FeatureHeatmap: Vizualization of multiple features

In mayer-lab/SeuratForMayer2018: Seurat : R Toolkit for Single Cell Genomics

Description

Similar to FeaturePlot, however, also splits the plot by visualizing each identity class separately.

Usage

FeatureHeatmap(object, features.plot, dim.1 = 1, dim.2 = 2,
  idents.use = NULL, pt.size = 2, cols.use = c("grey", "red"),
  pch.use = 16, reduction.use = "tsne", group.by = NULL,
  sep.scale = FALSE, do.return = FALSE, min.exp = -Inf, max.exp = Inf,
  rotate.key = FALSE, plot.horiz = FALSE, key.position = "right")

Arguments

object	Seurat object
features.plot	Vector of features to plot
dim.1	Dimension for x-axis (default 1)
dim.2	Dimension for y-axis (default 2)
idents.use	Which identity classes to display (default is all identity classes)
pt.size	Adjust point size for plotting
cols.use	Ordered vector of colors to use for plotting. Default is heat.colors(10).
pch.use	Pch for plotting
reduction.use	Which dimensionality reduction to use. Default is "tsne", can also be "pca", or "ica", assuming these are precomputed.
group.by	Group cells in different ways (for example, orig.ident)
sep.scale	Scale each group separately. Default is FALSE.
do.return	Return the ggplot2 object
min.exp	Min cutoff for scaled expression value, supports quantiles in the form of 'q##' (see FeaturePlot)
max.exp	Max cutoff for scaled expression value, supports quantiles in the form of 'q##' (see FeaturePlot)
rotate.key	rotate the legend
plot.horiz	rotate the plot such that the features are columns, groups are the rows
key.position	position of the legend ("top", "right", "bottom", "left")

Details

Particularly useful for seeing if the same groups of cells co-exhibit a common feature (i.e. co-express a gene), even within an identity class. Best understood by example.

Value

No return value, only a graphical output

See Also

FeaturePlot

mayer-lab/SeuratForMayer2018 documentation built on May 25, 2019, 9:34 p.m.