DimHeatmap: Dimensional reduction heatmap

In atakanekiz/Seurat3.0: Tools 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

DimHeatmap(object, dims = 1, nfeatures = 30, cells = NULL,
  reduction = "pca", disp.min = -2.5, disp.max = NULL,
  balanced = TRUE, projected = FALSE, ncol = NULL, combine = TRUE,
  fast = TRUE, slot = "scale.data", assays = NULL)

Arguments

object	Seurat object
dims	Dimensions to plot
nfeatures	Number of genes to plot
cells	A list of cells to plot. If numeric, just plots the top cells.
reduction	Which dimmensional reduction to use
disp.min	Minimum display value (all values below are clipped)
disp.max	Maximum display value (all values above are clipped); defaults to 2.5 if slot is 'scale.data', 6 otherwise
balanced	Plot an equal number of genes with both + and - scores.
projected	Use the full projected dimensional reduction
ncol	Number of columns to plot
combine	Combine plots into a single gg object; note that if TRUE; themeing will not work when plotting multiple dimensions
fast	If true, use image to generate plots; faster than using ggplot2, but not customizable
slot	Data slot to use, choose from 'raw.data', 'data', or 'scale.data'
assays	A vector of assays to pull data from

Value

A ggplot object

See Also

image geom_raster

Examples

DimHeatmap(object = pbmc_small)

atakanekiz/Seurat3.0 documentation built on May 26, 2019, 2:33 a.m.