Seurat: DotPlot – R documentation

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DotPlot

Dot plot visualization

Description

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).

Usage

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.by = "radius",
  scale.min = NA,
  scale.max = NA
)

Arguments

`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 `RColorBrewer::brewer.pal.info`, a pair of colors defining a gradient, or 3+ colors defining multiple gradients (if split.by is set)
`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 `FetchData` for more details
`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

Value

A ggplot object

Examples

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')

Seurat

Tools for Single Cell Genomics

v4.0.1

GPL-3 | file LICENSE

Authors

Andrew Butler [ctb] (<https://orcid.org/0000-0003-3608-0463>), Saket Choudhary [ctb] (<https://orcid.org/0000-0001-5202-7633>), Charlotte Darby [ctb] (<https://orcid.org/0000-0003-2195-5300>), Jeff Farrell [ctb], Christoph Hafemeister [ctb] (<https://orcid.org/0000-0001-6365-8254>), Yuhan Hao [ctb] (<https://orcid.org/0000-0002-1810-0822>), Paul Hoffman [aut, cre] (<https://orcid.org/0000-0002-7693-8957>), Jaison Jain [ctb] (<https://orcid.org/0000-0002-9478-5018>), Efthymia Papalexi [ctb] (<https://orcid.org/0000-0001-5898-694X>), Patrick Roelli [ctb], Rahul Satija [ctb] (<https://orcid.org/0000-0001-9448-8833>), Karthik Shekhar [ctb], Avi Srivastava [ctb] (<https://orcid.org/0000-0001-9798-2079>), Tim Stuart [ctb] (<https://orcid.org/0000-0002-3044-0897>), Kristof Torkenczy [ctb] (<https://orcid.org/0000-0002-4869-7957>), Shiwei Zheng [ctb] (<https://orcid.org/0000-0001-6682-6743>), Satija Lab and Collaborators [fnd]

Initial release

2021-03-17