DimPlot: Dimension Reduction Plots

In bioinfoDZ/RISC: Robust Integration of Single-Cell RNA-Seq Datasets

Dimension Reduction Plots

Description

The Dimension Reduction plots are widespread in scRNA-seq data analysis. Here, the "DimPlot" function not only can make plots for factor labels of individual cells but also can show gene expression values of each cell.

Usage

DimPlot(
  object,
  slot = "cell.umap",
  colFactor = NULL,
  genes = NULL,
  legend = TRUE,
  Colors = NULL,
  size = 0.5,
  Alpha = 0.8,
  plot.ncol = NULL,
  exp.range = NULL,
  exp.col = "firebrick2",
  label = FALSE,
  adjust.label = 0.25,
  label.font = 5
)

Arguments

object	RISC object: a framework dataset.
slot	The dimension_reduction slot for drawing the plots. The default is "cell.umap" under RISC object "DimReduction" item for UMAP plot, but the customer can add new dimension_reduction method under DimReduction and use it.
colFactor	Use the factor (column name) in the coldata to make a dimension_reduction plot, but each time only one column name can be inputted.
genes	Use the gene expression values (gene symbol) to make dimension reduction plot, each time more than one genes can be inputted.
legend	Whether a legend shown at dimension_reduction plot.
Colors	The users can use their own colors (color vector). The default of the "tSNEPlot" funciton will assign colors automatically.
size	Choose the size of dots at dimension_reduction plot, the default size is 0.5.
Alpha	Whether show transparency of individual points, the default is 0.8.
plot.ncol	If the users input more than one genes, the arrangement of multiple dimension_reduction plot depends on this parameter.
exp.range	The gene expression cutoff for plot, e.g. "c(0, 1.5)" for expression level between 0 and 1.5.
exp.col	The gradient color for gene expression.
label	Whether label the clusters or cell populations in the plot.
adjust.label	The adjustment of the label position.
label.font	The font size for the label.

References

Wickham, H. (2016)

Auguie, B. (2015)

Examples

# RISC object
obj0 = raw.mat[[3]]
obj0 = scPCA(obj0, npc = 10)
obj0 = scUMAP(obj0, npc = 3)
DimPlot(obj0, slot = "cell.umap", colFactor = 'Group', size = 2, label = TRUE)
DimPlot(obj0, genes = c('Gene718', 'Gene325', 'Gene604'), size = 2)

bioinfoDZ/RISC documentation built on March 30, 2024, 9:19 p.m.