PCHeatmap: Principal component heatmap

In mayer-lab/SeuratForMayer2018: 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 = PurpleAndYellow(), use.scale = TRUE, do.balanced = FALSE,
  remove.key = FALSE, label.columns = NULL, ...)

Arguments

object	Seurat object.
pc.use	PCs 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	Whether to label the columns. Default is TRUE for 1 PC, FALSE for > 1 PC
...	Extra parameters for 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

mayer-lab/SeuratForMayer2018 documentation built on May 25, 2019, 9:34 p.m.