GeneScatterPlot: Visualize gene expression over nonlinear dimensionality...

In elolab/ILoReg: ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data

Visualize gene expression over nonlinear dimensionality reduction

Description

GeneScatterPlot enables visualizing gene expression of a gene over nonlinear dimensionality reduction with t-SNE or UMAP.

Usage

GeneScatterPlot.SingleCellExperiment(
  object,
  genes,
  return.plot,
  dim.reduction.type,
  point.size,
  title,
  plot.expressing.cells.last,
  nrow,
  ncol
)

## S4 method for signature 'SingleCellExperiment'
GeneScatterPlot(
  object,
  genes = "",
  return.plot = FALSE,
  dim.reduction.type = "tsne",
  point.size = 0.7,
  title = "",
  plot.expressing.cells.last = FALSE,
  nrow = NULL,
  ncol = NULL
)

Arguments

object	of SingleCellExperiment class
genes	a character vector of the genes to be visualized
return.plot	whether to return the ggplot2 object or just draw it (default FALSE)
dim.reduction.type	"tsne" or "umap" (default "tsne")
point.size	point size (default 0.7)
title	text to write above the plot
plot.expressing.cells.last	whether to plot the expressing genes last to make the points more visible
nrow	a positive integer that specifies the number of rows in the plot grid. Default is NULL.
ncol	a positive integer that specifies the number of columns in the plot grid. Default is NULL.

Value

ggplot2 object if return.plot=TRUE

Examples

library(SingleCellExperiment)
sce <- SingleCellExperiment(assays = list(logcounts = pbmc3k_500))
sce <- PrepareILoReg(sce)
## These settings are just to accelerate the example, use the defaults.
sce <- RunParallelICP(sce,L=2,threads=1,C=0.1,k=5,r=1)
sce <- RunPCA(sce,p=5)
sce <- RunTSNE(sce)
GeneScatterPlot(sce,"CD14",dim.reduction.type="tsne")
sce <- RunUMAP(sce)
GeneScatterPlot(sce,"CD14",dim.reduction.type="umap")

elolab/ILoReg documentation built on March 28, 2022, 1:17 a.m.