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 = NULL,
  scale = TRUE,
  n.dim = 1:10,
  reduction.name = "escape.PCA",
  reduction.key = "PCA"
)

Arguments

input.data	Enrichment output from escape.matrix or runEscape.
assay	Name of the assay to plot if data is a single-cell object.
scale	Standardize the enrichment value (TRUE) or not (FALSE)
n.dim	The number of components to calculate.
reduction.name	Name of the reduced dimensions object to add if data is a single-cell object.
reduction.key	Name of the key to use with the components.

Value

single-cell object or list with PCA components to plot.

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")

ncborcherding/escape documentation built on May 13, 2024, 9:37 p.m.