add shuffle param to FeatureScatter

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: Seurat
-Version: 4.0.1.9002
+Version: 4.0.1.9003
 Date: 2021-03-31
 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.

NEWS.md

@@ -1,6 +1,7 @@
 # Seurat develop
 ## Added
 - New `AddAzimuthScores()` function
+- Add `shuffle` parameter to `FeatureScatter()` ([#4280](https://github.com/satijalab/seurat/pull/4280))
 
 ## Changes
 - Preserve feature metadata when converting from `SingleCellExperiment` to `SeuratObject` class

R/visualization.R

@@ -1854,6 +1854,9 @@ CellScatter <- function(
 #' be metrics, PC scores, etc. - anything that can be retreived with FetchData
 #' @param feature2 Second feature to plot.
 #' @param cells Cells to include on the scatter plot.
+#' @param shuffle Whether to randomly shuffle the order of points. This can be
+#' useful for crowded plots if points of interest are being buried. (default is FALSE)
+#' @param seed Sets the seed if randomly shuffling the order of points.
 #' @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 cols Colors to use for identity class plotting.
@@ -1887,6 +1890,8 @@ FeatureScatter <- function(
   feature1,
   feature2,
   cells = NULL,
+  shuffle = FALSE,
+  seed = 1,
   group.by = NULL,
   cols = NULL,
   pt.size = 1,
@@ -1899,6 +1904,10 @@ FeatureScatter <- function(
   raster = NULL
 ) {
   cells <- cells %||% colnames(x = object)
+  if (isTRUE(x = shuffle)) {
+    set.seed(seed = seed)
+    cells <- sample(x = cells)
+  }
   object[['ident']] <- Idents(object = object)
   group.by <- group.by %||% 'ident'
   data <-  FetchData(