Intuitive way of visualizing how gene 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 of 'expressing' cells (green is high). Splits the cells into two groups based on a grouping variable. Still in BETA
SplitDotPlotGG(object, grouping.var, genes.plot, gene.groups,
cols.use = c("green", "red"), col.min = -2.5, col.max = 2.5,
dot.min = 0, dot.scale = 6, group.by, plot.legend = FALSE,
do.return = FALSE, x.lab.rot = FALSE)Seurat object
Grouping variable for splitting the dataset
Input vector of genes
Add labeling bars to the top of the plot
colors to plot
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 0.05).
Scale the size of the points, similar to cex
Factor to group the cells by
plots the legends
Return ggplot2 object
Rotate x-axis labels
default, no return, only graphical output. If do.return=TRUE, returns a ggplot2 object