DimPlotMC: Seurat DimPlot for MCA like Dimensionality Reduction

In cbl-imagine/CellID: Unbiased Extraction of Single Cell Identity using Multiple Correspondence Analysis

Description

Small modification of the regular Seurat DimPlot function to enable plotting features for mca like dimensionality reduction.

Usage

DimPlotMC(X, reduction = "mca", dims = c(1, 2), features = NULL,
  size.feature = 2, size.feature.text = 5, as.text = FALSE, ...)

Arguments

X	a seurat object
reduction	Which dimensionality reduction to use. If not specified, searches for mca.
dims	Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions
features	character vector of features to plot, must be present in the specified dimension loadings
size.feature	integer indicating size of geom_point for features
size.feature.text	integer indicating size of geom_text for features
as.text	logical indicating as to include text label for feature plotting, will produce warning if TRUE and length(features) > 50
...	Other arguments passed to DimPlot

Value

A ggplot object

Examples

seuratPbmc <- RunMCA(seuratPbmc, nmcs = 5)
seuratPbmc <- DimPlotMC(seuratPbmc,  features = Seurat::VariableFeatures(seuratPbmc))

cbl-imagine/CellID documentation built on July 22, 2020, 7:18 p.m.