pcaEnrichment: Visualize the PCA of enrichment values

In ncborcherding/escape: Easy single cell analysis platform for enrichment

Visualize the PCA of enrichment values

Description

This function allows to the user to examine the distribution of principal components run on the enrichment values.

Usage

pcaEnrichment(
  input.data,
  dimRed = NULL,
  x.axis = "PC1",
  y.axis = "PC2",
  facet.by = NULL,
  style = "point",
  add.percent.contribution = TRUE,
  display.factors = FALSE,
  number.of.factors = 10,
  palette = "inferno"
)

Arguments

input.data	PCA from performPCA.
dimRed	Name of the dimensional reduction to plot if data is a single-cell object.
x.axis	Component to plot on the x.axis.
y.axis	Component set to plot on the y.axis.
facet.by	Variable to facet the plot into n distinct graphs.
style	Return a "hex" bin plot or a "point"-based plot.
add.percent.contribution	Add the relative percent of contribution of the selected components to the axis labels.
display.factors	Add an arrow overlay to show the direction and magnitude of individual gene sets on the PCA dimensions.
number.of.factors	The number of gene.sets to display on the overlay.
palette	Colors to use in visualization - input any hcl.pals.

Value

ggplot2 object with PCA distribution

Examples

GS <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))
pbmc_small <- SeuratObject::pbmc_small
pbmc_small <- runEscape(pbmc_small, 
                        gene.sets = GS, 
                        min.size = NULL)
                        
pbmc_small <- performPCA(pbmc_small, 
                         assay = "escape")
                         
pcaEnrichment(pbmc_small,
              x.axis = "PC1",
              y.axis = "PC2",
              dimRed = "escape.PCA")

ncborcherding/escape documentation built on Dec. 1, 2024, 8:12 a.m.