Seurat (version 2.3.4)

DimHeatmap: Dimensional reduction 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

DimHeatmap(object, assay.use = "RNA", reduction.type = "pca", dim.use = 1,
  cells.use = NULL, num.genes = 30, use.full = FALSE, 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, check.plot = TRUE, ...)

Arguments

object

Seurat object.

assay.use

Assay to pull from - default is RNA

reduction.type

Which dimmensional reduction t use

dim.use

Dimensions to plot

cells.use

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

num.genes

NUmber of genes to plot

use.full

Use the full PCA (projected PCA). Default is FALSE

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

Color 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

check.plot

Check that plotting will finish in a reasonable amount of time

...

Extra parameters for heatmap plotting.

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 {
DimHeatmap(object = pbmc_small)

# }