View source: R/visualization.R
DotPlot | R Documentation |
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
)
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
|
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 |
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 |
A ggplot object
RColorBrewer::brewer.pal.info
data("pbmc_small")
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')
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