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,
assay = NULL,
features,
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.min = NA,
scale.max = NA
)

## Arguments

object Seurat object Name of assay to use, defaults to the active assay Input vector of features, or named list of feature vectors if feature-grouped panels are desired (replicates the functionality of the old SplitDotPlotGG) 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) Minimum scaled average expression threshold (everything smaller will be set to this) Maximum scaled average expression threshold (everything larger will be set to this) 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. Scale the size of the points, similar to cex Identity classes to include in plot (default is all) Factor to group the cells by Factor to split the groups by (replicates the functionality of the old SplitDotPlotGG); see FetchData for more details Whether to order identities by hierarchical clusters based on given features, default is FALSE Determine whether the data is scaled, TRUE for default Scale the size of the points by 'size' or by 'radius' Set lower limit for scaling, use NA for default Set upper limit for scaling, use NA for default

## Value

A ggplot object

RColorBrewer::brewer.pal.info
data("pbmc_small")