Seurat (version 2.1.0)

FeatureHeatmap: Vizualization of multiple features

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")

Value

No return value, only a graphical output

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.

See Also

FeaturePlot

Examples

# NOT RUN {
pbmc_small
FeatureHeatmap(object = pbmc_small, features.plot = "PC1")

# }