Seurat (version 3.0.1)

DotPlot: Dot plot visualization

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

...

Ignored

Value

A ggplot object

See Also

RColorBrewer::brewer.pal.info

Examples

# NOT RUN {
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')

# }