Dot plot visualization

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).

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

Arguments

object

Seurat object

features

Input vector of features, or named list of feature vectors if feature-grouped panels are desired (replicates the functionality of the old SplitDotPlotGG)

assay

Name of assay to use, defaults to the active assay

cols

Colors to plot: the name of a palette from RColorBrewer::brewer.pal.info, a pair of colors defining a gradient, or 3+ colors defining multiple gradients (if split.by is set)

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

idents

Identity classes to include in plot (default is all)

group.by

Factor to group the cells by

split.by

A factor in object metadata to split the plot by, pass 'ident' to split by cell identity' see FetchData for more details

cluster.idents

Whether to order identities by hierarchical clusters based on given features, default is FALSE

scale

Determine whether the data is scaled, TRUE for default

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

data("pbmc_small")
cd_genes <- c("CD247", "CD3E", "CD9")
DotPlot(object = pbmc_small, features = cd_genes)
#> Warning: Scaling data with a low number of groups may produce misleading results

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')