ICHeatmap: Independent 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

ICHeatmap(object, ic.use = 1, cells.use = NULL, num.genes = 30,
  disp.min = -2.5, disp.max = 2.5, do.return = FALSE, col.use = pyCols,
  use.scale = TRUE, do.balanced = FALSE, ...)

Arguments

object	Seurat object
ic.use	Independent components to use
cells.use	Cells to include in the heatmap (default is all cells)
num.genes	Number of genes to return
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 - IC scores.
...	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.