Seurat (version 1.2.1)

pcHeatmap: Principal 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

pcHeatmap(object, pc.use = 1, cells.use = NULL, num.genes = 30, use.full = FALSE, disp.min = -2.5, disp.max = 2.5, do.return = FALSE, col.use = pyCols, use.scale = TRUE, do.balanced = FALSE, remove.key = FALSE, ...)

Arguments

object

Seurat object

pc.use

Principal components to use

cells.use

Cells to include in the heatmap (default is all cells)

num.genes

Number of genes to return

use.full

Use the full PCA (projected PCA). Default i s FALSE

disp.min

Minimum display value (all values below are clipped)

disp.max

Maximum display value (all values above are clipped)

do.return

Default is FALSE. If TRUE, return a matrix of scaled values which would be passed to heatmap.2

col.use

Color palette to use

use.scale

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

do.balanced

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

remove.key

Removes the color key from the plot.

...

Additional parameters to heatmap.2. Common examples are cexRow and cexCol, which set row and column text sizes

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