PCHeatmap: Principal component heatmap

Description Usage Arguments Value

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

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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, label.columns = NULL, ...)

Arguments

object

Seurat object

pc.use

Principal components to use

cells.use

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

label.columns

Whether to label the columns. Default is TRUE for 1 PC, FALSE for > 1 PC

...

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


nukappa/seurat_v2 documentation built on May 24, 2019, 9:57 a.m.