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 = c("point", "hex"),
  add.percent.contribution = TRUE,
  display.factors = FALSE,
  number.of.factors = 10,
  palette = "inferno"
)

Arguments

input.data	Single‑cell object (Seurat / SCE) **or** the raw list returned by ['performPCA()'].
dimRed	Name of the dimensional‑reduction slot to pull from a single‑cell object. Ignored when 'input.data' is the list output.
x.axis, y.axis	Character vectors naming the PCs to display (e.g. "PC1").
facet.by	Metadata column to facet plot.
style	"point" (default) or "hex".
add.percent.contribution	Include percent variance explained in axis labels.
display.factors	Draw arrows for the top gene‑set loadings.
number.of.factors	Integer; how many loadings to display if 'display.factors = TRUE'.
palette	Character. Any palette from hcl.pals. #' @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")

Value

A **ggplot2** object.

ncborcherding/escape documentation built on June 12, 2025, 1 p.m.