Seurat (version 2.3.4)

ICHeatmap: Independent component heatmap

Description

Draws a heatmap focusing on a principal component. Both cells and genes are sorted by their principal component scores. Allows for nice visualization of sources of heterogeneity in the dataset."()

Usage

ICHeatmap(object, ic.use = 1, cells.use = NULL, num.genes = 30,
  disp.min = -2.5, disp.max = 2.5, do.return = FALSE,
  col.use = PurpleAndYellow(), use.scale = TRUE, do.balanced = FALSE,
  remove.key = FALSE, label.columns = NULL, ...)

Arguments

object

Seurat object

ic.use

Components to use

cells.use

A list of cells to plot. If numeric, just plots the top cells.

num.genes

NUmber of genes to plot

disp.min

Minimum display value (all values below are clipped)

disp.max

Maximum display value (all values above are clipped)

do.return

If TRUE, returns plot object, otherwise plots plot object

col.use

Colors to plot.

use.scale

Default is TRUE: plot scaled data. If FALSE, plot raw data on the heatmap.

do.balanced

Plot an equal number of genes with both + and - scores.

remove.key

Removes the color key from the plot.

label.columns

Labels for columns

...

Extra parameters passed to DimHeatmap

Value

If do.return==TRUE, a matrix of scaled values which would be passed to heatmap.2. Otherwise, no return value, only a graphical output

Examples

# NOT RUN {
pbmc_small <- RunICA(object = pbmc_small, ics.compute = 25, print.results = FALSE)
ICHeatmap(object = pbmc_small)

# }

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