DotPlot: Dot plot visualization

In lambdamoses/SeuratBasics: Slimmed down version of Seurat

Description

Intuitive way of visualizing how feature expression changes across different identity classes (clusters). The size of the dot encodes the percentage of cells within a class, while the color encodes the AverageExpression level across all cells within a class (blue is high).

Usage

DotPlot(
  object,
  assay = NULL,
  features,
  cols = c("lightgrey", "blue"),
  col.min = -2.5,
  col.max = 2.5,
  dot.min = 0,
  dot.scale = 6,
  group.by = NULL,
  split.by = NULL,
  scale.by = "radius",
  scale.min = NA,
  scale.max = NA
)

Arguments

object	Seurat object
assay	Name of assay to use, defaults to the active assay
features	Input vector of features
cols	Colors to plot, can pass a single character giving the name of a palette from RColorBrewer::brewer.pal.info
col.min	Minimum scaled average expression threshold (everything smaller will be set to this)
col.max	Maximum scaled average expression threshold (everything larger will be set to this)
dot.min	The fraction of cells at which to draw the smallest dot (default is 0). All cell groups with less than this expressing the given gene will have no dot drawn.
dot.scale	Scale the size of the points, similar to cex
group.by	Factor to group the cells by
split.by	Factor to split the groups by (replicates the functionality of the old SplitDotPlotGG); see FetchData for more details
scale.by	Scale the size of the points by 'size' or by 'radius'
scale.min	Set lower limit for scaling, use NA for default
scale.max	Set upper limit for scaling, use NA for default

Value

A ggplot object

See Also

RColorBrewer::brewer.pal.info

Examples

cd_genes <- c("CD247", "CD3E", "CD9")
DotPlot(object = pbmc_small, features = cd_genes)
pbmc_small[['groups']] <- sample(x = c('g1', 'g2'), size = ncol(x = pbmc_small), replace = TRUE)
DotPlot(object = pbmc_small, features = cd_genes, split.by = 'groups')

lambdamoses/SeuratBasics documentation built on May 6, 2020, 9:32 a.m.