pcaEnrichment: Visualize the PCA of Enrichment Values
In ncborcherding/escape: Easy single cell analysis platform for enrichment

pcaEnrichment

R Documentation

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")