Description Usage Arguments Value See Also Examples
View source: R/visualization.R
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).
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object |
Seurat object |
assay |
Name of assay to use, defaults to the active assay |
features |
Input vector of features, or named list of feature vectors if feature-grouped panels are desired (replicates the functionality of the old SplitDotPlotGG) |
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 |
Factor to split the groups by (replicates the functionality
of the old SplitDotPlotGG);
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
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