chore: merge & conflict

R/clustering.R

@@ -113,7 +113,7 @@ doLeidenCluster <- function(gobject,
     if (isTRUE(set_seed)) {
         seed_number <- as.integer(seed_number)
     } else {
-        seed_number <- as.integer(sample(x = seq_len(10000), size = 1))
+        seed_number <- as.integer(sample(x = seq(10000), size = 1))
     }
 
     ## extract NN network
@@ -551,7 +551,7 @@ doGiottoClustree <- function(gobject,
     if (isTRUE(set_seed)) {
         seed_number <- as.integer(seed_number)
     } else {
-        seed_number <- as.integer(sample(x = seq_len(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(
@@ -1102,9 +1102,9 @@ doSNNCluster <- function(gobject,
     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 = seq_len(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 = seq_len(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"))
@@ -2814,7 +2814,7 @@ getClusterSimilarity <- function(gobject,
         as.character(group1), as.character(group2))]
     cor_table[, unified_group := paste(
         sort(c(group1, group2)), collapse = "--"), 
-        by = seq_len(nrow(cor_table))]
+        by = 1:nrow(cor_table)]
     cor_table <- cor_table[!duplicated(cor_table[, .(value, unified_group)])]
 
     cor_table <- merge(
@@ -2972,7 +2972,7 @@ mergeClusters <- function(gobject,
         as.character(get(cluster_column)) %in% finalvec,
         names(finalvec[finalvec == as.character(get(cluster_column))]),
         as.character(get(cluster_column))
-    ), by = seq_len(nrow(metadata))]
+    ), by = 1:nrow(metadata)]
 
 
 

R/cross_section.R

@@ -542,7 +542,7 @@ createCrossSection <- function(gobject,
             message("either point or norm vector was not provided.")
         } else {
             plane_equation <- c()
-            plane_equation[seq_len(3)] <- normVector
+            plane_equation[1:3] <- normVector
             plane_equation[4] <- -point1 %*% normVector
         }
     } else if (method == "point and two plane vectors") {
@@ -551,7 +551,7 @@ createCrossSection <- function(gobject,
                     provided.")
         } else {
             normVector <- crossprod(planeVector1, planeVector2)
-            plane_equation[seq_len(3)] <- normVector
+            plane_equation[1:3] <- normVector
             plane_equation[4] <- -point1 %*% normVector
         }
     } else if (method == "3 points") {
@@ -561,7 +561,7 @@ createCrossSection <- function(gobject,
             planeVector1 <- point2 - point1
             planeVector2 <- point3 - point1
             normVector <- crossprod(planeVector1, planeVector2)
-            plane_equation[seq_len(3)] <- normVector
+            plane_equation[1:3] <- normVector
             plane_equation[4] <- -point1 %*% normVector
         }
     }
@@ -584,7 +584,7 @@ createCrossSection <- function(gobject,
         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[seq_len(3)]))
+        plane_equation) / norm_vec(plane_equation[1:3]))
 
     # select cells within section ###
     cell_subset <- distance_to_plane_vector <= max_distance_to_section_plane
@@ -604,7 +604,7 @@ createCrossSection <- function(gobject,
     cell_subset_projection_locations <- t(apply(
         cell_subset_spatial_locations, 1, 
         function(x) projection_fun(x, plane_point = plane_point, 
-                                plane_norm = plane_equation[seq_len(3)])))
+                                plane_norm = plane_equation[1:3])))
 
     # get the local coordinates of selected cells on the section plane
     cell_subset_projection_PCA <- stats::prcomp(

R/kriging.R

@@ -56,6 +56,8 @@ setMethod(
     overwrite = FALSE,
     verbose = NULL,
     ...) {
+        sl <- NULL
+
         # This method prepares the data from the giotto object to pass
         # downstream where the actual interpolation happens
 

R/python_hmrf.R

@@ -551,6 +551,15 @@ 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 <- tempdir()
+#' doHMRF(g, spatial_genes = spat_genes[seq_len(10)]$feats, 
+#' output_folder = output_folder)
+#' 
+#' addHMRF(gobject = g, HMRFoutput = doHMRF)
 #' @export
 addHMRF <- function(gobject,
     spat_unit = NULL,
@@ -1088,7 +1097,7 @@ 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
@@ -1152,10 +1161,10 @@ checkAndFixSpatialGenes <- function(gobject,
 #' 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
+#' 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 
@@ -1165,6 +1174,11 @@ checkAndFixSpatialGenes <- function(gobject,
 #' 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,
@@ -1692,6 +1706,12 @@ initHMRF_V2 <-
 #' 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(
@@ -1752,7 +1772,7 @@ doHMRF_V2 <- function(HMRF_init_obj, betas = NULL) {
         cat(paste0("Default value beta = ", beta_seq, " is used..."))
     } else if (length(betas) != 3 || (sum(betas[seq_len(3)] < 0) > 0)) {
         stop("please provide betas as a vector of 3 non-negative numbers 
-            (initial value, nicrement, total iteration number)")
+            (initial value, increment, total iteration number)")
     } else {
         beta_init <- betas[1]
         beta_increment <- betas[2]
@@ -1782,7 +1802,7 @@ doHMRF_V2 <- function(HMRF_init_obj, betas = 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$class) <- rownames(y)
         res[[t_key]] <- tc.hmrfem
     }
     result.hmrf <- res
@@ -1805,6 +1825,13 @@ doHMRF_V2 <- function(HMRF_init_obj, betas = NULL) {
 #' for all the beta values, with the given HMRF model names. For example, if 
 #' name = ‘hmrf1’ and name of result in HMRFoutput is ‘k=8 b=0.00’, the 
 #' appended cell meta data column will be named with ‘hmrf1 k=8 b=0.00’
+#' @examples
+#' g <- GiottoData::loadGiottoMini("visium")
+#' g <- binSpect(g, return_gobject = TRUE)
+#' HMRF_init_obj <- initHMRF_V2(gobject = g, cl.method = "km")
+#' HMRFoutput <- doHMRF_V2(HMRF_init_obj = HMRF_init_obj, betas = c(0, 5, 2))
+#' 
+#' addHMRF_V2(gobject = g, HMRFoutput = HMRFoutput)
 #' @export
 addHMRF_V2 <- function(gobject, HMRFoutput, name = "hmrf") {
     if (!"HMRFoutput" %in% class(HMRFoutput)) {
@@ -1834,10 +1861,11 @@ addHMRF_V2 <- function(gobject, HMRFoutput, name = "hmrf") {
             spat_unit = spat_unit,
             feat_type = feat_type,
             column_cell_ID = "cell_ID",
-            new_metadata = HMRFoutput[[i]]$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)

R/spatial_genes.R

@@ -4557,7 +4557,7 @@ run_spatial_sim_tests_one_rep <- function(gobject,
     ...) {
     # 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)) {

R/spatial_interaction_spot.R

@@ -1390,7 +1390,7 @@ plotICFSpot <- function(gobject,
     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)) {