{filterInteractionChangedGenes}
 \alias{filterInteractionChangedGenes}
-\title{filterInteractionChangedGenes}
+\title{deprecated}
 \usage{
 filterInteractionChangedGenes(...)
 }
diff --git a/man/filterSpatialGenes.Rd b/man/filterSpatialGenes.Rd
index 8b301f8f1..63af3633e 100644
--- a/man/filterSpatialGenes.Rd
+++ b/man/filterSpatialGenes.Rd
@@ -36,7 +36,7 @@ list
 function to filter gene list with existing spatial gene sets
 }
 \details{
-This function filters given gene list with the gene sets of selected 
+This function filters given gene list with the gene sets of selected
 spatial gene test in Giotto,
 also controls the total size of the gene set with given max number.
 }
diff --git a/man/findCPG.Rd b/man/findCPG.Rd
index 12a52c745..0e7975769 100644
--- a/man/findCPG.Rd
+++ b/man/findCPG.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{findCPG}
 \alias{findCPG}
-\title{findCPG}
+\title{deprecated}
 \usage{
 findCPG(...)
 }
diff --git a/man/findCellProximityGenes.Rd b/man/findCellProximityGenes.Rd
index 401ad4042..031ca177c 100644
--- a/man/findCellProximityGenes.Rd
+++ b/man/findCellProximityGenes.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{findCellProximityGenes}
 \alias{findCellProximityGenes}
-\title{findCellProximityGenes}
+\title{deprecated}
 \usage{
 findCellProximityGenes(...)
 }
diff --git a/man/findCellTypesFromEnrichment.Rd b/man/findCellTypesFromEnrichment.Rd
index 093b2f84a..e919112a5 100644
--- a/man/findCellTypesFromEnrichment.Rd
+++ b/man/findCellTypesFromEnrichment.Rd
@@ -17,7 +17,7 @@ findCellTypesFromEnrichment(
 
 \item{spat_unit}{spatial unit in which the enrichment information is stored}
 
-\item{feat_type}{feature type for which the enrichment information was 
+\item{feat_type}{feature type for which the enrichment information was
 calculated}
 
 \item{enrichment_name}{name of the spatial enrichment
diff --git a/man/findICF.Rd b/man/findICF.Rd
deleted file mode 100644
index 86e830342..000000000
--- a/man/findICF.Rd
+++ /dev/null
@@ -1,105 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/spatial_interaction.R
-\name{findICF}
-\alias{findICF}
-\title{findICF}
-\usage{
-findICF(
-  gobject,
-  feat_type = NULL,
-  spat_unit = NULL,
-  expression_values = "normalized",
-  selected_feats = NULL,
-  cluster_column,
-  spatial_network_name = "Delaunay_network",
-  minimum_unique_cells = 1,
-  minimum_unique_int_cells = 1,
-  diff_test = c("permutation", "limma", "t.test", "wilcox"),
-  mean_method = c("arithmic", "geometric"),
-  offset = 0.1,
-  adjust_method = c("bonferroni", "BH", "holm", "hochberg", "hommel", "BY", "fdr",
-    "none"),
-  nr_permutations = 100,
-  exclude_selected_cells_from_test = TRUE,
-  do_parallel = TRUE,
-  set_seed = TRUE,
-  seed_number = 1234
-)
-}
-\arguments{
-\item{gobject}{giotto object}
-
-\item{feat_type}{feature type}
-
-\item{spat_unit}{spatial unit}
-
-\item{expression_values}{expression values to use}
-
-\item{selected_feats}{subset of selected features (optional)}
-
-\item{cluster_column}{name of column to use for cell types}
-
-\item{spatial_network_name}{name of spatial network to use}
-
-\item{minimum_unique_cells}{minimum number of target cells required}
-
-\item{minimum_unique_int_cells}{minimum number of interacting cells required}
-
-\item{diff_test}{which differential expression test}
-
-\item{mean_method}{method to use to calculate the mean}
-
-\item{offset}{offset value to use when calculating log2 ratio}
-
-\item{adjust_method}{which method to adjust p-values}
-
-\item{nr_permutations}{number of permutations if diff_test = permutation}
-
-\item{exclude_selected_cells_from_test}{exclude interacting cells other cells}
-
-\item{do_parallel}{run calculations in parallel with mclapply}
-
-\item{set_seed}{set a seed for reproducibility}
-
-\item{seed_number}{seed number}
-}
-\value{
-`icfObject` that contains the Interaction Changed differential gene
-scores
-}
-\description{
-Identifies cell-to-cell Interaction Changed Features (ICF),
-i.e. features that are differentially expressed due to proximity to other
-cell types.
-}
-\details{
-Function to calculate if genes are differentially expressed in
-cell types when they interact (approximated by physical proximity) with
-other cell types. The results data.table in the `icfObject` contains
-- at least - the following columns:
-\itemize{
- \item{features:}{ All or selected list of tested features}
- \item{sel:}{ average feature expression in the interacting cells from the target cell type }
- \item{other:}{ average feature expression in the NOT-interacting cells from the target cell type }
- \item{log2fc:}{ log2 fold-change between sel and other}
- \item{diff:}{ spatial expression difference between sel and other}
- \item{p.value:}{ associated p-value}
- \item{p.adj:}{ adjusted p-value}
- \item{cell_type:}{ target cell type}
- \item{int_cell_type:}{ interacting cell type}
- \item{nr_select:}{ number of cells for selected target cell type}
- \item{int_nr_select:}{ number of cells for interacting cell type}
- \item{nr_other:}{ number of other cells of selected target cell type}
- \item{int_nr_other:}{ number of other cells for interacting cell type}
- \item{unif_int:}{ cell-cell interaction}
-}
-}
-\examples{
-g <- GiottoData::loadGiottoMini("visium")
-
-findICF(g, cluster_column = "leiden_clus",
-selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
-}
-\seealso{
-\code{\link{findInteractionChangedFeats}}
-}
diff --git a/man/findICFSpot.Rd b/man/findICFSpot.Rd
index 6f2390d58..500f53e2d 100644
--- a/man/findICFSpot.Rd
+++ b/man/findICFSpot.Rd
@@ -12,6 +12,7 @@ findICFSpot(
   ave_celltype_exp,
   selected_features = NULL,
   spatial_network_name = "Delaunay_network",
+  deconv_name = "DWLS",
   minimum_unique_cells = 5,
   minimum_unique_int_cells = 5,
   CCI_cell_score = 0.1,
@@ -41,6 +42,8 @@ findICFSpot(
 
 \item{spatial_network_name}{name of spatial network to use}
 
+\item{deconv_name}{name of deconvolution/spatial enrichment values to use}
+
 \item{minimum_unique_cells}{minimum number of target cells required}
 
 \item{minimum_unique_int_cells}{minimum number of interacting cells required}
@@ -66,41 +69,70 @@ findICFSpot(
 \item{verbose}{be verbose}
 }
 \value{
-icfObject that contains the differential feat scores
+\code{icfObject} that contains the differential feat scores
 }
 \description{
-Identifies cell-to-cell Interaction Changed Features (ICF) for 
-spots, i.e. features expression residual that are different due to proximity 
-to other cell types.
+Identifies cell-to-cell Interaction Changed Features (ICF) for
+spots, i.e. features expression residual that are different due to proximity
+to other cell types. Works using results from celltype deconvolution methods
+such as those from \code{\link[=runDWLSDeconv]{runDWLSDeconv()}}.
 }
 \details{
-Function to calculate if features expression residual are 
-differentially expressed in cell types when they interact 
+Function to calculate if features expression residual are
+differentially expressed in cell types when they interact
 (approximated by physical proximity) with other cell types.
 Feature expression residual calculated as:
-(observed expression in spot - cell_type_proportion * 
+(observed expression in spot - cell_type_proportion *
 average_expressed_in_cell_type)
-The results data.table in the icfObject contains - at least - 
+The results data.table in the \code{icfObject} contains - at least -
 the following columns:
 \itemize{
- \item{features:}{ All or selected list of tested features}
- \item{sel:}{ average feature expression residual in the interacting cells from the target cell type }
- \item{other:}{ average feature expression residual in the NOT-interacting cells from the target cell type }
- \item{pcc_sel:}{ correlation between cell proximity score and expression residual in the interacting cells from the target cell type}
- \item{pcc_other:}{ correlation between cell proximity score and expression residual in the NOT-interacting cells from the target cell type }
- \item{pcc_diff:}{ correlation difference between sel and other}
- \item{p.value:}{ associated p-value}
- \item{p.adj:}{ adjusted p-value}
- \item{cell_type:}{ target cell type}
- \item{int_cell_type:}{ interacting cell type}
- \item{nr_select:}{ number of cells for selected target cell type}
- \item{int_nr_select:}{ number of cells for interacting cell type}
- \item{unif_int:}{ cell-cell interaction}
+\item \strong{features:} All or selected list of tested features
+\item \strong{sel:} average feature expression residual in the interacting cells
+from the target cell type
+\item \strong{other:} average feature expression residual in the NOT-interacting
+cells from the target cell type
+\item \strong{pcc_sel:} correlation between cell proximity score and expression
+residual in the interacting cells from the target cell type
+\item \strong{pcc_other:} correlation between cell proximity score and expression
+residual in the NOT-interacting cells from the target cell type
+\item \strong{pcc_diff:} correlation difference between sel and other
+\item \strong{p.value:} associated p-value
+\item \strong{p.adj:} adjusted p-value
+\item \strong{cell_type:} target cell type
+\item \strong{int_cell_type:} interacting cell type
+\item \strong{nr_select:} number of cells for selected target cell type
+\item \strong{int_nr_select:} number of cells for interacting cell type
+\item \strong{unif_int:} cell-cell interaction
 }
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
-g_expression <- getExpression(g, output = "matrix")
+x <- findMarkers_one_vs_all(g,
+    cluster_column = "leiden_clus", min_feats = 20
+)
+sign_gene <- x$feats
 
-findICFSpot(g, spat_unit = "cell", feat_type = "rna", ave_celltype_exp = g_expression, spatial_network_name = "spatial_network")
+sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+    nrow = length(sign_gene)
+)
+rownames(sign_matrix) <- sign_gene
+colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
+
+g <- runDWLSDeconv(gobject = g, sign_matrix = sign_matrix)
+ave_celltype_exp <- calculateMetaTable(g, metadata_cols = "leiden_clus")
+ave_celltype_exp <- reshape2::dcast(ave_celltype_exp, variable~leiden_clus)
+rownames(ave_celltype_exp) <- ave_celltype_exp$variable
+ave_celltype_exp <- ave_celltype_exp[,-1]
+colnames(ave_celltype_exp) <- colnames(sign_matrix)
+
+findICFSpot(g,
+    spat_unit = "cell",
+    feat_type = "rna",
+    ave_celltype_exp = ave_celltype_exp,
+    spatial_network_name = "spatial_network"
+)
+}
+\seealso{
+\code{\link[=findInteractionChangedFeats]{findInteractionChangedFeats()}}
 }
diff --git a/man/findICG.Rd b/man/findICG.Rd
index b6617ccec..170d83f2a 100644
--- a/man/findICG.Rd
+++ b/man/findICG.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{findICG}
 \alias{findICG}
-\title{findICG}
+\title{deprecated}
 \usage{
 findICG(...)
 }
diff --git a/man/findInteractionChangedFeats.Rd b/man/findInteractionChangedFeats.Rd
index 0252ce158..e47ebf356 100644
--- a/man/findInteractionChangedFeats.Rd
+++ b/man/findInteractionChangedFeats.Rd
@@ -2,6 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{findInteractionChangedFeats}
 \alias{findInteractionChangedFeats}
+\alias{findICF}
 \title{findInteractionChangedFeats}
 \usage{
 findInteractionChangedFeats(
@@ -25,6 +26,28 @@ findInteractionChangedFeats(
   set_seed = TRUE,
   seed_number = 1234
 )
+
+findICF(
+  gobject,
+  feat_type = NULL,
+  spat_unit = NULL,
+  expression_values = "normalized",
+  selected_feats = NULL,
+  cluster_column,
+  spatial_network_name = "Delaunay_network",
+  minimum_unique_cells = 1,
+  minimum_unique_int_cells = 1,
+  diff_test = c("permutation", "limma", "t.test", "wilcox"),
+  mean_method = c("arithmic", "geometric"),
+  offset = 0.1,
+  adjust_method = c("bonferroni", "BH", "holm", "hochberg", "hommel", "BY", "fdr",
+    "none"),
+  nr_permutations = 1000,
+  exclude_selected_cells_from_test = TRUE,
+  do_parallel = TRUE,
+  set_seed = TRUE,
+  seed_number = 1234
+)
 }
 \arguments{
 \item{gobject}{giotto object}
@@ -64,39 +87,61 @@ findInteractionChangedFeats(
 \item{seed_number}{seed number}
 }
 \value{
-icfObject that contains the Interaction Changed differential
+\code{icfObject} that contains the Interaction Changed differential
 feature scores
 }
 \description{
 Identifies cell-to-cell Interaction Changed Features (ICF),
 i.e. features that are differentially expressed due to proximity to other
-cell types.
+cell types. This function is appropriate for single-cell level data. For
+data from spot-based spatial assays or spatially binned data, see
+\code{\link[=findICFSpot]{findICFSpot()}}, which runs on top of DWLS results or similar spot-level
+cell-type enrichment outputs
 }
 \details{
 Function to calculate if features are differentially expressed in
 cell types when they interact (approximated by physical proximity) with
-other cell types. The results data.table in the icfObject contains
-- at least - the following columns:
+other cell types. The results data.table in the \code{icfObject} contains
+\itemize{
+\item at least - the following columns:
 \itemize{
- \item{features:}{ All or selected list of tested features}
- \item{sel:}{ average feature expression in the interacting cells from the target cell type }
- \item{other:}{ average feature expression in the NOT-interacting cells from the target cell type }
- \item{log2fc:}{ log2 fold-change between sel and other}
- \item{diff:}{ spatial expression difference between sel and other}
- \item{p.value:}{ associated p-value}
- \item{p.adj:}{ adjusted p-value}
- \item{cell_type:}{ target cell type}
- \item{int_cell_type:}{ interacting cell type}
- \item{nr_select:}{ number of cells for selected target cell type}
- \item{int_nr_select:}{ number of cells for interacting cell type}
- \item{nr_other:}{ number of other cells of selected target cell type}
- \item{int_nr_other:}{ number of other cells for interacting cell type}
- \item{unif_int:}{ cell-cell interaction}
+\item \strong{features:} All or selected list of tested features
+\item \strong{sel:} average feature expression in the interacting cells from the
+target cell type
+\item \strong{other:} average feature expression in the NOT-interacting cells from
+the target cell type
+\item \strong{log2fc:} log2 fold-change between sel and other
+\item \strong{diff:} spatial expression difference between sel and other
+\item \strong{p.value:} associated p-value
+\item \strong{p.adj:} adjusted p-value
+\item \strong{cell_type:} target cell type
+\item \strong{int_cell_type:} interacting cell type
+\item \strong{nr_select:} number of cells for selected target cell type
+\item \strong{int_nr_select:} number of cells for interacting cell type
+\item \strong{nr_other:} number of other cells of selected target cell type
+\item \strong{int_nr_other:} number of other cells for interacting cell type
+\item \strong{unif_int:} cell-cell interaction
+}
 }
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-findInteractionChangedFeats(g, cluster_column = "leiden_clus",
-selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
+icf1 <- findInteractionChangedFeats(g,
+    cluster_column = "leiden_clus",
+    selected_feats = c("Gna12", "Ccnd2", "Btbd17"),
+    nr_permutations = 10
+)
+force(icf1)
+force(icf1$ICFscores)
+
+# this is just an alias with a shorter name
+icf2 <- findICF(g,
+    cluster_column = "leiden_clus",
+    selected_feats = c("Gna12", "Ccnd2", "Btbd17"),
+    nr_permutations = 10
+ )
+}
+\seealso{
+\code{\link[=filterInteractionChangedFeats]{filterInteractionChangedFeats()}} \code{\link[=findICFSpot]{findICFSpot()}}
 }
diff --git a/man/findInteractionChangedGenes.Rd b/man/findInteractionChangedGenes.Rd
index 72339c8ce..43351c763 100644
--- a/man/findInteractionChangedGenes.Rd
+++ b/man/findInteractionChangedGenes.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{findInteractionChangedGenes}
 \alias{findInteractionChangedGenes}
-\title{findInteractionChangedGenes}
+\title{deprecated}
 \usage{
 findInteractionChangedGenes(...)
 }
diff --git a/man/findMastMarkers.Rd b/man/findMastMarkers.Rd
index fe2114267..09f100047 100644
--- a/man/findMastMarkers.Rd
+++ b/man/findMastMarkers.Rd
@@ -63,6 +63,8 @@ MAST might take a long time to run and finish
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-findMastMarkers(gobject = g, cluster_column = "leiden_clus", group_1 = 1,
-group_2 = 2)
+findMastMarkers(
+    gobject = g, cluster_column = "leiden_clus", group_1 = 1,
+    group_2 = 2
+)
 }
diff --git a/man/findNetworkNeighbors.Rd b/man/findNetworkNeighbors.Rd
index 827c3edd7..04fb0942a 100644
--- a/man/findNetworkNeighbors.Rd
+++ b/man/findNetworkNeighbors.Rd
@@ -34,6 +34,8 @@ the selected spatial network.
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-findNetworkNeighbors(gobject = g, spatial_network_name = "spatial_network",
-source_cell_ids = c("AACTCGATGGCGCAGT-1", "GGCTGGCTAGCTTAAA-1"))
+findNetworkNeighbors(
+    gobject = g, spatial_network_name = "spatial_network",
+    source_cell_ids = c("AACTCGATGGCGCAGT-1", "GGCTGGCTAGCTTAAA-1")
+)
 }
diff --git a/man/get10Xmatrix.Rd b/man/get10Xmatrix.Rd
index 65883902b..70b4a6eaa 100644
--- a/man/get10Xmatrix.Rd
+++ b/man/get10Xmatrix.Rd
@@ -14,31 +14,31 @@ get10Xmatrix(
 \arguments{
 \item{path_to_data}{path to the 10X folder}
 
-\item{gene_column_index}{which column from the features or genes .tsv file 
+\item{gene_column_index}{which column from the features or genes .tsv file
 to use for row ids}
 
 \item{remove_zero_rows}{removes rows with sum equal to zero}
 
-\item{split_by_type}{split into multiple matrices based on 3rd column of 
+\item{split_by_type}{split into multiple matrices based on 3rd column of
 features.tsv(.gz)}
 }
 \value{
 sparse expression matrix from 10X
 }
 \description{
-This function creates an expression matrix from a 10X 
+This function creates an expression matrix from a 10X
 structured folder
 }
 \details{
-A typical 10X folder is named raw_feature_bc_matrix or 
+A typical 10X folder is named raw_feature_bc_matrix or
 filtered_feature_bc_matrix and it has 3 files:
 \itemize{
   \item{barcodes.tsv(.gz)}
   \item{features.tsv(.gz) or genes.tsv(.gz)}
   \item{matrix.mtx(.gz)}
 }
-By default the first column of the features or genes .tsv file will be used, 
+By default the first column of the features or genes .tsv file will be used,
 however if multiple
-annotations are provided (e.g. ensembl gene ids and gene symbols) the user 
+annotations are provided (e.g. ensembl gene ids and gene symbols) the user
 can select another column.
 }
diff --git a/man/get10Xmatrix_h5.Rd b/man/get10Xmatrix_h5.Rd
index 81802562c..22950009c 100644
--- a/man/get10Xmatrix_h5.Rd
+++ b/man/get10Xmatrix_h5.Rd
@@ -14,23 +14,23 @@ get10Xmatrix_h5(
 \arguments{
 \item{path_to_data}{path to the 10X .h5 file}
 
-\item{gene_ids}{use gene symbols (default) or ensembl ids for the gene 
+\item{gene_ids}{use gene symbols (default) or ensembl ids for the gene
 expression matrix}
 
 \item{remove_zero_rows}{removes rows with sum equal to zero}
 
-\item{split_by_type}{split into multiple matrices based on 3rd column of 
+\item{split_by_type}{split into multiple matrices based on 3rd column of
 features.tsv(.gz)}
 }
 \value{
 (list of) sparse expression matrix from 10X
 }
 \description{
-This function creates an expression matrix from a 10X h5 file 
+This function creates an expression matrix from a 10X h5 file
 path
 }
 \details{
-If the .h5 10x file has multiple classes of features 
-(e.g. expression vs QC probes) or modalities (e.g. RNA and protein), and 
+If the .h5 10x file has multiple classes of features
+(e.g. expression vs QC probes) or modalities (e.g. RNA and protein), and
 \code{split_by_type} param is \code{TRUE}, multiple matrices will be returned
 }
diff --git a/man/getBalancedSpatCoexpressionFeats.Rd b/man/getBalancedSpatCoexpressionFeats.Rd
index 674807070..1bac56357 100644
--- a/man/getBalancedSpatCoexpressionFeats.Rd
+++ b/man/getBalancedSpatCoexpressionFeats.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/spatial_genes.R
 \name{getBalancedSpatCoexpressionFeats}
 \alias{getBalancedSpatCoexpressionFeats}
-\title{getBalancedSpatCoexpressionFeats}
+\title{Get balanced spatial coexpression features}
 \usage{
 getBalancedSpatCoexpressionFeats(
   spatCorObject,
@@ -37,9 +37,11 @@ balanced manner
 \details{
 There are 3 different ways of selecting features from the spatial
 co-expression modules
-\itemize{
-  \item{1. weighted: }{Features are ranked based on summarized pairwise co-expression scores}
-  \item{2. random: }{A random selection of features, set seed for reproducibility}
-  \item{3. informed: }{Features are selected based on prior information/ranking}
+\enumerate{
+\item \strong{weighted:} Features are ranked based on summarized pairwise
+co-expression scores
+\item \strong{random:} A random selection of features, set seed for
+reproducibility
+\item \strong{informed:} Features are selected based on prior information/ranking
 }
 }
diff --git a/man/getCellsFromPolygon.Rd b/man/getCellsFromPolygon.Rd
index 127fedba7..56fa70eea 100644
--- a/man/getCellsFromPolygon.Rd
+++ b/man/getCellsFromPolygon.Rd
@@ -21,11 +21,11 @@ getCellsFromPolygon(
 
 \item{spat_loc_name}{name of spatial locations to use, default = 'raw'}
 
-\item{polygons}{character. A vector with polygon names to extract cells 
+\item{polygons}{character. A vector with polygon names to extract cells
 from. If NULL, cells from all polygons are retrieved}
 }
 \value{
-A terra 'SpatVector' with cell ID, x y coordinates, and polygon ID 
+A terra 'SpatVector' with cell ID, x y coordinates, and polygon ID
 where each cell is located in.
 }
 \description{
@@ -34,12 +34,15 @@ Get cells located within the polygons area
 \examples{
 ## Plot interactive polygons
 g <- GiottoData::loadGiottoMini("visium")
-my_polygon_coords <- data.frame(poly_ID = rep("polygon1", 3), 
-sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202))
+my_polygon_coords <- data.frame(
+    poly_ID = rep("polygon1", 3),
+    sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202)
+)
 
 ## Add polygon coordinates to Giotto object
-my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords, 
-name = "selections")
+my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords,
+    name = "selections"
+)
 g <- addGiottoPolygons(
     gobject = g,
     gpolygons = list(my_giotto_polygons)
diff --git a/man/get_sectionThickness.Rd b/man/get_sectionThickness.Rd
index 017af63c1..d32edafc9 100644
--- a/man/get_sectionThickness.Rd
+++ b/man/get_sectionThickness.Rd
@@ -6,6 +6,7 @@
 \usage{
 get_sectionThickness(
   gobject,
+  spat_unit = NULL,
   thickness_unit = c("cell", "natural"),
   slice_thickness = 2,
   spatial_network_name = "Delaunay_network",
@@ -16,6 +17,8 @@ get_sectionThickness(
 \arguments{
 \item{gobject}{gobject}
 
+\item{spat_unit}{spatial unit}
+
 \item{thickness_unit}{thickness_unit}
 
 \item{spatial_network_name}{spatial_network_name}
diff --git a/man/initHMRF_V2.Rd b/man/initHMRF_V2.Rd
index 4f2726a60..7623ab16d 100644
--- a/man/initHMRF_V2.Rd
+++ b/man/initHMRF_V2.Rd
@@ -51,26 +51,26 @@ initHMRF_V2(
 
 \item{use_spatial_genes}{which of Giotto's spatial genes to use}
 
-\item{use_score}{use score as gene selection criterion 
+\item{use_score}{use score as gene selection criterion
 (applies when use_spatial_genes=silhouetteRank)}
 
 \item{gene_list_from_top}{total spatial genes before sampling}
 
-\item{filter_method}{filter genes by top or by elbow method, prior to 
+\item{filter_method}{filter genes by top or by elbow method, prior to
 sampling}
 
 \item{user_gene_list}{user-specified genes (optional)}
 
-\item{use_pca}{if PCA is used on the spatial gene expression value for 
+\item{use_pca}{if PCA is used on the spatial gene expression value for
 clustering}
 
 \item{use_pca_dim}{dimensions of the PCs of the selected expression}
 
 \item{gene_samples}{number of spatial gene subset to use for HMRF}
 
-\item{gene_sampling_rate}{parameter (1-50) controlling proportion of gene 
-samples from different module when sampling, 1 corresponding to equal gene 
-samples between different modules; 50 corresponding to gene samples 
+\item{gene_sampling_rate}{parameter (1-50) controlling proportion of gene
+samples from different module when sampling, 1 corresponding to equal gene
+samples between different modules; 50 corresponding to gene samples
 proportional to module size.}
 
 \item{gene_sampling_seed}{random number seed to sample spatial genes}
@@ -79,24 +79,24 @@ proportional to module size.}
 
 \item{cluster_metagene}{number of metagenes to use}
 
-\item{top_metagene}{= number of genes in each cluster for the metagene 
+\item{top_metagene}{= number of genes in each cluster for the metagene
 calculation}
 
-\item{existing_spatial_enrichm_to_use}{name of existing spatial enrichment 
+\item{existing_spatial_enrichm_to_use}{name of existing spatial enrichment
 result to use}
 
-\item{use_neighborhood_composition}{if neighborhood composition is used for 
+\item{use_neighborhood_composition}{if neighborhood composition is used for
 hmrf}
 
-\item{spatial_network_name_for_neighborhood}{spatial network used to 
+\item{spatial_network_name_for_neighborhood}{spatial network used to
 calculate neighborhood composition}
 
 \item{metadata_to_use}{metadata used to calculate neighborhood composition}
 
-\item{hmrf_seed}{random number seed to generate initial mean vector of HMRF 
+\item{hmrf_seed}{random number seed to generate initial mean vector of HMRF
 model}
 
-\item{cl.method}{clustering method to calculate the initial mean vector, 
+\item{cl.method}{clustering method to calculate the initial mean vector,
 selecting from 'km', 'leiden', or 'louvain'}
 
 \item{resolution.cl}{resolution of Leiden or Louvain clustering}
@@ -107,7 +107,7 @@ selecting from 'km', 'leiden', or 'louvain'}
 
 \item{zscore}{type of zscore to use}
 
-\item{nstart}{number of Kmeans initializations from which to select the 
+\item{nstart}{number of Kmeans initializations from which to select the
 best initialization}
 
 \item{factor_step}{dampened factor step}
@@ -118,37 +118,43 @@ best initialization}
 initialized HMRF
 }
 \description{
-Run initialzation for HMRF model
+Run initialization for HMRF model
 }
 \details{
-This function is the initialization step of HMRF domain clustering. First, 
+This function is the initialization step of HMRF domain clustering. First,
 user specify which of Giotto's spatial genes to run,
-through use_spatial_genes. Spatial genes have been stored in the gene 
+through use_spatial_genes. Spatial genes have been stored in the gene
 metadata table. A first pass of genes will filter genes that
-are not significantly spatial, as determined by filter_method. If 
+are not significantly spatial, as determined by filter_method. If
 filter_method is none, then top 2500 (gene_list_from_top) genes
-ranked by pvalue are considered spatial. If filter_method is elbow, then the 
+ranked by pvalue are considered spatial. If filter_method is elbow, then the
 exact cutoff is determined by the elbow in
-the -log10 P-value vs. gene rank plot. Second, users have a few options to 
+the -log10 P-value vs. gene rank plot. Second, users have a few options to
 decrease the dimension of the spatial genes for
 clustering, listed with selection priority:
    1. use PCA of the spatial gene expressions (selected by use_pca)
    2. use metagene expressions (selected by use_metagene)
    3. sampling to select 500 spatial genes (controlled by gene_samples).
-Third, once spatial genes are finalized, we are using clustering method to 
+Third, once spatial genes are finalized, we are using clustering method to
 initialize HMRF.
-Instead of select spatial genes for domain clustering, HMRF method could 
-also applied on unit neighbohood composition of any group
-membership(such as cell types), specified by parameter: 
+Instead of select spatial genes for domain clustering, HMRF method could
+also applied on unit neighborhood composition of any group
+membership(such as cell types), specified by parameter:
 use_neighborhood_composition,  spatial_network_name_for_neighborhood and
-metadata_to_use. Also HMRF provides the oppertunity for user to do 
+metadata_to_use. Also HMRF provides the opportunity for user to do
 clustering by any customized spatial enrichment matrix
 (existing_spatial_enrichm_to_use).
-There are 3 clustering algorithm: K-means, Leiden, and Louvain to determine 
+There are 3 clustering algorithm: K-means, Leiden, and Louvain to determine
 initial centroids of HMRF. The initialization is
-then finished. This function returns a list containing y (expression), 
+then finished. This function returns a list containing y (expression),
 nei (neighborhood structure), numnei (number of neighbors),
-blocks (graph colors), damp (dampened factor), mu (mean), 
+blocks (graph colors), damp (dampened factor), mu (mean),
 sigma (covariance), k, genes, edgelist, init.cl (initial clusters),
 spat_unit, feat_type. This information is needed for the second step, doHMRF.
 }
+\examples{
+g <- GiottoData::loadGiottoMini("visium")
+g <- binSpect(g, return_gobject = TRUE)
+
+initHMRF_V2(gobject = g, cl.method = "km")
+}
diff --git a/man/insertCrossSectionGenePlot3D.Rd b/man/insertCrossSectionFeatPlot3D.Rd
similarity index 88%
rename from man/insertCrossSectionGenePlot3D.Rd
rename to man/insertCrossSectionFeatPlot3D.Rd
index ba689ea15..54505b6bb 100644
--- a/man/insertCrossSectionGenePlot3D.Rd
+++ b/man/insertCrossSectionFeatPlot3D.Rd
@@ -1,11 +1,13 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/cross_section.R
-\name{insertCrossSectionGenePlot3D}
-\alias{insertCrossSectionGenePlot3D}
-\title{insertCrossSectionGenePlot3D}
+\name{insertCrossSectionFeatPlot3D}
+\alias{insertCrossSectionFeatPlot3D}
+\title{insertCrossSectionFeatPlot3D}
 \usage{
-insertCrossSectionGenePlot3D(
+insertCrossSectionFeatPlot3D(
   gobject,
+  spat_unit = NULL,
+  feat_type = NULL,
   spat_loc_name = "raw",
   crossSection_obj = NULL,
   name = NULL,
@@ -30,6 +32,10 @@ insertCrossSectionGenePlot3D(
 \arguments{
 \item{gobject}{giotto object}
 
+\item{spat_unit}{spatial unit}
+
+\item{feat_type}{feature type}
+
 \item{spat_loc_name}{name of spatial locations}
 
 \item{crossSection_obj}{cross section object as alternative input.
diff --git a/man/insertCrossSectionSpatPlot3D.Rd b/man/insertCrossSectionSpatPlot3D.Rd
index cdf766d93..92ddb4635 100644
--- a/man/insertCrossSectionSpatPlot3D.Rd
+++ b/man/insertCrossSectionSpatPlot3D.Rd
@@ -6,6 +6,8 @@
 \usage{
 insertCrossSectionSpatPlot3D(
   gobject,
+  spat_unit = NULL,
+  feat_type = NULL,
   spat_loc_name = "raw",
   crossSection_obj = NULL,
   name = NULL,
@@ -26,9 +28,13 @@ insertCrossSectionSpatPlot3D(
 \arguments{
 \item{gobject}{giotto object}
 
+\item{spat_unit}{spatial unit}
+
+\item{feat_type}{feature type}
+
 \item{spat_loc_name}{name of spatial locations}
 
-\item{crossSection_obj}{cross section object as alternative input. 
+\item{crossSection_obj}{cross section object as alternative input.
 default = NULL.}
 
 \item{name}{name of virtual cross section to use}
@@ -53,7 +59,7 @@ default = NULL.}
 
 \item{custom_ratio}{custom_ratio}
 
-\item{default_save_name}{default save name for saving, don't change, 
+\item{default_save_name}{default save name for saving, don't change,
 change save_name in save_param}
 
 \item{...}{parameters for spatPlot3D}
@@ -62,7 +68,7 @@ change save_name in save_param}
 ggplot
 }
 \description{
-Visualize the meshgrid lines of cross section together with 
+Visualize the meshgrid lines of cross section together with
 cells
 }
 \details{
diff --git a/man/interpolateFeature.Rd b/man/interpolateFeature.Rd
index 325ceb5fd..52b3eb239 100644
--- a/man/interpolateFeature.Rd
+++ b/man/interpolateFeature.Rd
@@ -76,11 +76,6 @@ the same `filename` is provided.}
 
 \item{rastersize}{numeric. Length of major axis in px of interpolation
 raster to create.}
-
-\item{name}{name of interpolation `giottoLargeImage` to generate}
-
-\item{filename}{character. Output filename. Default is \[`name`\].tif within
-the working directory.}
 }
 \value{
 `giotto` method returns a `giotto` object with newly made appended
diff --git a/man/jackstrawPlot.Rd b/man/jackstrawPlot.Rd
index 6dd082e9c..6a757e5c7 100644
--- a/man/jackstrawPlot.Rd
+++ b/man/jackstrawPlot.Rd
@@ -69,8 +69,8 @@ ggplot object for jackstraw method
 identify significant prinicipal components (PCs)
 }
 \details{
-The Jackstraw method uses the \code{\link[jackstraw]{permutationPA}} 
- function. By systematically permuting genes it identifies robust, and thus 
+The Jackstraw method uses the \code{\link[jackstraw]{permutationPA}}
+ function. By systematically permuting genes it identifies robust, and thus
  significant, PCs.
 }
 \examples{
diff --git a/man/loadHMRF.Rd b/man/loadHMRF.Rd
index fb1e9d8b0..000d323a0 100644
--- a/man/loadHMRF.Rd
+++ b/man/loadHMRF.Rd
@@ -32,10 +32,14 @@ load previous HMRF
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 x <- tempdir()
-doHMRF(g, spatial_genes = c("Gna12", "Ccnd2"), output_folder = x,
-betas = c(0, 2, 50))
+doHMRF(g,
+    spatial_genes = c("Gna12", "Ccnd2"), output_folder = x,
+    betas = c(0, 2, 50)
+)
 
-loadHMRF(output_folder_used = x, betas_used = c(0, 2, 50), 
-python_path_used = NULL)
+loadHMRF(
+    output_folder_used = x, betas_used = c(0, 2, 50),
+    python_path_used = NULL
+)
 
 }
diff --git a/man/load_merscope_folder.Rd b/man/load_merscope_folder.Rd
index d796bfa5b..ab1f888ed 100644
--- a/man/load_merscope_folder.Rd
+++ b/man/load_merscope_folder.Rd
@@ -33,10 +33,10 @@
 )
 }
 \arguments{
-\item{dir_items}{list of full filepaths from 
+\item{dir_items}{list of full filepaths from
 \code{\link{.read_merscope_folder}}}
 
-\item{data_to_use}{which of either the 'subcellular' or 'aggregate' 
+\item{data_to_use}{which of either the 'subcellular' or 'aggregate'
 information to use for object creation}
 
 \item{cores}{how many cores or threads to use to read data if paths are
diff --git a/man/load_xenium_folder.Rd b/man/load_xenium_folder.Rd
index a6c07895d..73808b43e 100644
--- a/man/load_xenium_folder.Rd
+++ b/man/load_xenium_folder.Rd
@@ -47,13 +47,13 @@
 \item{data_to_use}{which type(s) of expression data to build the gobject with
 (e.g. default: \strong{'subcellular'}, 'aggregate', or 'all')}
 
-\item{h5_expression}{(boolean) whether to load cell_feature_matrix from .h5 
+\item{h5_expression}{(boolean) whether to load cell_feature_matrix from .h5
 file. Default is \code{TRUE}}
 
 \item{h5_gene_ids}{use gene symbols (default) or ensembl ids for the .h5 gene
 expression matrix}
 
-\item{gene_column_index}{which column from the features or genes .tsv file 
+\item{gene_column_index}{which column from the features or genes .tsv file
 to use for row ids}
 
 \item{cores}{how many cores or threads to use to read data if paths are
diff --git a/man/makeSignMatrixDWLS.Rd b/man/makeSignMatrixDWLS.Rd
index 204b848b3..de71e35d3 100644
--- a/man/makeSignMatrixDWLS.Rd
+++ b/man/makeSignMatrixDWLS.Rd
@@ -46,13 +46,17 @@ from the cell metadata (\code{\link{pDataDT}}).
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
-sign_gene <- c("Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
-"Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
-"Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
-"Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b","Ipcef1")
+sign_gene <- c(
+    "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
+    "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
+    "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
+    "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1"
+)
 
-makeSignMatrixDWLS(gobject = g, sign_gene = sign_gene,
-cell_type_vector = pDataDT(g)[["leiden_clus"]])
+makeSignMatrixDWLS(
+    gobject = g, sign_gene = sign_gene,
+    cell_type_vector = pDataDT(g)[["leiden_clus"]]
+)
 }
 \seealso{
 \code{\link{runDWLSDeconv}}
diff --git a/man/makeSignMatrixDWLSfromMatrix.Rd b/man/makeSignMatrixDWLSfromMatrix.Rd
index fffe99932..4873980f1 100644
--- a/man/makeSignMatrixDWLSfromMatrix.Rd
+++ b/man/makeSignMatrixDWLSfromMatrix.Rd
@@ -21,17 +21,21 @@ Function to convert a single-cell RNAseq matrix into a format
  that can be used with \code{\link{runDWLSDeconv}}.
 }
 \examples{
-sign_gene <- c("Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
-"Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
-"Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
-"Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b","Ipcef1")
+sign_gene <- c(
+    "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
+    "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
+    "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
+    "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1"
+)
 
-sign_matrix <- matrix(rnorm(length(sign_gene)*3), nrow = length(sign_gene))
+sign_matrix <- matrix(rnorm(length(sign_gene) * 3), nrow = length(sign_gene))
 rownames(sign_matrix) <- sign_gene
 colnames(sign_matrix) <- c("cell_type1", "cell_type2", "cell_type3")
 
-makeSignMatrixDWLSfromMatrix(matrix = sign_matrix, sign_gene = sign_gene,
-cell_type_vector = c("cell_type1", "cell_type2", "cell_type3"))
+makeSignMatrixDWLSfromMatrix(
+    matrix = sign_matrix, sign_gene = sign_gene,
+    cell_type_vector = c("cell_type1", "cell_type2", "cell_type3")
+)
 }
 \seealso{
 \code{\link{runDWLSDeconv}}
diff --git a/man/makeSignMatrixPAGE.Rd b/man/makeSignMatrixPAGE.Rd
index 07dcd6f5e..805dde245 100644
--- a/man/makeSignMatrixPAGE.Rd
+++ b/man/makeSignMatrixPAGE.Rd
@@ -25,15 +25,25 @@ The names of the cell types or processes that are provided in the list need
 to be given (sign_names).
 }
 \examples{
-sign_list <- list(cell_type1 = c("Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c",
-"Rims2", "Gfap", "Gjc3", "Chrna4", "Prkcd"),
-cell_type2 = c("Prr18", "Grb14", "Tprn", "Clic1", "Olig2", "Hrh3", "Tmbim1",
-"Carhsp1", "Tmem88b", "Ugt8a"),
-cell_type2 = c("Arpp19", "Lamp5", "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4",
-"Cygb", "Ttc9b","Ipcef1"))
+sign_list <- list(
+    cell_type1 = c(
+        "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c",
+        "Rims2", "Gfap", "Gjc3", "Chrna4", "Prkcd"
+    ),
+    cell_type2 = c(
+        "Prr18", "Grb14", "Tprn", "Clic1", "Olig2", "Hrh3", "Tmbim1",
+        "Carhsp1", "Tmem88b", "Ugt8a"
+    ),
+    cell_type2 = c(
+        "Arpp19", "Lamp5", "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4",
+        "Cygb", "Ttc9b", "Ipcef1"
+    )
+)
 
-makeSignMatrixPAGE(sign_names = c("cell_type1", "cell_type2", "cell_type3"),
-sign_list = sign_list)
+makeSignMatrixPAGE(
+    sign_names = c("cell_type1", "cell_type2", "cell_type3"),
+    sign_list = sign_list
+)
 }
 \seealso{
 \code{\link{PAGEEnrich}}
diff --git a/man/makeSignMatrixRank.Rd b/man/makeSignMatrixRank.Rd
index f96c404bf..f6008fdcf 100644
--- a/man/makeSignMatrixRank.Rd
+++ b/man/makeSignMatrixRank.Rd
@@ -30,17 +30,21 @@ and a corresponding single-cell cluster vector into
 a rank matrix that can be used with the Rank enrichment option.
 }
 \examples{
-sign_gene <- c("Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
-"Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
-"Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
-"Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b","Ipcef1")
+sign_gene <- c(
+    "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
+    "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
+    "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
+    "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1"
+)
 
-sign_matrix <- matrix(rnorm(length(sign_gene)*3), nrow = length(sign_gene))
+sign_matrix <- matrix(rnorm(length(sign_gene) * 3), nrow = length(sign_gene))
 rownames(sign_matrix) <- sign_gene
 colnames(sign_matrix) <- c("cell_type1", "cell_type2", "cell_type3")
 
-makeSignMatrixRank(sc_matrix = sign_matrix,
-sc_cluster_ids = c("cell_type1", "cell_type2", "cell_type3"))
+makeSignMatrixRank(
+    sc_matrix = sign_matrix,
+    sc_cluster_ids = c("cell_type1", "cell_type2", "cell_type3")
+)
 }
 \seealso{
 \code{\link{rankEnrich}}
diff --git a/man/numPts_below_line.Rd b/man/numPts_below_line.Rd
index 18561105a..96dc3b617 100644
--- a/man/numPts_below_line.Rd
+++ b/man/numPts_below_line.Rd
@@ -7,23 +7,23 @@
 numPts_below_line(myVector, slope, x)
 }
 \arguments{
-\item{myVector}{input sequence of sorted positive values from smallest to 
+\item{myVector}{input sequence of sorted positive values from smallest to
 greatest}
 
 \item{slope}{slope to compare}
 
-\item{x}{location point of the line to compare, integer from 1 to length of 
+\item{x}{location point of the line to compare, integer from 1 to length of
 myVector}
 }
 \value{
 numeric
 }
 \description{
-function to calculate the number of data points below a given 
+function to calculate the number of data points below a given
 line
 }
 \details{
-This function calculates the number of data points in a sorted sequence 
+This function calculates the number of data points in a sorted sequence
 below a line with given slope through a certain point on this sequence.
 }
 \keyword{internal}
diff --git a/man/pieCellTypesFromEnrichment.Rd b/man/pieCellTypesFromEnrichment.Rd
index 0febd10be..86aac16bf 100644
--- a/man/pieCellTypesFromEnrichment.Rd
+++ b/man/pieCellTypesFromEnrichment.Rd
@@ -22,7 +22,7 @@ pieCellTypesFromEnrichment(
 
 \item{spat_unit}{spatial unit in which the enrichment information is stored}
 
-\item{feat_type}{feature type for which the enrichment information was 
+\item{feat_type}{feature type for which the enrichment information was
 calculated}
 
 \item{enrichment_name}{name of the spatial enrichment
diff --git a/man/plotCCcomDotplot.Rd b/man/plotCCcomDotplot.Rd
index 3a2c48589..854d74f64 100644
--- a/man/plotCCcomDotplot.Rd
+++ b/man/plotCCcomDotplot.Rd
@@ -27,19 +27,19 @@ plotCCcomDotplot(
 \arguments{
 \item{gobject}{giotto object}
 
-\item{comScores}{communication scores from \code{\link{exprCellCellcom}} 
+\item{comScores}{communication scores from \code{\link{exprCellCellcom}}
 or \code{\link{spatCellCellcom}}}
 
 \item{selected_LR}{selected ligand-receptor combinations}
 
-\item{selected_cell_LR}{selected cell-cell combinations for ligand-receptor 
+\item{selected_cell_LR}{selected cell-cell combinations for ligand-receptor
 combinations}
 
 \item{show_LR_names}{show ligand-receptor names}
 
 \item{show_cell_LR_names}{show cell-cell names}
 
-\item{cluster_on}{values to use for clustering of cell-cell and 
+\item{cluster_on}{values to use for clustering of cell-cell and
 ligand-receptor pairs}
 
 \item{cor_method}{correlation method used for clustering}
@@ -66,15 +66,17 @@ or 'sequential' (scaled based on data range)}
 ggplot
 }
 \description{
-Plots dotplot for ligand-receptor communication scores in 
+Plots dotplot for ligand-receptor communication scores in
 cell-cell interactions
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-comScores <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"), 
-feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17"))
+comScores <- exprCellCellcom(g,
+    cluster_column = "leiden_clus",
+    feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"),
+    feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17")
+)
 
 plotCCcomDotplot(gobject = g, comScores = comScores, show_plot = TRUE)
 }
diff --git a/man/plotCCcomHeatmap.Rd b/man/plotCCcomHeatmap.Rd
index ebaf01ba7..cc370e675 100644
--- a/man/plotCCcomHeatmap.Rd
+++ b/man/plotCCcomHeatmap.Rd
@@ -27,12 +27,12 @@ plotCCcomHeatmap(
 \arguments{
 \item{gobject}{giotto object}
 
-\item{comScores}{communinication scores from \code{\link{exprCellCellcom}} 
+\item{comScores}{communinication scores from \code{\link{exprCellCellcom}}
 or \code{\link{spatCellCellcom}}}
 
 \item{selected_LR}{selected ligand-receptor combinations}
 
-\item{selected_cell_LR}{selected cell-cell combinations for ligand-receptor 
+\item{selected_cell_LR}{selected cell-cell combinations for ligand-receptor
 combinations}
 
 \item{show_LR_names}{show ligand-receptor names}
@@ -65,15 +65,17 @@ or 'sequential' (scaled based on data range)}
 ggplot
 }
 \description{
-Plots heatmap for ligand-receptor communication scores in 
+Plots heatmap for ligand-receptor communication scores in
 cell-cell interactions
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-comScores <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"), 
-feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17"))
+comScores <- exprCellCellcom(g,
+    cluster_column = "leiden_clus",
+    feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"),
+    feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17")
+)
 
 plotCCcomHeatmap(gobject = g, comScores = comScores, show_plot = TRUE)
 }
diff --git a/man/plotCPF.Rd b/man/plotCPF.Rd
index 1b67ffcdc..8b5829673 100644
--- a/man/plotCPF.Rd
+++ b/man/plotCPF.Rd
@@ -39,7 +39,7 @@ plotCPF(
 
 \item{min_int_cells}{minimum number of interacting neighbor cell type}
 
-\item{min_int_cells_expr}{minimum expression level for interacting neighbor 
+\item{min_int_cells_expr}{minimum expression level for interacting neighbor
 cell type}
 
 \item{min_fdr}{minimum adjusted p-value}
@@ -75,9 +75,13 @@ Create visualization for cell proximity feature scores
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
-icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus",
-selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
+icfObject <- findInteractionChangedFeats(g,
+    cluster_column = "leiden_clus",
+    selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10
+)
 
-plotCPF(gobject = g, icfObject = icfObject, show_plot = TRUE,
-save_plot = FALSE, return_plot = FALSE)
+plotCPF(
+    gobject = g, icfObject = icfObject, show_plot = TRUE,
+    save_plot = FALSE, return_plot = FALSE
+)
 }
diff --git a/man/plotCellProximityFeatSpot.Rd b/man/plotCellProximityFeatSpot.Rd
index d6a8bbb71..d1b765942 100644
--- a/man/plotCellProximityFeatSpot.Rd
+++ b/man/plotCellProximityFeatSpot.Rd
@@ -72,7 +72,9 @@ Create visualization for cell proximity feature scores
 g <- GiottoData::loadGiottoMini("visium")
 icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus")
 
-plotCellProximityFeatSpot(gobject = g, icfObject = icfObject,
-show_plot = TRUE, save_plot = FALSE, return_plot = FALSE, 
-min_pcc_diff = 0.01)
+plotCellProximityFeatSpot(
+    gobject = g, icfObject = icfObject,
+    show_plot = TRUE, save_plot = FALSE, return_plot = FALSE,
+    min_pcc_diff = 0.01
+)
 }
diff --git a/man/plotCellProximityFeats.Rd b/man/plotCellProximityFeats.Rd
index f2e0bb574..bff1fd4fc 100644
--- a/man/plotCellProximityFeats.Rd
+++ b/man/plotCellProximityFeats.Rd
@@ -39,7 +39,7 @@ plotCellProximityFeats(
 
 \item{min_int_cells}{minimum number of interacting neighbor cell type}
 
-\item{min_int_cells_expr}{minimum expression level for interacting neighbor 
+\item{min_int_cells_expr}{minimum expression level for interacting neighbor
 cell type}
 
 \item{min_fdr}{minimum adjusted p-value}
@@ -77,6 +77,8 @@ Create visualization for cell proximity feature scores
 g <- GiottoData::loadGiottoMini("visium")
 icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus")
 
-plotCellProximityFeats(gobject = g, icfObject = icfObject,
-show_plot = TRUE, save_plot = FALSE, return_plot = FALSE)
+plotCellProximityFeats(
+    gobject = g, icfObject = icfObject,
+    show_plot = TRUE, save_plot = FALSE, return_plot = FALSE
+)
 }
diff --git a/man/plotCellTypesFromEnrichment.Rd b/man/plotCellTypesFromEnrichment.Rd
index 25d8cf44d..31eaa8a8f 100644
--- a/man/plotCellTypesFromEnrichment.Rd
+++ b/man/plotCellTypesFromEnrichment.Rd
@@ -22,7 +22,7 @@ plotCellTypesFromEnrichment(
 
 \item{spat_unit}{spatial unit in which the enrichment information is stored}
 
-\item{feat_type}{feature type for which the enrichment information was 
+\item{feat_type}{feature type for which the enrichment information was
 calculated}
 
 \item{enrichment_name}{name of the spatial enrichment
@@ -52,6 +52,6 @@ plotCellTypesFromEnrichment
 This function generates a bar plot of cell types vs the frequency
 of that cell type in the data. These cell type results are
 based on the provided `enrichment_name`, and will be determined
-by the maximum value of the z-score or p-value for a given cell or 
+by the maximum value of the z-score or p-value for a given cell or
 annotation.
 }
diff --git a/man/plotCombineCCcom.Rd b/man/plotCombineCCcom.Rd
index 0c2e16932..f5c014c70 100644
--- a/man/plotCombineCCcom.Rd
+++ b/man/plotCombineCCcom.Rd
@@ -30,7 +30,7 @@ plotCombineCCcom(
 
 \item{selected_LR}{selected ligand-receptor pair}
 
-\item{selected_cell_LR}{selected cell-cell interaction pair for 
+\item{selected_cell_LR}{selected cell-cell interaction pair for
 ligand-receptor pair}
 
 \item{detail_plot}{show detailed info in both interacting cell types}
@@ -61,23 +61,31 @@ ligand-receptor pair}
 ggplot
 }
 \description{
-Create visualization for combined (pairwise) cell proximity 
+Create visualization for combined (pairwise) cell proximity
 gene scores
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-comScores <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"), 
-feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17"))
+comScores <- exprCellCellcom(g,
+    cluster_column = "leiden_clus",
+    feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"),
+    feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17")
+)
 
-exprCC <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik")
-spatialCC <- spatCellCellcom(gobject = g, cluster_column = "leiden_clus",
-feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot")
+exprCC <- exprCellCellcom(g,
+    cluster_column = "leiden_clus",
+    feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik"
+)
+spatialCC <- spatCellCellcom(
+    gobject = g, cluster_column = "leiden_clus",
+    feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot"
+)
 
 combCCcom <- combCCcom(spatialCC = spatialCC, exprCC = exprCC)
 
-plotCombineCCcom(gobject = g, combCCcom = combCCcom,
-selected_LR = c("Gm19935-9630013A20Rik"), selected_cell_LR = c("1--1"))
+plotCombineCCcom(
+    gobject = g, combCCcom = combCCcom,
+    selected_LR = c("Gm19935-9630013A20Rik"), selected_cell_LR = c("1--1")
+)
 }
diff --git a/man/plotCombineCellCellCommunication.Rd b/man/plotCombineCellCellCommunication.Rd
index 85f61c833..6527a36d0 100644
--- a/man/plotCombineCellCellCommunication.Rd
+++ b/man/plotCombineCellCellCommunication.Rd
@@ -30,7 +30,7 @@ plotCombineCellCellCommunication(
 
 \item{selected_LR}{selected ligand-receptor pair}
 
-\item{selected_cell_LR}{selected cell-cell interaction pair for 
+\item{selected_cell_LR}{selected cell-cell interaction pair for
 ligand-receptor pair}
 
 \item{detail_plot}{show detailed info in both interacting cell types}
@@ -61,23 +61,31 @@ ligand-receptor pair}
 ggplot
 }
 \description{
-Create visualization for combined (pairwise) cell proximity 
+Create visualization for combined (pairwise) cell proximity
 gene scores
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-comScores <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"), 
-feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17"))
+comScores <- exprCellCellcom(g,
+    cluster_column = "leiden_clus",
+    feat_set_1 = c("Gm19935", "2900040C04Rik", "Ccnd2"),
+    feat_set_2 = c("9630013A20Rik", "Gna12", "Btbd17")
+)
 
-exprCC <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik")
-spatialCC <- spatCellCellcom(gobject = g, cluster_column = "leiden_clus",
-feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot")
+exprCC <- exprCellCellcom(g,
+    cluster_column = "leiden_clus",
+    feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik"
+)
+spatialCC <- spatCellCellcom(
+    gobject = g, cluster_column = "leiden_clus",
+    feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot"
+)
 
 combCCcom <- combCCcom(spatialCC = spatialCC, exprCC = exprCC)
 
-plotCombineCellCellCommunication(gobject = g, combCCcom = combCCcom,
-selected_LR = c("Gm19935-9630013A20Rik"), selected_cell_LR = c("1--1"))
+plotCombineCellCellCommunication(
+    gobject = g, combCCcom = combCCcom,
+    selected_LR = c("Gm19935-9630013A20Rik"), selected_cell_LR = c("1--1")
+)
 }
diff --git a/man/plotCombineICF.Rd b/man/plotCombineICF.Rd
index bc5c19f32..4c9a94f75 100644
--- a/man/plotCombineICF.Rd
+++ b/man/plotCombineICF.Rd
@@ -65,13 +65,16 @@ Create visualization for combined (pairwise) ICF scores
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-g_icf <- findInteractionChangedFeats(g, 
-cluster_column = "leiden_clus", 
-selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
+g_icf <- findInteractionChangedFeats(g,
+    cluster_column = "leiden_clus",
+    selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10
+)
 
 combIcfObject <- combineInteractionChangedFeats(g_icf)
 
-plotCombineICF(gobject = g, combIcfObject = combIcfObject, 
-selected_feat_to_feat = c("Btbd17--Ccnd2", "Btbd17--Gna12"), 
-selected_interactions = "1--8")
+plotCombineICF(
+    gobject = g, combIcfObject = combIcfObject,
+    selected_feat_to_feat = c("Btbd17--Ccnd2", "Btbd17--Gna12"),
+    selected_interactions = "1--8"
+)
 }
diff --git a/man/plotCombineInteractionChangedFeats.Rd b/man/plotCombineInteractionChangedFeats.Rd
index 651eafea0..901b07c1d 100644
--- a/man/plotCombineInteractionChangedFeats.Rd
+++ b/man/plotCombineInteractionChangedFeats.Rd
@@ -65,14 +65,17 @@ Create visualization for combined (pairwise) ICF scores
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-g_icf <- findInteractionChangedFeats(g, 
-cluster_column = "leiden_clus", 
-selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
+g_icf <- findInteractionChangedFeats(g,
+    cluster_column = "leiden_clus",
+    selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10
+)
 
 combIcfObject <- combineInteractionChangedFeats(g_icf)
 
-plotCombineInteractionChangedFeats(gobject = g, 
-combIcfObject = combIcfObject, 
-selected_feat_to_feat = c("Btbd17--Ccnd2", "Btbd17--Gna12"), 
-selected_interactions = "1--8")
+plotCombineInteractionChangedFeats(
+    gobject = g,
+    combIcfObject = combIcfObject,
+    selected_feat_to_feat = c("Btbd17--Ccnd2", "Btbd17--Gna12"),
+    selected_interactions = "1--8"
+)
 }
diff --git a/man/plotICF.Rd b/man/plotICF.Rd
index a81872f3c..c67c3628e 100644
--- a/man/plotICF.Rd
+++ b/man/plotICF.Rd
@@ -50,10 +50,14 @@ Create barplot to visualize interaction changed features
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
-icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus",
-selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
+icfObject <- findInteractionChangedFeats(g,
+    cluster_column = "leiden_clus",
+    selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10
+)
 
-plotICF(gobject = g, icfObject = icfObject,
-source_type = "1", source_markers = "Ccnd2", 
-ICF_feats = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17"))
+plotICF(
+    gobject = g, icfObject = icfObject,
+    source_type = "1", source_markers = "Ccnd2",
+    ICF_feats = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17")
+)
 }
diff --git a/man/plotICFSpot.Rd b/man/plotICFSpot.Rd
index 1fe50620a..7e5fca157 100644
--- a/man/plotICFSpot.Rd
+++ b/man/plotICFSpot.Rd
@@ -49,10 +49,14 @@ Create barplot to visualize interaction changed features
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
-icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus",
-selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
+icfObject <- findInteractionChangedFeats(g,
+    cluster_column = "leiden_clus",
+    selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10
+)
 
-plotICFSpot(gobject = g, icfObject = icfObject,
-source_type = "1", source_markers = "Ccnd2", 
-ICF_features = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17"))
+plotICFSpot(
+    gobject = g, icfObject = icfObject,
+    source_type = "1", source_markers = "Ccnd2",
+    ICF_features = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17")
+)
 }
diff --git a/man/plotInteractionChangedFeats.Rd b/man/plotInteractionChangedFeats.Rd
index 8fe37185d..7395258f9 100644
--- a/man/plotInteractionChangedFeats.Rd
+++ b/man/plotInteractionChangedFeats.Rd
@@ -50,10 +50,14 @@ Create barplot to visualize interaction changed features
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
-icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus",
-selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
+icfObject <- findInteractionChangedFeats(g,
+    cluster_column = "leiden_clus",
+    selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10
+)
 
-plotInteractionChangedFeats(gobject = g, icfObject = icfObject,
-source_type = "1", source_markers = "Ccnd2", 
-ICF_feats = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17"))
+plotInteractionChangedFeats(
+    gobject = g, icfObject = icfObject,
+    source_type = "1", source_markers = "Ccnd2",
+    ICF_feats = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17")
+)
 }
diff --git a/man/plotInteractive3D.Rd b/man/plotInteractive3D.Rd
index d53cc4773..c4e8cb3d0 100644
--- a/man/plotInteractive3D.Rd
+++ b/man/plotInteractive3D.Rd
@@ -22,10 +22,10 @@ plotInteractive3D(
 
 \item{feat_type}{feature type (e.g. "rna", "dna", "protein")}
 
-\item{cell_color}{character. What to color cells by 
+\item{cell_color}{character. What to color cells by
 (e.g. metadata col or spatial enrichment col)}
 
-\item{cell_color_code}{character. discrete colors to use. Palette to use or 
+\item{cell_color_code}{character. discrete colors to use. Palette to use or
 named vector of colors}
 
 \item{point_size}{size of point (cell)}
@@ -35,7 +35,7 @@ named vector of colors}
 \item{height}{plot height}
 }
 \value{
-data.table with selected cell_IDs, spatial coordinates, and 
+data.table with selected cell_IDs, spatial coordinates, and
 cluster_ID.
 }
 \description{
diff --git a/man/plotInteractivePolygons.Rd b/man/plotInteractivePolygons.Rd
index b3a2d5726..e5411ca9b 100644
--- a/man/plotInteractivePolygons.Rd
+++ b/man/plotInteractivePolygons.Rd
@@ -17,6 +17,6 @@ plotInteractivePolygons(x, width = "auto", height = "auto", ...)
 A `data.table` containing x,y coordinates from the plotted polygons.
 }
 \description{
-Plot interactive polygons on an image and retrieve the polygons 
+Plot interactive polygons on an image and retrieve the polygons
 coordinates.
 }
diff --git a/man/plotPolygons.Rd b/man/plotPolygons.Rd
index aaf26065d..11454f434 100644
--- a/man/plotPolygons.Rd
+++ b/man/plotPolygons.Rd
@@ -22,10 +22,10 @@ plotPolygons(
 
 \item{spat_unit}{spatial unit}
 
-\item{polygons}{character. Vector of polygon names to plot. If NULL, all 
+\item{polygons}{character. Vector of polygon names to plot. If NULL, all
 polygons are plotted}
 
-\item{...}{Additional parameters passed to ggplot2::geom_polygon() or 
+\item{...}{Additional parameters passed to ggplot2::geom_polygon() or
 graphics::polygon}
 }
 \value{
@@ -37,12 +37,15 @@ Plot stored polygons
 \examples{
 ## Plot interactive polygons
 g <- GiottoData::loadGiottoMini("visium")
-my_polygon_coords <- data.frame(poly_ID = rep("polygon1", 3), 
-sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202))
+my_polygon_coords <- data.frame(
+    poly_ID = rep("polygon1", 3),
+    sdimx = c(5477, 5959, 4720), sdimy = c(-4125, -2808, -5202)
+)
 
 ## Add polygon coordinates to Giotto object
-my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords, 
-name = "selections")
+my_giotto_polygons <- createGiottoPolygonsFromDfr(my_polygon_coords,
+    name = "selections"
+)
 g <- addGiottoPolygons(
     gobject = g,
     gpolygons = list(my_giotto_polygons)
diff --git a/man/plotRankSpatvsExpr.Rd b/man/plotRankSpatvsExpr.Rd
index 0ebfbc8f8..fa91fe188 100644
--- a/man/plotRankSpatvsExpr.Rd
+++ b/man/plotRankSpatvsExpr.Rd
@@ -49,7 +49,7 @@ or 'sequential' (scaled based on data range)}
 
 \item{ylims}{y-limits, numerical vector of 2}
 
-\item{selected_ranks}{numerical vector, will be used to print out the 
+\item{selected_ranks}{numerical vector, will be used to print out the
 percentage of top spatial ranks are recovered}
 
 \item{show_plot}{logical. show plot}
@@ -66,17 +66,21 @@ percentage of top spatial ranks are recovered}
 ggplot
 }
 \description{
-Plots dotplot to compare ligand-receptor rankings from 
+Plots dotplot to compare ligand-receptor rankings from
 spatial and expression information
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-exprCC <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik")
-spatialCC <- spatCellCellcom(gobject = g, cluster_column = "leiden_clus",
-feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot",
-random_iter = 10)
+exprCC <- exprCellCellcom(g,
+    cluster_column = "leiden_clus",
+    feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik"
+)
+spatialCC <- spatCellCellcom(
+    gobject = g, cluster_column = "leiden_clus",
+    feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot",
+    random_iter = 10
+)
 
 combCC <- combCCcom(spatialCC = spatialCC, exprCC = exprCC)
 
diff --git a/man/plotRecovery.Rd b/man/plotRecovery.Rd
index 59c47fea0..4cba6b66b 100644
--- a/man/plotRecovery.Rd
+++ b/man/plotRecovery.Rd
@@ -42,17 +42,21 @@ plotRecovery(
 ggplot
 }
 \description{
-Plots recovery plot to compare ligand-receptor rankings from 
+Plots recovery plot to compare ligand-receptor rankings from
 spatial and expression information
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-exprCC <- exprCellCellcom(g, cluster_column = "leiden_clus", 
-feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik")
-spatialCC <- spatCellCellcom(gobject = g, cluster_column = "leiden_clus",
-feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot",
-random_iter = 10)
+exprCC <- exprCellCellcom(g,
+    cluster_column = "leiden_clus",
+    feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik"
+)
+spatialCC <- spatCellCellcom(
+    gobject = g, cluster_column = "leiden_clus",
+    feat_set_1 = "Gm19935", feat_set_2 = "9630013A20Rik", verbose = "a lot",
+    random_iter = 10
+)
 
 combCC <- combCCcom(spatialCC = spatialCC, exprCC = exprCC)
 
diff --git a/man/print.combIcfObject.Rd b/man/print.combIcfObject.Rd
new file mode 100644
index 000000000..027c1f64e
--- /dev/null
+++ b/man/print.combIcfObject.Rd
@@ -0,0 +1,17 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/spatial_interaction.R
+\name{print.combIcfObject}
+\alias{print.combIcfObject}
+\title{combIcfObject print method}
+\usage{
+\method{print}{combIcfObject}(x, ...)
+}
+\arguments{
+\item{x}{object to print}
+
+\item{\dots}{additional params to pass (none implemented)}
+}
+\description{
+combIcfObject print method
+}
+\keyword{internal}
diff --git a/man/print.icfObject.Rd b/man/print.icfObject.Rd
new file mode 100644
index 000000000..058a52704
--- /dev/null
+++ b/man/print.icfObject.Rd
@@ -0,0 +1,17 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/spatial_interaction.R
+\name{print.icfObject}
+\alias{print.icfObject}
+\title{icfObject print method}
+\usage{
+\method{print}{icfObject}(x, ...)
+}
+\arguments{
+\item{x}{object to print}
+
+\item{\dots}{additional params to pass (none implemented)}
+}
+\description{
+icfObject print method
+}
+\keyword{internal}
diff --git a/man/processGiotto.Rd b/man/processGiotto.Rd
index d1cbee687..4aebcb3e5 100644
--- a/man/processGiotto.Rd
+++ b/man/processGiotto.Rd
@@ -43,6 +43,8 @@ adjust_params must be set to NULL
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-processGiotto(gobject = g,
-adjust_params = list(covariate_columns = "leiden_clus"))
+processGiotto(
+    gobject = g,
+    adjust_params = list(covariate_columns = "leiden_clus")
+)
 }
diff --git a/man/rankSpatialCorGroups.Rd b/man/rankSpatialCorGroups.Rd
index 67ca19916..1cb000fe6 100644
--- a/man/rankSpatialCorGroups.Rd
+++ b/man/rankSpatialCorGroups.Rd
@@ -49,6 +49,8 @@ g <- GiottoData::loadGiottoMini("visium")
 spatCorObject <- detectSpatialCorFeats(g, method = "network")
 clusters <- clusterSpatialCorFeats(spatCorObject = spatCorObject)
 
-rankSpatialCorGroups(gobject = g, spatCorObject = clusters,
-use_clus_name = "spat_clus")
+rankSpatialCorGroups(
+    gobject = g, spatCorObject = clusters,
+    use_clus_name = "spat_clus"
+)
 }
diff --git a/man/readPolygonFilesVizgen.Rd b/man/readPolygonFilesVizgen.Rd
index 228b6de32..24e856da0 100644
--- a/man/readPolygonFilesVizgen.Rd
+++ b/man/readPolygonFilesVizgen.Rd
@@ -58,7 +58,7 @@ object and add the smoothed polygons to the object
 }
 \section{Functions}{
 \itemize{
-\item \code{.h5_read_vizgen()}: (internal) Optimized .hdf5 reading for 
+\item \code{.h5_read_vizgen()}: (internal) Optimized .hdf5 reading for
 vizgen merscope output. Returns a data.table of xyz coords and cell_id
 
 }}
diff --git a/man/readPolygonFilesVizgenHDF5.Rd b/man/readPolygonFilesVizgenHDF5.Rd
index 4e60c5839..58417691e 100644
--- a/man/readPolygonFilesVizgenHDF5.Rd
+++ b/man/readPolygonFilesVizgenHDF5.Rd
@@ -35,7 +35,7 @@ readPolygonFilesVizgenHDF5(
 \item{segm_to_use}{segmentation results to use (usually = 1. Depends on if
 alternative segmentations were generated)}
 
-\item{custom_polygon_names}{a character vector to provide custom polygon 
+\item{custom_polygon_names}{a character vector to provide custom polygon
 names (optional)}
 
 \item{flip_x_axis}{flip x axis of polygon coordinates (multiply by -1)}
@@ -54,11 +54,11 @@ names (optional)}
 
 \item{cores}{cores to use}
 
-\item{create_gpoly_parallel}{(default = TRUE) Whether to run gpoly creation 
+\item{create_gpoly_parallel}{(default = TRUE) Whether to run gpoly creation
 in parallel}
 
 \item{create_gpoly_bin}{(Optional, default = FALSE) Parallelization option.
-Accepts integer values as an binning size when generating giottoPolygon 
+Accepts integer values as an binning size when generating giottoPolygon
 objects}
 
 \item{verbose}{be verbose}
@@ -72,11 +72,11 @@ list of giottoPolygon or data.table
 }
 \description{
 Read polygon info for all cells or for only selected FOVs from
-Vizgen HDF5 files. Data is returned as a list of giottoPolygons or 
+Vizgen HDF5 files. Data is returned as a list of giottoPolygons or
 data.tables of the requested z indices.
 }
 \details{
-Set H5Fopen_flags to "H5F_ACC_RDONLY" if you encounter permission 
+Set H5Fopen_flags to "H5F_ACC_RDONLY" if you encounter permission
 issues.
 }
 \seealso{
diff --git a/man/readPolygonFilesVizgenHDF5_old.Rd b/man/readPolygonFilesVizgenHDF5_old.Rd
index f4faef7a1..1cdf2a8c8 100644
--- a/man/readPolygonFilesVizgenHDF5_old.Rd
+++ b/man/readPolygonFilesVizgenHDF5_old.Rd
@@ -26,7 +26,7 @@ readPolygonFilesVizgenHDF5_old(
 
 \item{polygon_feat_types}{a vector containing the polygon feature types}
 
-\item{custom_polygon_names}{a character vector to provide custom polygon 
+\item{custom_polygon_names}{a character vector to provide custom polygon
 names (optional)}
 
 \item{flip_x_axis}{flip x axis of polygon coordinates (multiply by -1)}
@@ -49,11 +49,11 @@ names (optional)}
 data.table
 }
 \description{
-Read and create polygons for all cells, or for only selected 
+Read and create polygons for all cells, or for only selected
 FOVs.
 }
 \details{
-Set H5Fopen_flags to "H5F_ACC_RDONLY" if you encounter permission 
+Set H5Fopen_flags to "H5F_ACC_RDONLY" if you encounter permission
 issues.
 }
 \seealso{
diff --git a/man/readPolygonVizgenParquet.Rd b/man/readPolygonVizgenParquet.Rd
index a90faf9a1..3ee5f1a73 100644
--- a/man/readPolygonVizgenParquet.Rd
+++ b/man/readPolygonVizgenParquet.Rd
@@ -14,7 +14,7 @@ readPolygonVizgenParquet(
 \arguments{
 \item{file}{parquet file to load}
 
-\item{z_index}{either 'all' or a numeric vector of z_indices to get polygons 
+\item{z_index}{either 'all' or a numeric vector of z_indices to get polygons
 for}
 
 \item{calc_centroids}{calculate centroids for the polygons (default = TRUE)}
@@ -25,6 +25,6 @@ for}
 giottoPolygons
 }
 \description{
-Read Vizgen exported cell boundary parquet files as giottoPolyons. The z 
+Read Vizgen exported cell boundary parquet files as giottoPolyons. The z
 level can be selected.
 }
diff --git a/man/read_crossSection.Rd b/man/read_crossSection.Rd
index 518701bd0..77e30dcc5 100644
--- a/man/read_crossSection.Rd
+++ b/man/read_crossSection.Rd
@@ -4,11 +4,18 @@
 \alias{read_crossSection}
 \title{read_crossSection}
 \usage{
-read_crossSection(gobject, name = NULL, spatial_network_name = NULL)
+read_crossSection(
+  gobject,
+  spat_unit = NULL,
+  name = NULL,
+  spatial_network_name = NULL
+)
 }
 \arguments{
 \item{gobject}{gobject}
 
+\item{spat_unit}{spatial unit}
+
 \item{name}{name}
 
 \item{spatial_network_name}{spatial_network_name}
diff --git a/man/registerGiottoObjectList.Rd b/man/registerGiottoObjectList.Rd
index 7cfc09b09..46eeeab63 100644
--- a/man/registerGiottoObjectList.Rd
+++ b/man/registerGiottoObjectList.Rd
@@ -26,41 +26,41 @@ registerGiottoObjectList(
 
 \item{spat_unit}{spatial unit}
 
-\item{method}{Method used to align gobjects. Current options are either 
+\item{method}{Method used to align gobjects. Current options are either
 using FIJI register_virtual_stack_slices output or rvision}
 
 \item{image_unreg}{Gobject image slot to use. Defaults to 'image' (optional)}
 
-\item{image_reg_name}{Arbitrary image slot name for registered images to 
+\item{image_reg_name}{Arbitrary image slot name for registered images to
 occupy. Defaults to replacement of 'image' slot (optional)}
 
 \item{image_list}{RVISION - under construction}
 
 \item{save_dir}{RVISION - under construction}
 
-\item{spatloc_unreg}{Unregistered spatial locations to align. Defaults to 
+\item{spatloc_unreg}{Unregistered spatial locations to align. Defaults to
 'raw' slot (optional)}
 
-\item{spatloc_reg_name}{Arbitrary name for registered spatial locations. 
+\item{spatloc_reg_name}{Arbitrary name for registered spatial locations.
 Defaults to replacement of 'raw' slot (optional)}
 
 \item{fiji_xml_files}{Filepaths to FIJI registration XML outputs}
 
-\item{fiji_registered_images}{Registered images output by FIJI 
+\item{fiji_registered_images}{Registered images output by FIJI
 register_virtual_stack_slices}
 
 \item{scale_factor}{Scaling to be applied to spatial coordinates}
 
-\item{allow_rvision_autoscale}{Whether or not to allow rvision to 
+\item{allow_rvision_autoscale}{Whether or not to allow rvision to
 automatically scale the images when performing image registration}
 
 \item{verbose}{Be verbose}
 }
 \value{
-List of registered giotto objects where the registered images and 
+List of registered giotto objects where the registered images and
 spatial locations
 }
 \description{
-Wrapper function for registerGiottoObjectListFiji and 
+Wrapper function for registerGiottoObjectListFiji and
 registerGiottoObjectListRvision
 }
diff --git a/man/registerGiottoObjectListFiji.Rd b/man/registerGiottoObjectListFiji.Rd
index 456e3febd..3b7cf7ee6 100644
--- a/man/registerGiottoObjectListFiji.Rd
+++ b/man/registerGiottoObjectListFiji.Rd
@@ -24,39 +24,39 @@ registerGiottoObjectListFiji(
 
 \item{spat_unit}{spatial unit}
 
-\item{image_unreg}{name of original unregistered images. Defaults to 
+\item{image_unreg}{name of original unregistered images. Defaults to
 'image' (optional)}
 
-\item{image_reg_name}{arbitrary name for registered images to occupy. 
+\item{image_reg_name}{arbitrary name for registered images to occupy.
 Defaults to replacement of 'image' (optional)}
 
-\item{image_replace_name}{arbitrary name for any images replaced due to 
+\item{image_replace_name}{arbitrary name for any images replaced due to
 image_reg_name argument (optional)}
 
-\item{registered_images}{registered images output by FIJI 
+\item{registered_images}{registered images output by FIJI
 register_virtual_stack_slices}
 
 \item{spatloc_unreg}{spatial locations to use. Defaults to 'raw' (optional)}
 
-\item{spatloc_reg_name}{name for registered spatial locations. Defaults to 
+\item{spatloc_reg_name}{name for registered spatial locations. Defaults to
 replacement of 'raw' (optional)}
 
-\item{spatloc_replace_name}{arbitrary name for any spatial locations 
+\item{spatloc_replace_name}{arbitrary name for any spatial locations
 replaced due to spatloc_reg_name argument (optional)}
 
-\item{xml_files}{atomic vector of filepaths to xml outputs from FIJI 
+\item{xml_files}{atomic vector of filepaths to xml outputs from FIJI
 register_virtual_stack_slices}
 
-\item{scale_factor}{vector of scaling factors of images used in registration 
+\item{scale_factor}{vector of scaling factors of images used in registration
 vs spatlocs}
 
 \item{verbose}{be verbose}
 }
 \value{
-list of registered giotto objects where the registered images and 
+list of registered giotto objects where the registered images and
 spatial locations
 }
 \description{
-Function to spatially align gobject data based on FIJI image 
+Function to spatially align gobject data based on FIJI image
 registration.
 }
diff --git a/man/registerGiottoObjectListRvision.Rd b/man/registerGiottoObjectListRvision.Rd
index ba85763e2..3b30843d3 100644
--- a/man/registerGiottoObjectListRvision.Rd
+++ b/man/registerGiottoObjectListRvision.Rd
@@ -22,16 +22,16 @@ registerGiottoObjectListRvision(
 
 \item{spatloc_unreg}{spatial locations to use}
 
-\item{spatloc_reg_name}{name for registered spatial locations to. Defaults 
+\item{spatloc_reg_name}{name for registered spatial locations to. Defaults
 to replacement of spat_unreg (optional)}
 
 \item{verbose}{be verbose}
 }
 \value{
-list of registered giotto objects where the registered images and 
+list of registered giotto objects where the registered images and
 spatial locations
 }
 \description{
-Function to spatially align gobject data based on Rvision image 
+Function to spatially align gobject data based on Rvision image
 registration.
 }
diff --git a/man/registerImagesFIJI.Rd b/man/registerImagesFIJI.Rd
index d15bd8464..49326fc32 100644
--- a/man/registerImagesFIJI.Rd
+++ b/man/registerImagesFIJI.Rd
@@ -37,10 +37,10 @@ registerImagesFIJI(
 
 \item{output_img_dir}{Folder to save registered images to}
 
-\item{transforms_save_dir}{(jython implementation only) Folder to save 
+\item{transforms_save_dir}{(jython implementation only) Folder to save
 transforms to}
 
-\item{ref_img_name}{(jython implementation only) File name of reference 
+\item{ref_img_name}{(jython implementation only) File name of reference
 image for the registration}
 
 \item{init_gauss_blur}{Point detector option: initial image blurring}
@@ -86,14 +86,14 @@ image for the registration}
 executing it.}
 }
 \value{
-list of registered giotto objects where the registered images and 
+list of registered giotto objects where the registered images and
 spatial locations
 }
 \description{
-Wrapper function for Register Virtual Stack Slices plugin in 
+Wrapper function for Register Virtual Stack Slices plugin in
 FIJI
 }
 \details{
-This function was adapted from runFijiMacro function in 
+This function was adapted from runFijiMacro function in
 jimpipeline by jefferislab
 }
diff --git a/man/runDWLSDeconv.Rd b/man/runDWLSDeconv.Rd
index 3530492e1..fa8670b4f 100644
--- a/man/runDWLSDeconv.Rd
+++ b/man/runDWLSDeconv.Rd
@@ -51,13 +51,15 @@ expression data
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 x <- findMarkers_one_vs_all(g,
-cluster_column = "leiden_clus", min_feats = 20)
+    cluster_column = "leiden_clus", min_feats = 20
+)
 sign_gene <- x$feats
 
-sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10),
-nrow = length(sign_gene))
+sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+    nrow = length(sign_gene)
+)
 rownames(sign_matrix) <- sign_gene
-colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
+colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
 
 runDWLSDeconv(gobject = g, sign_matrix = sign_matrix)
 }
diff --git a/man/runGiottoHarmony.Rd b/man/runGiottoHarmony.Rd
index 274b3bb23..35348339b 100644
--- a/man/runGiottoHarmony.Rd
+++ b/man/runGiottoHarmony.Rd
@@ -70,7 +70,7 @@ giotto object with updated Harmony dimension reduction
 run UMAP
 }
 \details{
-This is a simple wrapper for the HarmonyMatrix function in the 
+This is a simple wrapper for the HarmonyMatrix function in the
 Harmony package \doi{10.1038/s41592-019-0619-0}.
 }
 \examples{
diff --git a/man/runHyperGeometricEnrich.Rd b/man/runHyperGeometricEnrich.Rd
index f04ee5273..ee0a43465 100644
--- a/man/runHyperGeometricEnrich.Rd
+++ b/man/runHyperGeometricEnrich.Rd
@@ -59,13 +59,15 @@ hypergeometric test, -log10(p-value).
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 x <- findMarkers_one_vs_all(g,
-cluster_column = "leiden_clus", min_feats = 20)
+    cluster_column = "leiden_clus", min_feats = 20
+)
 sign_gene <- x$feats
 
-sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10),
-nrow = length(sign_gene))
+sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+    nrow = length(sign_gene)
+)
 rownames(sign_matrix) <- sign_gene
-colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
+colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
 
 runHyperGeometricEnrich(gobject = g, sign_matrix = sign_matrix)
 }
diff --git a/man/runPAGEEnrich.Rd b/man/runPAGEEnrich.Rd
index d81c5134c..8d1ec1818 100644
--- a/man/runPAGEEnrich.Rd
+++ b/man/runPAGEEnrich.Rd
@@ -80,13 +80,16 @@ gene set.
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
-sign_gene <- c("Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap", 
-"Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2", 
-"Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5", 
-"Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b","Ipcef1")
+sign_gene <- c(
+    "Bcl11b", "Lmo1", "F3", "Cnih3", "Ppp1r3c", "Rims2", "Gfap",
+    "Gjc3", "Chrna4", "Prkcd", "Prr18", "Grb14", "Tprn", "Clic1", "Olig2",
+    "Hrh3", "Tmbim1", "Carhsp1", "Tmem88b", "Ugt8a", "Arpp19", "Lamp5",
+    "Galnt6", "Hlf", "Hs3st2", "Tbr1", "Myl4", "Cygb", "Ttc9b", "Ipcef1"
+)
 
-sign_matrix <- matrix(rnorm(length(sign_gene)*3, mean  = 10), 
-nrow = length(sign_gene))
+sign_matrix <- matrix(rnorm(length(sign_gene) * 3, mean = 10),
+    nrow = length(sign_gene)
+)
 rownames(sign_matrix) <- sign_gene
 colnames(sign_matrix) <- c("cell_type1", "cell_type2", "cell_type3")
 
diff --git a/man/runPCA.Rd b/man/runPCA.Rd
index 58073b9d0..2e3ca25e1 100644
--- a/man/runPCA.Rd
+++ b/man/runPCA.Rd
@@ -78,7 +78,7 @@ dimension reduction and clusterings are based on your features of interest.
   \item feats_to_use = NULL: will use all features from the selected matrix
   \item feats_to_use = <hvg name>: can be used to select a column name of
   highly variable features, created by (see \code{\link{calculateHVF}})
-  \item feats_to_use = c('geneA', 'geneB', ...): will use all manually 
+  \item feats_to_use = c('geneA', 'geneB', ...): will use all manually
   provided features
 }
 By default the number of principle components that we calculate is 100, which
diff --git a/man/runPCAprojection.Rd b/man/runPCAprojection.Rd
index 31d0251f6..1492e60ad 100644
--- a/man/runPCAprojection.Rd
+++ b/man/runPCAprojection.Rd
@@ -69,11 +69,11 @@ runPCAprojection(
 giotto object with updated PCA dimension recuction
 }
 \description{
-runs a Principal Component Analysis on a random 
+runs a Principal Component Analysis on a random
 subset + projection
 }
 \details{
-See \code{\link[BiocSingular]{runPCA}} and 
+See \code{\link[BiocSingular]{runPCA}} and
 \code{\link[FactoMineR]{PCA}} for more information about other parameters.
 This PCA implementation is similar to  \code{\link{runPCA}}, except that it
 performs PCA on a subset of the cells or features, and predict on the others.
@@ -82,7 +82,7 @@ This can significantly increase speed without sacrificing accuracy too much.
   \item feats_to_use = NULL: will use all features from the selected matrix
   \item feats_to_use = <hvg name>: can be used to select a column name of
   highly variable features, created by (see \code{\link{calculateHVF}})
-  \item feats_to_use = c('geneA', 'geneB', ...): will use all manually 
+  \item feats_to_use = c('geneA', 'geneB', ...): will use all manually
   provided features
 }
 }
diff --git a/man/runPCAprojectionBatch.Rd b/man/runPCAprojectionBatch.Rd
index 926001375..518ff46c0 100644
--- a/man/runPCAprojectionBatch.Rd
+++ b/man/runPCAprojectionBatch.Rd
@@ -72,28 +72,30 @@ runPCAprojectionBatch(
 giotto object with updated PCA dimension reduction
 }
 \description{
-runs a Principal Component Analysis on multiple random 
+runs a Principal Component Analysis on multiple random
 batches + projection
 }
 \details{
-See \code{\link[BiocSingular]{runPCA}} and 
+See \code{\link[BiocSingular]{runPCA}} and
 \code{\link[FactoMineR]{PCA}} for more information about other parameters.
-This PCA implementation is similar to  \code{\link{runPCA}} and  
+This PCA implementation is similar to  \code{\link{runPCA}} and
 \code{\link{runPCAprojection}},
-except that it performs PCA on multiple subsets (batches) of the cells or 
+except that it performs PCA on multiple subsets (batches) of the cells or
 features,
-and predict on the others. This can significantly increase speed without 
+and predict on the others. This can significantly increase speed without
 sacrificing accuracy too much.
 \itemize{
   \item feats_to_use = NULL: will use all features from the selected matrix
   \item feats_to_use = <hvg name>: can be used to select a column name of
   highly variable features, created by (see \code{\link{calculateHVF}})
-  \item feats_to_use = c('geneA', 'geneB', ...): will use all manually 
+  \item feats_to_use = c('geneA', 'geneB', ...): will use all manually
   provided features
 }
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-runPCAprojectionBatch(g)
+# set feats_to_use to NULL since there are not many hvfs
+# (only 48 in this mini dataset)
+runPCAprojectionBatch(g, feats_to_use = NULL)
 }
diff --git a/man/runPatternSimulation.Rd b/man/runPatternSimulation.Rd
index 7a63a7ddd..c06ba28da 100644
--- a/man/runPatternSimulation.Rd
+++ b/man/runPatternSimulation.Rd
@@ -86,7 +86,11 @@ and runs the different spatial gene detection tests
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-runPatternSimulation(gobject = g, pattern_cell_ids = c("AAAGGGATGTAGCAAG-1",
-"TCAAACAACCGCGTCG-1", "ACGATCATACATAGAG-1", "TATGCTCCCTACTTAC-1"),
-spatial_network_name = "spatial_network", gene_names = c("Gna12", "Ccnd2"))
+runPatternSimulation(
+    gobject = g, pattern_cell_ids = c(
+        "AAAGGGATGTAGCAAG-1",
+        "TCAAACAACCGCGTCG-1", "ACGATCATACATAGAG-1", "TATGCTCCCTACTTAC-1"
+    ),
+    spatial_network_name = "spatial_network", gene_names = c("Gna12", "Ccnd2")
+)
 }
diff --git a/man/runRankEnrich.Rd b/man/runRankEnrich.Rd
index efe7c02a8..4c1059e33 100644
--- a/man/runRankEnrich.Rd
+++ b/man/runRankEnrich.Rd
@@ -75,17 +75,21 @@ and the final enrichment score is then calculated as the sum of top 100 RBPs.
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
-x <- findMarkers_one_vs_all(g, 
-cluster_column = "leiden_clus", min_feats = 20)
+x <- findMarkers_one_vs_all(g,
+    cluster_column = "leiden_clus", min_feats = 20
+)
 sign_gene <- x$feats
 
-sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10), 
-nrow = length(sign_gene))
+sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+    nrow = length(sign_gene)
+)
 rownames(sign_matrix) <- sign_gene
-colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
+colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
 
-runRankEnrich(gobject = g, sign_matrix = sign_matrix, 
-expression_values = "normalized")
+runRankEnrich(
+    gobject = g, sign_matrix = sign_matrix,
+    expression_values = "normalized"
+)
 }
 \seealso{
 \code{\link{makeSignMatrixRank}}
diff --git a/man/runSpatialDeconv.Rd b/man/runSpatialDeconv.Rd
index 9b62f08d4..65cb4e709 100644
--- a/man/runSpatialDeconv.Rd
+++ b/man/runSpatialDeconv.Rd
@@ -53,14 +53,16 @@ expression data
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
-x <- findMarkers_one_vs_all(g, 
-cluster_column = "leiden_clus", min_feats = 20)
+x <- findMarkers_one_vs_all(g,
+    cluster_column = "leiden_clus", min_feats = 20
+)
 sign_gene <- x$feats
 
-sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10), 
-nrow = length(sign_gene))
+sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+    nrow = length(sign_gene)
+)
 rownames(sign_matrix) <- sign_gene
-colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
+colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
 
 runSpatialDeconv(gobject = g, sign_matrix = sign_matrix)
 }
diff --git a/man/runSpatialEnrich.Rd b/man/runSpatialEnrich.Rd
index 9946d4c02..4f1924cab 100644
--- a/man/runSpatialEnrich.Rd
+++ b/man/runSpatialEnrich.Rd
@@ -85,14 +85,16 @@ For details see the individual functions:
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
-x <- findMarkers_one_vs_all(g, 
-cluster_column = "leiden_clus", min_feats = 20)
+x <- findMarkers_one_vs_all(g,
+    cluster_column = "leiden_clus", min_feats = 20
+)
 sign_gene <- x$feats
 
-sign_matrix <- matrix(rnorm(length(sign_gene)*8, mean  = 10), 
-nrow = length(sign_gene))
+sign_matrix <- matrix(rnorm(length(sign_gene) * 8, mean = 10),
+    nrow = length(sign_gene)
+)
 rownames(sign_matrix) <- sign_gene
-colnames(sign_matrix) <- paste0("celltype_",unique(x$cluster))
+colnames(sign_matrix) <- paste0("celltype_", unique(x$cluster))
 
 runSpatialEnrich(gobject = g, sign_matrix = sign_matrix)
 }
diff --git a/man/runUMAPprojection.Rd b/man/runUMAPprojection.Rd
index ad99c7cda..4bc1e2abd 100644
--- a/man/runUMAPprojection.Rd
+++ b/man/runUMAPprojection.Rd
@@ -84,7 +84,7 @@ giotto object with updated UMAP dimension reduction
 run UMAP on subset and project on the rest
 }
 \details{
-See \code{\link[uwot]{umap}} for more information about these and 
+See \code{\link[uwot]{umap}} for more information about these and
 other parameters.
 \itemize{
   \item Input for UMAP dimension reduction can be another dimension reduction (default = 'pca')
diff --git a/man/runWNN.Rd b/man/runWNN.Rd
index 4a542f892..f58829f3f 100644
--- a/man/runWNN.Rd
+++ b/man/runWNN.Rd
@@ -45,8 +45,8 @@ runWNN(
 \item{verbose}{be verbose}
 }
 \value{
-A Giotto object with integrated UMAP (integrated.umap) within the 
-dimension_reduction slot and Leiden clusters (integrated_leiden_clus) in the 
+A Giotto object with integrated UMAP (integrated.umap) within the
+dimension_reduction slot and Leiden clusters (integrated_leiden_clus) in the
 cellular metadata.
 }
 \description{
diff --git a/man/runtSNE.Rd b/man/runtSNE.Rd
index ac280eba2..ff8cddfee 100644
--- a/man/runtSNE.Rd
+++ b/man/runtSNE.Rd
@@ -72,7 +72,7 @@ giotto object with updated tSNE dimension recuction
 run tSNE
 }
 \details{
-See \code{\link[Rtsne]{Rtsne}} for more information about these and 
+See \code{\link[Rtsne]{Rtsne}} for more information about these and
 other parameters. \cr
 \itemize{
   \item Input for tSNE dimension reduction can be another dimension reduction (default = 'pca')
diff --git a/man/sampling_sp_genes.Rd b/man/sampling_sp_genes.Rd
index 6028564bf..8832de69e 100644
--- a/man/sampling_sp_genes.Rd
+++ b/man/sampling_sp_genes.Rd
@@ -22,11 +22,11 @@ list
 function to select a set of spatial genes
 }
 \details{
-This function samples a subset of spatial genes among different clusters, 
+This function samples a subset of spatial genes among different clusters,
 with size n = target.
-Number of samples from each cluster denpends on the relative proportion of 
+Number of samples from each cluster denpends on the relative proportion of
 each cluster.
-Changing from equal size by setting sample_rate = 1 to with exact proportion 
+Changing from equal size by setting sample_rate = 1 to with exact proportion
 of each cluster by setting sample_rate = +Inf
 }
 \keyword{internal}
diff --git a/man/screePlot.Rd b/man/screePlot.Rd
index d4f0a542f..2f65fb4f9 100644
--- a/man/screePlot.Rd
+++ b/man/screePlot.Rd
@@ -72,14 +72,14 @@ screePlot(
 ggplot object for scree method
 }
 \description{
-identify significant principal components (PCs) using an 
+identify significant principal components (PCs) using an
 screeplot (a.k.a. elbowplot)
 }
 \details{
 Screeplot works by plotting the explained variance of each
- individual PC in a barplot allowing you to identify which PC provides a 
+ individual PC in a barplot allowing you to identify which PC provides a
  significant contribution (a.k.a 'elbow method'). \cr
- Screeplot will use an available pca object, based on the parameter 'name', 
+ Screeplot will use an available pca object, based on the parameter 'name',
  or it will create it if it's not available (see \code{\link{runPCA}})
 }
 \examples{
diff --git a/man/showCellProportionSwitchedPie.Rd b/man/showCellProportionSwitchedPie.Rd
index 723b80486..e79ddc0b4 100644
--- a/man/showCellProportionSwitchedPie.Rd
+++ b/man/showCellProportionSwitchedPie.Rd
@@ -24,8 +24,8 @@ ggplot
 showCellProportionSwitchedPie
 }
 \details{
-Creates a pie chart showing how many cells switched clusters after 
+Creates a pie chart showing how many cells switched clusters after
 annotation resizing.
-The function showPolygonSizeInfluence() must have been run on the Giotto 
+The function showPolygonSizeInfluence() must have been run on the Giotto
 Object for this function to run.
 }
diff --git a/man/showCellProportionSwitchedSanKey.Rd b/man/showCellProportionSwitchedSanKey.Rd
index 69e5fa050..c350901d1 100644
--- a/man/showCellProportionSwitchedSanKey.Rd
+++ b/man/showCellProportionSwitchedSanKey.Rd
@@ -12,12 +12,12 @@ showCellProportionSwitchedSanKey(
 )
 }
 \arguments{
-\item{gobject}{giotto object which contains metadata for both spat_unit and 
+\item{gobject}{giotto object which contains metadata for both spat_unit and
 alt_spat_unit}
 
 \item{spat_unit}{spatial unit}
 
-\item{alt_spat_unit}{alternative spatial unit which stores data after 
+\item{alt_spat_unit}{alternative spatial unit which stores data after
 resizing annotations}
 
 \item{feat_type}{feature type}
diff --git a/man/showPolygonSizeInfluence.Rd b/man/showPolygonSizeInfluence.Rd
index c3562b6a8..44d7469f2 100644
--- a/man/showPolygonSizeInfluence.Rd
+++ b/man/showPolygonSizeInfluence.Rd
@@ -19,12 +19,12 @@ showPolygonSizeInfluence(
 
 \item{spat_unit}{spatial unit}
 
-\item{alt_spat_unit}{alternaitve spatial unit which represents resized 
+\item{alt_spat_unit}{alternaitve spatial unit which represents resized
 polygon data}
 
 \item{feat_type}{feature type}
 
-\item{clus_name}{name of cluster column in cell_metadata for given spat_unit 
+\item{clus_name}{name of cluster column in cell_metadata for given spat_unit
 and alt_spat_unit, i.e. "kmeans"}
 
 \item{return_plot}{logical. whether to return the plot object}
@@ -43,12 +43,12 @@ Compares cell metadata from spat_unit-feat_type pairs as provided.
 New columns, resize_switch and cluster_interaction, will be created within
 cell_metadata for spat_unit-feat_type.
 
-These new columns will describe if a given cell switched cluster number when 
+These new columns will describe if a given cell switched cluster number when
 resized.
 If the same amount of clusters exist for spat_unit-feat_type and
 alt_spat_unit-feat_type, then clusters are determined to be
 corresponding based on % overlap in cell_IDs in each cluster.
 
-Otherwise, multiple clusters from the spatial unit feature type pair are 
+Otherwise, multiple clusters from the spatial unit feature type pair are
 condensed to align with the smaller number of clusters and ensure overlap.
 }
diff --git a/man/signPCA.Rd b/man/signPCA.Rd
index 6245bde35..0df0b6379 100644
--- a/man/signPCA.Rd
+++ b/man/signPCA.Rd
@@ -84,14 +84,14 @@ ggplot object for scree method and maxtrix of p-values for jackstraw
 identify significant prinicipal components (PCs)
 }
 \details{
-Two different methods can be used to assess the number of relevant 
+Two different methods can be used to assess the number of relevant
 or significant prinicipal components (PC's). \cr
  1. Screeplot works by plotting the explained variance of each
- individual PC in a barplot allowing you to identify which PC provides a 
+ individual PC in a barplot allowing you to identify which PC provides a
  significant
  contribution  (a.k.a. 'elbow method'). \cr
- 2. The Jackstraw method uses the \code{\link[jackstraw]{permutationPA}} 
- function. By systematically permuting genes it identifies robust, and thus 
+ 2. The Jackstraw method uses the \code{\link[jackstraw]{permutationPA}}
+ function. By systematically permuting genes it identifies robust, and thus
  significant, PCs.
  \cr
 }
diff --git a/man/simulateOneGenePatternGiottoObject.Rd b/man/simulateOneGenePatternGiottoObject.Rd
index 68829a014..b73185499 100644
--- a/man/simulateOneGenePatternGiottoObject.Rd
+++ b/man/simulateOneGenePatternGiottoObject.Rd
@@ -37,7 +37,7 @@ part of the spatial pattern}
 \item{normalization_params}{additional parameters for (re-)normalizing}
 }
 \value{
-Reprocessed Giotto object for which one gene has a forced 
+Reprocessed Giotto object for which one gene has a forced
 spatial pattern
 }
 \description{
@@ -46,8 +46,12 @@ Create a simulated spatial pattern for one selected gnee
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-simulateOneGenePatternGiottoObject(gobject = g, 
-pattern_cell_ids = c("AAAGGGATGTAGCAAG-1", "TCAAACAACCGCGTCG-1",
-"ACGATCATACATAGAG-1", "TATGCTCCCTACTTAC-1"),
-gene_name = "Gna12")
+simulateOneGenePatternGiottoObject(
+    gobject = g,
+    pattern_cell_ids = c(
+        "AAAGGGATGTAGCAAG-1", "TCAAACAACCGCGTCG-1",
+        "ACGATCATACATAGAG-1", "TATGCTCCCTACTTAC-1"
+    ),
+    gene_name = "Gna12"
+)
 }
diff --git a/man/spatCellCellcom.Rd b/man/spatCellCellcom.Rd
index e73bf8ebc..5c04c1a1d 100644
--- a/man/spatCellCellcom.Rd
+++ b/man/spatCellCellcom.Rd
@@ -2,14 +2,15 @@
 % Please edit documentation in R/spatial_interaction.R
 \name{spatCellCellcom}
 \alias{spatCellCellcom}
-\title{spatCellCellcom}
+\alias{specificCellCellcommunicationScores}
+\title{Spatial cell cell communication scoring}
 \usage{
 spatCellCellcom(
   gobject,
   feat_type = NULL,
   spat_unit = NULL,
   spatial_network_name = "Delaunay_network",
-  cluster_column = "cell_types",
+  cluster_column = NULL,
   random_iter = 1000,
   feat_set_1,
   feat_set_2,
@@ -27,6 +28,30 @@ spatCellCellcom(
   seed_number = 1234,
   verbose = c("a little", "a lot", "none")
 )
+
+specificCellCellcommunicationScores(
+  gobject,
+  feat_type = NULL,
+  spat_unit = NULL,
+  spatial_network_name = "Delaunay_network",
+  cluster_column = NULL,
+  random_iter = 100,
+  cell_type_1 = NULL,
+  cell_type_2 = NULL,
+  feat_set_1,
+  feat_set_2,
+  gene_set_1 = NULL,
+  gene_set_2 = NULL,
+  log2FC_addendum = 0.1,
+  min_observations = 2,
+  detailed = FALSE,
+  adjust_method = c("fdr", "bonferroni", "BH", "holm", "hochberg", "hommel", "BY",
+    "none"),
+  adjust_target = c("feats", "cells"),
+  set_seed = FALSE,
+  seed_number = 1234,
+  verbose = TRUE
+)
 }
 \arguments{
 \item{gobject}{giotto object to use}
@@ -71,6 +96,10 @@ considered}
 \item{seed_number}{seed number}
 
 \item{verbose}{verbose}
+
+\item{cell_type_1}{character. First cell type}
+
+\item{cell_type_2}{character. Second cell type}
 }
 \value{
 Cell-Cell communication scores for feature pairs based on spatial
@@ -110,13 +139,13 @@ LR_expr and rand_expr over all random spatial permutations
 \item \strong{pvalue:} p-value
 \item \strong{LR_cell_comb:} cell type pair combination
 \item \strong{p.adj:} adjusted p-value
-\item \strong{PI:} significance score: log2fc * -log10(p.adj)
+\item \strong{PI:} significance score: \eqn{log2fc * -log10(p.adj)}
 }
 }
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
-spatCellCellcom(
+res1 <- spatCellCellcom(
     gobject = g,
     cluster_column = "leiden_clus",
     feat_set_1 = "Gm19935",
@@ -124,4 +153,15 @@ spatCellCellcom(
     verbose = "a lot",
     random_iter = 10
 )
+force(res1)
+
+res2 <- specificCellCellcommunicationScores(g,
+    cluster_column = "leiden_clus",
+    cell_type_1 = 1,
+    cell_type_2 = 2,
+    feat_set_1 = "Gm19935",
+    feat_set_2 = "9630013A20Rik"
+)
+
+force(res2)
 }
diff --git a/man/spatCellCellcomSpots.Rd b/man/spatCellCellcomSpots.Rd
index 6c219f697..ac6e46389 100644
--- a/man/spatCellCellcomSpots.Rd
+++ b/man/spatCellCellcomSpots.Rd
@@ -36,7 +36,7 @@ spatCellCellcomSpots(
 
 \item{ave_celltype_exp}{Matrix with average expression per cell type}
 
-\item{spatial_network_name}{spatial network to use for identifying 
+\item{spatial_network_name}{spatial network to use for identifying
 interacting cells}
 
 \item{cluster_column}{cluster column with cell type information}
@@ -47,17 +47,17 @@ interacting cells}
 
 \item{feature_set_2}{second specific feature set from feature pairs}
 
-\item{min_observations}{minimum number of interactions needed to be 
+\item{min_observations}{minimum number of interactions needed to be
 considered}
 
 \item{expression_values}{(e.g. 'normalized', 'scaled', 'custom')}
 
-\item{detailed}{provide more detailed information 
+\item{detailed}{provide more detailed information
 (random variance and z-score)}
 
 \item{adjust_method}{which method to adjust p-values}
 
-\item{adjust_target}{adjust multiple hypotheses at the cell or feature 
+\item{adjust_target}{adjust multiple hypotheses at the cell or feature
 level}
 
 \item{do_parallel}{run calculations in parallel with mclapply}
@@ -71,37 +71,44 @@ level}
 \item{verbose}{verbose (e.g. 'a little', 'a lot', 'none')}
 }
 \value{
-Cell-Cell communication scores for feature pairs based on spatial 
+Cell-Cell communication scores for feature pairs based on spatial
 interaction
 }
 \description{
-Spatial Cell-Cell communication scores based on spatial 
+Spatial Cell-Cell communication scores based on spatial
 expression of interacting cells at spots resolution
 }
 \details{
-Statistical framework to identify if pairs of features 
-(such as ligand-receptor combinations) are expressed at higher levels than 
-expected based on a reshuffled null distribution of feature expression 
+Statistical framework to identify if pairs of features
+(such as ligand-receptor combinations) are expressed at higher levels than
+expected based on a reshuffled null distribution of feature expression
 values in cells that are spatially in proximity to each other.
 \itemize{
- \item{LR_comb:}{Pair of ligand and receptor}
- \item{lig_cell_type:}{ cell type to assess expression level of ligand }
- \item{lig_expr:}{ average expression residual(observed - DWLS_predicted) of ligand in lig_cell_type }
- \item{ligand:}{ ligand name }
- \item{rec_cell_type:}{ cell type to assess expression level of receptor }
- \item{rec_expr:}{ average expression residual(observed - DWLS_predicted) of receptor in rec_cell_type}
- \item{receptor:}{ receptor name }
- \item{LR_expr:}{ combined average ligand and receptor expression residual}
- \item{lig_nr:}{ total number of cells from lig_cell_type that spatially interact with cells from rec_cell_type }
- \item{rec_nr:}{ total number of cells from rec_cell_type that spatially interact with cells from lig_cell_type }
- \item{rand_expr:}{ average combined ligand and receptor expression residual from random spatial permutations }
- \item{av_diff:}{ average difference between LR_expr and rand_expr over all random spatial permutations }
- \item{sd_diff:}{ (optional) standard deviation of the difference between LR_expr and rand_expr over all random spatial permutations }
- \item{z_score:}{ (optinal) z-score }
- \item{log2fc:}{ LR_expr - rand_expr }
- \item{pvalue:}{ p-value }
- \item{LR_cell_comb:}{ cell type pair combination }
- \item{p.adj:}{ adjusted p-value }
- \item{PI:}{ significanc score: log2fc * -log10(p.adj) }
+ * LR_comb:Pair of ligand and receptor
+ * lig_cell_type: cell type to assess expression level of ligand
+ * lig_expr: average expression residual(observed - DWLS_predicted) of
+ ligand in lig_cell_type
+ * ligand: ligand name
+ * rec_cell_type: cell type to assess expression level of receptor
+ * rec_expr: average expression residual(observed - DWLS_predicted) of
+ receptor in rec_cell_type
+ * receptor: receptor name
+ * LR_expr: combined average ligand and receptor expression residual
+ * lig_nr: total number of cells from lig_cell_type that spatially interact
+ with cells from rec_cell_type
+ * rec_nr: total number of cells from rec_cell_type that spatially interact
+ with cells from lig_cell_type
+ * rand_expr: average combined ligand and receptor expression residual from
+ random spatial permutations
+ * av_diff: average difference between LR_expr and rand_expr over all random
+ spatial permutations
+ * sd_diff: (optional) standard deviation of the difference between LR_expr
+ and rand_expr over all random spatial permutations
+ * z_score: (optional) z-score
+ * log2fc: LR_expr - rand_expr
+ * pvalue: p-value
+ * LR_cell_comb: cell type pair combination
+ * p.adj: adjusted p-value
+ * PI: significanc score: log2fc \* -log10(p.adj)
 }
 }
diff --git a/man/spatialAEH.Rd b/man/spatialAEH.Rd
index c97cc454a..02457c49a 100644
--- a/man/spatialAEH.Rd
+++ b/man/spatialAEH.Rd
@@ -48,7 +48,7 @@ An updated giotto object
 Compute spatial variable genes with spatialDE method
 }
 \details{
-This function is a wrapper for the SpatialAEH method 
+This function is a wrapper for the SpatialAEH method
 implemented in the ...
 }
 \examples{
diff --git a/man/spatialDE.Rd b/man/spatialDE.Rd
index 876a72c88..d6e414023 100644
--- a/man/spatialDE.Rd
+++ b/man/spatialDE.Rd
@@ -62,7 +62,7 @@ a list of data.frames with results and plot (optional)
 Compute spatial variable genes with spatialDE method
 }
 \details{
-This function is a wrapper for the SpatialDE method originally 
+This function is a wrapper for the SpatialDE method originally
 implemented
 in python. See publication \doi{10.1038/nmeth.4636}
 }
diff --git a/man/spdepAutoCorr.Rd b/man/spdepAutoCorr.Rd
index 9364ef8af..31b226470 100644
--- a/man/spdepAutoCorr.Rd
+++ b/man/spdepAutoCorr.Rd
@@ -19,7 +19,7 @@ spdepAutoCorr(
 \item{gobject}{Input a Giotto object.}
 
 \item{method}{Specify a method name to compute auto correlation.
-Available methods include 
+Available methods include
 \code{"geary.test", "lee.test", "lm.morantest","moran.test"}.}
 
 \item{spat_unit}{spatial unit}
@@ -28,7 +28,7 @@ Available methods include
 
 \item{expression_values}{expression values to use, default = normalized}
 
-\item{spatial_network_to_use}{spatial network to use, 
+\item{spatial_network_to_use}{spatial network to use,
 default = spatial_network}
 
 \item{return_gobject}{if FALSE, results are returned as data.table.
diff --git a/man/specificCellCellcommunicationScores.Rd b/man/specificCellCellcommunicationScores.Rd
deleted file mode 100644
index ac12f78b9..000000000
--- a/man/specificCellCellcommunicationScores.Rd
+++ /dev/null
@@ -1,115 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/spatial_interaction.R
-\name{specificCellCellcommunicationScores}
-\alias{specificCellCellcommunicationScores}
-\title{specificCellCellcommunicationScores}
-\usage{
-specificCellCellcommunicationScores(
-  gobject,
-  feat_type = NULL,
-  spat_unit = NULL,
-  spatial_network_name = "Delaunay_network",
-  cluster_column = "cell_types",
-  random_iter = 100,
-  cell_type_1 = "astrocyte",
-  cell_type_2 = "endothelial",
-  feat_set_1,
-  feat_set_2,
-  gene_set_1 = NULL,
-  gene_set_2 = NULL,
-  log2FC_addendum = 0.1,
-  min_observations = 2,
-  detailed = FALSE,
-  adjust_method = c("fdr", "bonferroni", "BH", "holm", "hochberg", "hommel", "BY",
-    "none"),
-  adjust_target = c("feats", "cells"),
-  set_seed = FALSE,
-  seed_number = 1234,
-  verbose = TRUE
-)
-}
-\arguments{
-\item{gobject}{giotto object to use}
-
-\item{feat_type}{feature type}
-
-\item{spat_unit}{spatial unit}
-
-\item{spatial_network_name}{spatial network to use for identifying
-interacting cells}
-
-\item{cluster_column}{cluster column with cell type information}
-
-\item{random_iter}{number of iterations}
-
-\item{cell_type_1}{first cell type}
-
-\item{cell_type_2}{second cell type}
-
-\item{feat_set_1}{first specific gene set from gene pairs}
-
-\item{feat_set_2}{second specific gene set from gene pairs}
-
-\item{gene_set_1}{deprecated, use feat_set_1}
-
-\item{gene_set_2}{deprecated, use feat_set_2}
-
-\item{log2FC_addendum}{addendum to add when calculating log2FC}
-
-\item{min_observations}{minimum number of interactions needed to be
-considered}
-
-\item{detailed}{provide more detailed information
-(random variance and z-score)}
-
-\item{adjust_method}{which method to adjust p-values}
-
-\item{adjust_target}{adjust multiple hypotheses at the cell or feature level}
-
-\item{set_seed}{set a seed for reproducibility}
-
-\item{seed_number}{seed number}
-
-\item{verbose}{verbose}
-}
-\value{
-Cell-Cell communication scores for feature pairs based on spatial 
-interaction
-}
-\description{
-Specific Cell-Cell communication scores based on spatial
-expression of interacting cells
-}
-\details{
-Statistical framework to identify if pairs of features
-(such as ligand-receptor combinations)
-are expressed at higher levels than expected based on a reshuffled null
-distribution of feature expression values in cells that are spatially in
-proximity to each other.
-\itemize{
- \item{LR_comb:}{Pair of ligand and receptor}
- \item{lig_cell_type:}{ cell type to assess expression level of ligand }
- \item{lig_expr:}{ average expression of ligand in lig_cell_type }
- \item{ligand:}{ ligand name }
- \item{rec_cell_type:}{ cell type to assess expression level of receptor }
- \item{rec_expr:}{ average expression of receptor in rec_cell_type}
- \item{receptor:}{ receptor name }
- \item{LR_expr:}{ combined average ligand and receptor expression }
- \item{lig_nr:}{ total number of cells from lig_cell_type that spatially interact with cells from rec_cell_type }
- \item{rec_nr:}{ total number of cells from rec_cell_type that spatially interact with cells from lig_cell_type }
- \item{rand_expr:}{ average combined ligand and receptor expression from random spatial permutations }
- \item{av_diff:}{ average difference between LR_expr and rand_expr over all random spatial permutations }
- \item{sd_diff:}{ (optional) standard deviation of the difference between LR_expr and rand_expr over all random spatial permutations }
- \item{z_score:}{ (optinal) z-score }
- \item{log2fc:}{ log2 fold-change (LR_expr/rand_expr) }
- \item{pvalue:}{ p-value }
- \item{LR_cell_comb:}{ cell type pair combination }
- \item{p.adj:}{ adjusted p-value }
- \item{PI:}{ significanc score: log2fc * -log10(p.adj) }
-}
-}
-\examples{
-g <- GiottoData::loadGiottoMini("visium")
-
-specificCellCellcommunicationScores(g, cluster_column = "leiden_clus")
-}
diff --git a/man/subClusterCells.Rd b/man/subClusterCells.Rd
index 4eca454f8..a595efc8a 100644
--- a/man/subClusterCells.Rd
+++ b/man/subClusterCells.Rd
@@ -2,7 +2,9 @@
 % Please edit documentation in R/clustering.R
 \name{subClusterCells}
 \alias{subClusterCells}
-\title{subClusterCells}
+\alias{doLeidenSubCluster}
+\alias{doLouvainSubCluster}
+\title{Cell subclustering}
 \usage{
 subClusterCells(
   gobject,
@@ -10,12 +12,17 @@ subClusterCells(
   cluster_method = c("leiden", "louvain_community", "louvain_multinet"),
   cluster_column = NULL,
   selected_clusters = NULL,
-  hvg_param = list(reverse_log_scale = TRUE, difference_in_cov = 1, expression_values =
+  hvg_param = deprecated(),
+  hvf_param = list(reverse_log_scale = TRUE, difference_in_cov = 1, expression_values =
     "normalized"),
-  hvg_min_perc_cells = 5,
-  hvg_mean_expr_det = 1,
-  use_all_genes_as_hvg = FALSE,
-  min_nr_of_hvg = 5,
+  hvg_min_perc_cells = deprecated(),
+  hvf_min_perc_cells = 5,
+  hvg_mean_expr_det = deprecated(),
+  hvf_mean_expr_det = 1,
+  use_all_genes_as_hvg = deprecated(),
+  use_all_feats_as_hvf = FALSE,
+  min_nr_of_hvg = deprecated(),
+  min_nr_of_hvf = 5,
   pca_param = list(expression_values = "normalized", scale_unit = TRUE),
   nn_param = list(dimensions_to_use = 1:20),
   k_neighbors = 10,
@@ -29,40 +36,111 @@ subClusterCells(
   return_gobject = TRUE,
   verbose = TRUE
 )
+
+doLeidenSubCluster(
+  gobject,
+  feat_type = NULL,
+  name = "sub_leiden_clus",
+  cluster_column = NULL,
+  selected_clusters = NULL,
+  hvf_param = list(reverse_log_scale = TRUE, difference_in_cov = 1, expression_values =
+    "normalized"),
+  hvg_param = deprecated(),
+  hvf_min_perc_cells = 5,
+  hvg_min_perc_cells = deprecated(),
+  hvf_mean_expr_det = 1,
+  hvg_mean_expr_det = deprecated(),
+  use_all_feats_as_hvf = FALSE,
+  use_all_genes_as_hvg = deprecated(),
+  min_nr_of_hvf = 5,
+  min_nr_of_hvg = deprecated(),
+  pca_param = list(expression_values = "normalized", scale_unit = TRUE),
+  nn_param = list(dimensions_to_use = 1:20),
+  k_neighbors = 10,
+  resolution = 0.5,
+  n_iterations = 500,
+  python_path = NULL,
+  nn_network_to_use = "sNN",
+  network_name = "sNN.pca",
+  return_gobject = TRUE,
+  toplevel = 2,
+  verbose = TRUE
+)
+
+doLouvainSubCluster(
+  gobject,
+  name = "sub_louvain_clus",
+  version = c("community", "multinet"),
+  cluster_column = NULL,
+  selected_clusters = NULL,
+  hvg_param = deprecated(),
+  hvf_param = list(reverse_log_scale = TRUE, difference_in_cov = 1, expression_values =
+    "normalized"),
+  hvg_min_perc_cells = deprecated(),
+  hvf_min_perc_cells = 5,
+  hvg_mean_expr_det = deprecated(),
+  hvf_mean_expr_det = 1,
+  use_all_genes_as_hvg = deprecated(),
+  use_all_feats_as_hvf = FALSE,
+  min_nr_of_hvg = deprecated(),
+  min_nr_of_hvf = 5,
+  pca_param = list(expression_values = "normalized", scale_unit = TRUE),
+  nn_param = list(dimensions_to_use = 1:20),
+  k_neighbors = 10,
+  resolution = 0.5,
+  gamma = 1,
+  omega = 1,
+  python_path = NULL,
+  nn_network_to_use = "sNN",
+  network_name = "sNN.pca",
+  return_gobject = TRUE,
+  verbose = TRUE
+)
 }
 \arguments{
-\item{gobject}{giotto object}
+\item{gobject}{\code{giotto} object}
 
 \item{name}{name for new clustering result}
 
-\item{cluster_method}{clustering method to use}
+\item{cluster_method}{clustering method to use. Currently one of "leiden"
+(default), "louvain_community", "louvain_multinet"}
 
 \item{cluster_column}{cluster column to subcluster}
 
 \item{selected_clusters}{only do subclustering on these clusters}
 
-\item{hvg_param}{parameters for calculateHVG}
+\item{hvg_param}{deprecated}
 
-\item{hvg_min_perc_cells}{threshold for detection in min percentage of cells}
+\item{hvf_param}{list of parameters for \code{\link[=calculateHVF]{calculateHVF()}}}
 
-\item{hvg_mean_expr_det}{threshold for mean expression level in cells with
+\item{hvg_min_perc_cells}{deprecated}
+
+\item{hvf_min_perc_cells}{threshold for detection in min percentage of cells}
+
+\item{hvg_mean_expr_det}{deprecated}
+
+\item{hvf_mean_expr_det}{threshold for mean expression level in cells with
 detection}
 
-\item{use_all_genes_as_hvg}{forces all genes to be HVG and to be used as
+\item{use_all_genes_as_hvg}{deprecated}
+
+\item{use_all_feats_as_hvf}{forces all features to be HVF and to be used as
 input for PCA}
 
-\item{min_nr_of_hvg}{minimum number of HVG, or all genes will be used as
+\item{min_nr_of_hvg}{deprecated}
+
+\item{min_nr_of_hvf}{minimum number of HVF, or all features will be used as
 input for PCA}
 
-\item{pca_param}{parameters for runPCA}
+\item{pca_param}{list of parameters for \code{\link[=runPCA]{runPCA()}}}
 
-\item{nn_param}{parameters for parameters for createNearestNetwork}
+\item{nn_param}{list of parameters for \code{\link[=createNearestNetwork]{createNearestNetwork()}}}
 
-\item{k_neighbors}{number of k for createNearestNetwork}
+\item{k_neighbors}{number of k for \code{\link[=createNearestNetwork]{createNearestNetwork()}}}
 
-\item{resolution}{resolution}
+\item{resolution}{resolution for community algorithm}
 
-\item{n_iterations}{number of interations to run the Leiden algorithm.}
+\item{n_iterations}{number of iterations to run the Leiden algorithm.}
 
 \item{gamma}{gamma}
 
@@ -74,34 +152,64 @@ input for PCA}
 
 \item{network_name}{name of NN network to use}
 
-\item{return_gobject}{boolean: return giotto object (default = TRUE)}
+\item{return_gobject}{logical. return \code{giotto} object (default = TRUE)}
 
 \item{verbose}{verbose}
+
+\item{feat_type}{feature type}
+
+\item{toplevel}{do not use}
+
+\item{version}{version of Louvain algorithm to use. One of "community" or
+"multinet", with the default being "community"}
 }
 \value{
-giotto object with new subclusters appended to cell metadata
+\code{giotto} object with new subclusters appended to cell metadata
 }
 \description{
-subcluster cells
+Perform cell subclustering by taking an annotated group of
+cells and performing another round of clustering on just that subset.
+Several methods are implemented. \code{subClusterCells()} is the main wrapper
+function. \code{doLeidenSubCluster()} and \code{doLouvainSubCluster()} are more
+specific implementations.
 }
 \details{
 This function performs subclustering on selected clusters.
 The systematic steps are:
-\itemize{
-  \item{1. subset Giotto object}
-  \item{2. identify highly variable genes}
-  \item{3. run PCA}
-  \item{4. create nearest neighbouring network}
-  \item{5. do clustering}
+\enumerate{
+\item subset Giotto object
+\item identify highly variable genes
+\item run PCA
+\item create nearest neighbouring network
+\item do clustering
 }
 }
+\section{Functions}{
+\itemize{
+\item \code{doLeidenSubCluster()}: Further subcluster cells using a NN-network and
+the Leiden algorithm
+
+\item \code{doLouvainSubCluster()}: subcluster cells using a NN-network and the
+Louvain algorithm
+
+}}
 \examples{
 g <- GiottoData::loadGiottoMini("visium")
 
+# Run some subclusterings based on "leiden_clus" annotations that already
+# exist in the visium mini object
+
+# default method is leiden subclustering
 subClusterCells(g, cluster_column = "leiden_clus")
-}
-\seealso{
-\code{\link{.doLouvainCluster_multinet}},
-\code{\link{.doLouvainCluster_community}}
-and  @seealso \code{\link{doLeidenCluster}}
+
+# use louvain instead
+subClusterCells(g,
+    cluster_column = "leiden_clus",
+    cluster_method = "louvain_community"
+)
+
+# directly call the more specific functions
+doLeidenSubCluster(g, cluster_column = "leiden_clus")
+
+doLouvainSubCluster(g, cluster_column = "leiden_clus")
 }
diff --git a/man/trendSceek.Rd b/man/trendSceek.Rd
index fdcc03bf4..72bea81f6 100644
--- a/man/trendSceek.Rd
+++ b/man/trendSceek.Rd
@@ -45,7 +45,7 @@ data.frame with trendsceek spatial genes results
 Compute spatial variable genes with trendsceek method
 }
 \details{
-This function is a wrapper for the trendsceek_test method 
+This function is a wrapper for the trendsceek_test method
 implemented in the trendsceek package
 Publication: \doi{10.1038/nmeth.4634}
 }
diff --git a/man/viewHMRFresults2D.Rd b/man/viewHMRFresults2D.Rd
index aeae8bd19..8d0c87a38 100644
--- a/man/viewHMRFresults2D.Rd
+++ b/man/viewHMRFresults2D.Rd
@@ -4,11 +4,23 @@
 \alias{viewHMRFresults2D}
 \title{viewHMRFresults2D}
 \usage{
-viewHMRFresults2D(gobject, HMRFoutput, k = NULL, betas_to_view = NULL, ...)
+viewHMRFresults2D(
+  gobject,
+  spat_unit = NULL,
+  feat_type = NULL,
+  HMRFoutput,
+  k = NULL,
+  betas_to_view = NULL,
+  ...
+)
 }
 \arguments{
 \item{gobject}{giotto object}
 
+\item{spat_unit}{spatial unit}
+
+\item{feat_type}{feature type}
+
 \item{HMRFoutput}{HMRF output from doHMRF}
 
 \item{k}{number of HMRF domains}
diff --git a/man/viewHMRFresults3D.Rd b/man/viewHMRFresults3D.Rd
index 0eb7e2e3f..3158bd2fe 100644
--- a/man/viewHMRFresults3D.Rd
+++ b/man/viewHMRFresults3D.Rd
@@ -4,11 +4,23 @@
 \alias{viewHMRFresults3D}
 \title{viewHMRFresults3D}
 \usage{
-viewHMRFresults3D(gobject, HMRFoutput, k = NULL, betas_to_view = NULL, ...)
+viewHMRFresults3D(
+  gobject,
+  spat_unit = NULL,
+  feat_type = NULL,
+  HMRFoutput,
+  k = NULL,
+  betas_to_view = NULL,
+  ...
+)
 }
 \arguments{
 \item{gobject}{giotto object}
 
+\item{spat_unit}{spatial unit}
+
+\item{feat_type}{feature type}
+
 \item{HMRFoutput}{HMRF output from doHMRF}
 
 \item{k}{number of HMRF domains}
diff --git a/man/viewHMRFresults_V2.Rd b/man/viewHMRFresults_V2.Rd
index 67a05c64a..c8baf3d77 100644
--- a/man/viewHMRFresults_V2.Rd
+++ b/man/viewHMRFresults_V2.Rd
@@ -66,8 +66,8 @@ spatial plots with HMRF domains
 function to view HMRF results with multiple betas
 }
 \details{
-This function plots spatial map of HMRF domain clusters for multiple beta 
+This function plots spatial map of HMRF domain clusters for multiple beta
 with the name (hmrf_name),
-matching the first part of the cell meta column names with HMRF clusters 
+matching the first part of the cell meta column names with HMRF clusters
 (for example name of ‘hmrf1 k=8 b=0.00’ is ‘hmrf1’)
 }
diff --git a/man/visium_micron_scalefactor.Rd b/man/visium_micron_scalefactor.Rd
index 9c9f93949..96eb9e3ea 100644
--- a/man/visium_micron_scalefactor.Rd
+++ b/man/visium_micron_scalefactor.Rd
@@ -8,7 +8,7 @@
 .visium_micron_scale(json_scalefactors)
 }
 \arguments{
-\item{json_scalefactors}{list of scalefactors from 
+\item{json_scalefactors}{list of scalefactors from
 .visium_read_scalefactors()}
 }
 \value{
diff --git a/man/write_giotto_viewer_annotation.Rd b/man/write_giotto_viewer_annotation.Rd
index ef5c40f51..9e2c2334b 100644
--- a/man/write_giotto_viewer_annotation.Rd
+++ b/man/write_giotto_viewer_annotation.Rd
@@ -21,7 +21,7 @@ write_giotto_viewer_annotation(
 write a .txt and .annot file for the selection annotation
 }
 \description{
-write out factor-like annotation data from a giotto object for 
+write out factor-like annotation data from a giotto object for
 the Viewer
 }
 \keyword{internal}
diff --git a/vignettes/intro_to_giotto.Rmd b/vignettes/intro_to_giotto.Rmd
index a446de3aa..3f5ce35ec 100644
--- a/vignettes/intro_to_giotto.Rmd
+++ b/vignettes/intro_to_giotto.Rmd
@@ -9,8 +9,8 @@ vignette: >
 
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
-  collapse = TRUE,
-  comment = "#>"
+    collapse = TRUE,
+    comment = "#>"
 )
 ```
 
@@ -63,8 +63,9 @@ g <- runPCA(g)
 Plot PCA
 
 ```{r}
-plotPCA(g, 
-        cell_color = "leiden_clus")
+plotPCA(g,
+    cell_color = "leiden_clus"
+)
 ```
 
 Run UMAP
@@ -77,7 +78,8 @@ Plot UMAP
 
 ```{r}
 plotUMAP(g,
-         cell_color = "leiden_clus")
+    cell_color = "leiden_clus"
+)
 ```
 
 Run tSNE
@@ -90,7 +92,8 @@ Plot tSNE
 
 ```{r}
 plotTSNE(g,
-         cell_color = "leiden_clus")
+    cell_color = "leiden_clus"
+)
 ```
 
 Do clustering
@@ -103,7 +106,8 @@ Spatial plot with clusters
 
 ```{r}
 spatPlot2D(g,
-           cell_color = "leiden_clus")
+    cell_color = "leiden_clus"
+)
 ```
 
 Session info