Draws a ridge plot of single cell data (gene expression, metrics, PC scores, etc.)
RidgePlot(
object,
features,
cols = NULL,
idents = NULL,
sort = FALSE,
assay = NULL,
group.by = NULL,
y.max = NULL,
same.y.lims = FALSE,
log = FALSE,
ncol = NULL,
slot = "data",
stack = FALSE,
combine = TRUE,
fill.by = "feature"
)
Seurat object
Features to plot (gene expression, metrics, PC scores, anything that can be retreived by FetchData)
Colors to use for plotting
Which classes to include in the plot (default is all)
Sort identity classes (on the x-axis) by the average expression of the attribute being potted, can also pass 'increasing' or 'decreasing' to change sort direction
Name of assay to use, defaults to the active assay
Group (color) cells in different ways (for example, orig.ident)
Maximum y axis value
Set all the y-axis limits to the same values
plot the feature axis on log scale
Number of columns if multiple plots are displayed
Use non-normalized counts data for plotting
Horizontally stack plots for each feature
Combine plots into a single patchworked
ggplot object. If FALSE
, return a list of ggplot
Color violins/ridges based on either 'feature' or 'ident'
A patchworked
ggplot object if
combine = TRUE
; otherwise, a list of ggplot objects
# NOT RUN {
data("pbmc_small")
RidgePlot(object = pbmc_small, features = 'PC_1')
# }
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