performPCA: Perform Principal Component Analysis on Enrichment Data

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

Perform Principal Component Analysis on Enrichment Data

Description

This function allows users to calculate the principal components for the gene set enrichment values. For single-cell data, the PCA will be stored with the dimensional reductions. If a matrix is used as input, the output is a list for further plotting. Alternatively, users can use functions for PCA calculations based on their desired workflow in lieu of using performPCA, but will not be compatible with downstream pcaEnrichment visualization.

Usage

performPCA(
  input.data,
  assay = "escape",
  scale = TRUE,
  n.dim = 10,
  reduction.name = "escape.PCA",
  reduction.key = "escPC_"
)

Arguments

input.data	Output of escape.matrix or a single‑cell object previously processed by runEscape.
assay	Name of the assay holding enrichment scores when 'input.data' is a single‑cell object. Ignored otherwise.
scale	Logical; if 'TRUE' standardises each gene-set column before PCA.
n.dim	Integer >= 1 or vector; the **largest** value sets the number of principal components to compute / keep.
reduction.name, reduction.key	Names used when writing back to a Seurat / SCE object.

Value

*If* 'input.data' is a single-cell object, the same object with a new dimensional-reduction slot. *Otherwise* a list with 'PCA', 'eigen_values', 'contribution', and 'rotation'.

Examples

gs <- list(Bcells = c("MS4A1", "CD79B", "CD79A", "IGH1", "IGH2"),
           Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))

pbmc <- SeuratObject::pbmc_small |>
  runEscape(gene.sets = gs,
            min.size = NULL)           
                        
pbmc <- performPCA(pbmc, 
                   assay = "escape")

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