PCHeatmap: Principal component heatmap

In nukappa/seurat_v2: Seurat : R toolkit for single cell genomics

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, 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.