Merge pull request #9560 from samuel-marsh/typo_combined

Typo combined

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: Seurat
-Version: 5.1.0.9013
+Version: 5.1.0.9014
 Title: Tools for Single Cell Genomics
 Description: A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.
 Authors@R: c(

R/clustering.R

@@ -190,7 +190,7 @@ FindSubCluster <- function(
 #' embeddings from dimensional reductions.
 #'
 #' @param object The object used to calculate knn
-#' @param nn.idx k near neighbour indices. A cells x k matrix.
+#' @param nn.idx k near neighbor indices. A cells x k matrix.
 #' @param assay Assay used for prediction
 #' @param reduction Cell embedding of the reduction used for prediction
 #' @param dims Number of dimensions of cell embedding
@@ -522,7 +522,7 @@ FindClusters.Seurat <- function(
 #' cosine, manhattan, and hamming
 #' @param n.trees More trees gives higher precision when using annoy approximate
 #' nearest neighbor search
-#' @param nn.eps Error bound when performing nearest neighbor seach using RANN;
+#' @param nn.eps Error bound when performing nearest neighbor search using RANN;
 #' default of 0.0 implies exact nearest neighbor search
 #' @param verbose Whether or not to print output to the console
 #' @param l2.norm Take L2Norm of the data
@@ -868,7 +868,7 @@ FindNeighbors.Seurat <- function(
 # @param n.trees More trees gives higher precision when querying
 # @param k Number of neighbors
 # @param search.k During the query it will inspect up to search_k nodes which
-# gives you a run-time tradeoff between better accuracy and speed.
+# gives you a run-time trade off between better accuracy and speed.
 # @param include.distance Include the corresponding distances
 # @param index optional index object, will be recomputed if not provided
 #
@@ -928,7 +928,7 @@ AnnoyBuildIndex <- function(data, metric = "euclidean", n.trees = 50) {
 # @param query A set of data to be queried against the index
 # @param k Number of neighbors
 # @param search.k During the query it will inspect up to search_k nodes which
-# gives you a run-time tradeoff between better accuracy and speed.
+# gives you a run-time trade off between better accuracy and speed.
 # @param include.distance Include the corresponding distances in the result
 #
 # @return A list with 'nn.idx' (for each element in 'query', the index of the

R/dimensional_reduction.R

@@ -10,7 +10,7 @@ NULL
 #'
 #' Randomly permutes a subset of data, and calculates projected PCA scores for
 #' these 'random' genes. Then compares the PCA scores for the 'random' genes
-#' with the observed PCA scores to determine statistical signifance. End result
+#' with the observed PCA scores to determine statistical significance. End result
 #' is a p-value for each gene's association with each principal component.
 #'
 #' @param object Seurat object
@@ -276,7 +276,7 @@ PCASigGenes <- function(
 #' data("pbmc_small")
 #' pbmc_small
 #' pbmc_small <- ProjectDim(object = pbmc_small, reduction = "pca")
-#' # Vizualize top projected genes in heatmap
+#' # Visualize top projected genes in heatmap
 #' DimHeatmap(object = pbmc_small, reduction = "pca", dims = 1, balanced = TRUE)
 #'
 ProjectDim <- function(
@@ -1693,8 +1693,8 @@ RunUMAP.Neighbor <- function(
 #' be selected based on the size of the input dataset (200 for large datasets, 500 for small).
 #' @param learning.rate The initial learning rate for the embedding optimization.
 #' @param min.dist This controls how tightly the embedding is allowed compress points together.
-#' Larger values ensure embedded points are moreevenly distributed, while smaller values allow the
-#' algorithm to optimise more accurately with regard to local structure. Sensible values are in
+#' Larger values ensure embedded points are more evenly distributed, while smaller values allow the
+#' algorithm to optimize more accurately with regard to local structure. Sensible values are in
 #' the range 0.001 to 0.5.
 #' @param spread The effective scale of embedded points. In combination with min.dist this
 #' determines how clustered/clumped the embedded points are.
@@ -1721,7 +1721,7 @@ RunUMAP.Neighbor <- function(
 #' @param uwot.sgd Set \code{uwot::umap(fast_sgd = TRUE)}; see \code{\link[uwot]{umap}} for more details
 #' @param metric.kwds A dictionary of arguments to pass on to the metric, such as the p value for
 #' Minkowski distance. If NULL then no arguments are passed on.
-#' @param angular.rp.forest Whether to use an angular random projection forest to initialise the
+#' @param angular.rp.forest Whether to use an angular random projection forest to initialize the
 #' approximate nearest neighbor search. This can be faster, but is mostly on useful for metric that
 #' use an angular style distance such as cosine, correlation etc. In the case of those metrics
 #' angular forests will be chosen automatically.

R/integration.R

@@ -7484,7 +7484,7 @@ ProjectDimReduc <- function(query,
 #' @param bridge.query.assay Assay name for bridge used for query mapping. ATAC by default
 #' @param supervised.reduction Type of supervised dimensional reduction to be performed
 #' for integrating the bridge and query.
-#' #' Options are:
+#' Options are:
 #' \itemize{
 #'    \item{slsi: Perform supervised LSI as the dimensional reduction for
 #'    the bridge-query integration}
@@ -7810,7 +7810,7 @@ FindBridgeIntegrationAnchors <- function(
 #'
 #' This is a convenience wrapper function around the following three functions
 #' that are often run together when perform integration.
-#' #' \code{\link{FindIntegrationAnchors}}, \code{\link{RunPCA}},
+#' \code{\link{FindIntegrationAnchors}}, \code{\link{RunPCA}},
 #' \code{\link{IntegrateEmbeddings}}.
 #'
 #' @inheritParams FindIntegrationAnchors

R/objects.R

@@ -330,10 +330,10 @@ VisiumV1 <- setClass(
 
 #' The VisiumV2 class
 #'
-#' The VisiumV2 class represents spatial information from the 10X Genomics 
-#' Visium HD platform - it can also accomodate data from the standard 
+#' The VisiumV2 class represents spatial information from the 10X Genomics
+#' Visium HD platform - it can also accomodate data from the standard
 #' Visium platform
-#' 
+#'
 #' @slot image A three-dimensional array with PNG image data, see
 #' \code{\link[png]{readPNG}} for more details
 #' @slot scale.factors An object of class \code{\link{scalefactors}}; see
@@ -1176,7 +1176,7 @@ as.Seurat.SingleCellExperiment <- function(
   CheckDots(...)
   if (!PackageCheck('SingleCellExperiment', error = FALSE)) {
     stop(
-      "Please install SingleCellExperiment from Bioconductor before converting to a SingeCellExperiment object.",
+      "Please install SingleCellExperiment from Bioconductor before converting to a SingleCellExperiment object.",
       "\nhttps://bioconductor.org/packages/SingleCellExperiment/",
       call. = FALSE
     )
@@ -1319,7 +1319,7 @@ as.Seurat.SingleCellExperiment <- function(
 as.SingleCellExperiment.Seurat <- function(x, assay = NULL, ...) {
   CheckDots(...)
   if (!PackageCheck('SingleCellExperiment', error = FALSE)) {
-    stop("Please install SingleCellExperiment from Bioconductor before converting to a SingeCellExperiment object")
+    stop("Please install SingleCellExperiment from Bioconductor before converting to a SingleCellExperiment object")
   }
   assay <- assay %||% Assays(object = x)
   if (!all(assay %in% Assays(object = x))) {
@@ -1603,7 +1603,7 @@ GetImage.VisiumV1 <- function(
   ...
 ) {
   mode <- match.arg(arg = mode)
-  
+
   image <- slot(object = object, name = 'image')
 
   image <- switch(
@@ -1793,8 +1793,8 @@ Radius.STARmap <- function(object, ...) {
 }
 
 #'
-#' @param scale A factor to scale the radius by; one of: "hires", 
-#' "lowres", or \code{NULL} for the unscaled value.  
+#' @param scale A factor to scale the radius by; one of: "hires",
+#' "lowres", or \code{NULL} for the unscaled value.
 #'
 #' @rdname Radius
 #' @concept objects

R/preprocessing.R

@@ -32,7 +32,7 @@ globalVariables(
 #' @param object Seurat object
 #' @param barcode.column Column to use as proxy for barcodes ("nCount_RNA" by default)
 #' @param group.column Column to group by ("orig.ident" by default)
-#' @param threshold.high Ignore barcodes of rank above thisf threshold in inflection calculation
+#' @param threshold.high Ignore barcodes of rank above this threshold in inflection calculation
 #' @param threshold.low Ignore barcodes of rank below this threshold in inflection calculation
 #'
 #' @return Returns Seurat object with a new list in the `tools` slot, `CalculateBarcodeInflections` with values:
@@ -4036,7 +4036,7 @@ SubsetByBarcodeInflections <- function(object) {
 #'   \item \dQuote{\code{mean.var.plot}} (mvp): First, uses a function to
 #'     calculate average expression (mean.function) and dispersion
 #'     (dispersion.function) for each feature. Next, divides features into
-#'     \code{num.bin} (deafult 20) bins based on their average expression,
+#'     \code{num.bin} (default 20) bins based on their average expression,
 #'     and calculates z-scores for dispersion within each bin. The purpose of
 #'     this is to identify variable features while controlling for the
 #'     strong relationship between variability and average expression
@@ -4061,7 +4061,7 @@ SubsetByBarcodeInflections <- function(object) {
 #'     x-axis (default)
 #'   \item \dQuote{\code{equal_frequency}}: each bin contains an equal number
 #'     of features (can increase statistical power to detect overdispersed
-#'     eatures at high expression values, at the cost of reduced resolution
+#'     features at high expression values, at the cost of reduced resolution
 #'     along the x-axis)
 #' }
 #' @param verbose show progress bar for calculations

R/visualization.R

@@ -581,7 +581,7 @@ RidgePlot <- function(
 #' @param pt.size Point size for points
 #' @param alpha Alpha value for points
 #' @param split.by A factor in object metadata to split the plot by, pass 'ident'
-#'  to split by cell identity'
+#' to split by cell identity
 #' @param split.plot  plot each group of the split violin plots by multiple or
 #' single violin shapes.
 #' @param adjust Adjust parameter for geom_violin
@@ -798,7 +798,7 @@ ColorDimSplit <- function(
 #' @param group.by Name of one or more metadata columns to group (color) cells by
 #' (for example, orig.ident); pass 'ident' to group by identity class
 #' @param split.by A factor in object metadata to split the plot by, pass 'ident'
-#'  to split by cell identity'
+#' to split by cell identity
 #' @param shape.by If NULL, all points are circles (default). You can specify any
 #' cell attribute (that can be pulled with FetchData) allowing for both
 #' different colors and different shapes on cells.  Only applicable if \code{raster = FALSE}.
@@ -1015,7 +1015,7 @@ DimPlot <- function(
 #' @param min.cutoff,max.cutoff Vector of minimum and maximum cutoff values for each feature,
 #'  may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10')
 #' @param split.by A factor in object metadata to split the plot by, pass 'ident'
-#'  to split by cell identity'
+#' to split by cell identity
 #' @param keep.scale How to handle the color scale across multiple plots. Options are:
 #' \itemize{
 #'   \item \dQuote{feature} (default; by row/feature scaling): The plots for
@@ -1024,7 +1024,7 @@ DimPlot <- function(
 #'   \item \dQuote{all} (universal scaling): The plots for all features and
 #'     conditions are scaled to the maximum expression value for the feature
 #'     with the highest overall expression
-#'   \item \code{all} (no scaling): Each individual plot is scaled to the
+#'   \item \code{NULL} (no scaling): Each individual plot is scaled to the
 #'     maximum expression value of the feature in the condition provided to
 #'     \code{split.by}. Be aware setting \code{NULL} will result in color
 #'     scales that are not comparable between plots
@@ -1970,7 +1970,7 @@ CellScatter <- function(
 #' @param pt.size Size of the points on the plot
 #' @param shape.by Ignored for now
 #' @param split.by A factor in object metadata to split the feature plot by, pass 'ident'
-#'  to split by cell identity'
+#' to split by cell identity
 #' @param span Spline span in loess function call, if \code{NULL}, no spline added
 #' @param smooth Smooth the graph (similar to smoothScatter)
 #' @param slot Slot to pull data from, should be one of 'counts', 'data', or 'scale.data'
@@ -3633,9 +3633,9 @@ ISpatialDimPlot <- function(
           coords.hover <- hovered[1, colnames(coords)[1:2]] / scale.factor
           group.hover <- hovered[1, group.by]
           sprintf(
-            "Cell: %s, Group: %s, Coordinates: (%.2f, %.2f)", 
-            cell.hover, 
-            group.hover, 
+            "Cell: %s, Group: %s, Coordinates: (%.2f, %.2f)",
+            cell.hover,
+            group.hover,
             coords.hover[[1]],
             coords.hover[[2]]
           )
@@ -3867,7 +3867,7 @@ ISpatialFeaturePlot <- function(
 #' default, ggplot2 assigns colors
 #' @param image.alpha Adjust the opacity of the background images. Set to 0 to
 #' remove.
-#' @param image.scale Choose the scale factor ("lowres"/"hires") to apply in 
+#' @param image.scale Choose the scale factor ("lowres"/"hires") to apply in
 #' order to matchthe plot with the specified `image` - defaults to "lowres"
 #' @param crop Crop the plot in to focus on points plotted. Set to \code{FALSE} to show
 #' entire background image.
@@ -3911,8 +3911,8 @@ ISpatialFeaturePlot <- function(
 #' @param alpha Controls opacity of spots. Provide as a vector specifying the
 #' min and max for SpatialFeaturePlot. For SpatialDimPlot, provide a single
 #' alpha value for each plot.
-#' @param shape Control the shape of the spots - same as the ggplot2 parameter. 
-#' The default is 21, which plots circles - use 22 to plot squares. 
+#' @param shape Control the shape of the spots - same as the ggplot2 parameter.
+#' The default is 21, which plots circles - use 22 to plot squares.
 #' @param stroke Control the width of the border around the spots
 #' @param interactive Launch an interactive SpatialDimPlot or SpatialFeaturePlot
 #' session, see \code{\link{ISpatialDimPlot}} or
@@ -4374,7 +4374,7 @@ BarcodeInflectionsPlot <- function(object) {
 #' @param idents Identity classes to include in plot (default is all)
 #' @param group.by Factor to group the cells by
 #' @param split.by A factor in object metadata to split the plot by, pass 'ident'
-#'  to split by cell identity'
+#' to split by cell identity
 #' see \code{\link{FetchData}} for more details
 #' @param cluster.idents Whether to order identities by hierarchical clusters
 #' based on given features, default is FALSE
@@ -8979,16 +8979,16 @@ SingleRasterMap <- function(
 #' colors
 #' @param image.alpha Adjust the opacity of the background images. Set to 0 to
 #' remove.
-#' @param image.scale Choose the scale factor ("lowres"/"hires") to apply in 
+#' @param image.scale Choose the scale factor ("lowres"/"hires") to apply in
 #' order to matchthe plot with the specified `image` - defaults to "lowres"
 #' @param pt.alpha Adjust the opacity of the points if plotting a
 #' \code{SpatialDimPlot}
 #' @param crop Crop the plot in to focus on points plotted. Set to \code{FALSE}
 #' to show entire background image.
 #' @param pt.size.factor Sets the size of the points relative to spot.radius
 #' @param stroke Control the width of the border around the spots
-#' @param shape Control the shape of the spots - same as the ggplot2 parameter. 
-#' The default is 21, which plots cirlces - use 22 to plot squares. 
+#' @param shape Control the shape of the spots - same as the ggplot2 parameter.
+#' The default is 21, which plots cirlces - use 22 to plot squares.
 #' @param col.by Mapping variable for the point color
 #' @param alpha.by Mapping variable for the point alpha value
 #' @param cells.highlight A list of character or numeric vectors of cells to