ClusteringScatterPlot: Visualize the clustering over nonliner dimensionality...

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

Description

ClusteringScatterPlot function enables visualizing the clustering over nonliner dimensionality reduction (t-SNE or UMAP).

Usage

ClusteringScatterPlot.SingleCellExperiment(
  object,
  clustering.type,
  return.plot,
  dim.reduction.type,
  point.size,
  title,
  show.legend
)

## S4 method for signature 'SingleCellExperiment'
ClusteringScatterPlot(
  object,
  clustering.type = "manual",
  return.plot = FALSE,
  dim.reduction.type = "",
  point.size = 0.7,
  title = "",
  show.legend = TRUE
)

Arguments

object	of SingleCellExperiment class
clustering.type	"manual" or "optimal". "manual" refers to the clustering formed using the "SelectKClusters" function and "optimal" to the clustering formed using the "CalcSilhInfo" function. Default is "manual".
return.plot	a logical denoting whether to return the ggplot2 object. Default is FALSE.
dim.reduction.type	"tsne" or "umap". Default is "tsne".
point.size	point size. Default is Default is 0.7.
title	text to write above the plot
show.legend	whether to show the legend on the right side of the plot. Default is TRUE.

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 <- HierarchicalClustering(sce)
sce <- SelectKClusters(sce,K=5)
sce <- RunTSNE(sce)
ClusteringScatterPlot(sce,"manual",dim.reduction.type="tsne")
sce <- RunUMAP(sce)
ClusteringScatterPlot(sce,"manual",dim.reduction.type="umap")

ILoReg documentation built on Nov. 8, 2020, 8:20 p.m.