RunCellGSEA: Run Gene Set Enrichment Analysis on cells

In RausellLab/CelliD: Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis

Run Gene Set Enrichment Analysis on cells

Description

Calculate cells gene specificty ranking and then perform geneset enrichment analysis (fgsea) on it. However, due to the very long running time of gene set enrichment analysis, we recommend the usage of RunCellHGT.

Usage

RunCellGSEA(
  X,
  pathways,
  reduction,
  dims,
  features,
  cells,
  nperm,
  minSize,
  maxSize,
  gseaParam,
  n.core
)

## S3 method for class 'Seurat'
RunCellGSEA(
  X,
  pathways,
  reduction = "mca",
  dims = seq(50),
  features = NULL,
  cells = NULL,
  nperm = 1000,
  minSize = 10,
  maxSize = 500,
  gseaParam = 0,
  n.core = 1
)

## S3 method for class 'SingleCellExperiment'
RunCellGSEA(
  X,
  pathways,
  reduction = "mca",
  dims = seq(50),
  features = NULL,
  cells = NULL,
  nperm = 1000,
  minSize = 10,
  maxSize = 500,
  gseaParam = 0,
  n.core = 1
)

Arguments

X	Seurat or SingleCellExperiment object
pathways	List of gene sets to check
reduction	Which dimensionality reduction to use, must be based on MCA.
dims	A vector of integers indicating which dimensions to use with reduction embeddings and loadings for distance calculation.
features	Character vector of feature names to subset feature coordinates. If not specified will take all features available from specified reduction Loadings.
cells	Character vector of cell names to subset cell coordinates. If not specified will take all features available from specified reduction Embeddings
nperm	Number of permutations to do. Minimial possible nominal p-value is about 1/nperm
minSize	Minimal size of a gene set to test. All pathways below the threshold are excluded.
maxSize	Maximal size of a gene set to test. All pathways above the threshold are excluded.
gseaParam	GSEA parameter value, all gene-level statis are raised to the power of 'gseaParam' before calculation of GSEA enrichment scores
n.core	A single integer to specify the number of core for parallelisation.

Value

A data.table with geneset enrichment analysis statistics.

Examples

seuratPbmc <- RunMCA(seuratPbmc, nmcs = 5)
GSEAResults <- RunCellGSEA(seuratPbmc, Hallmark, dims = 1:5)

RausellLab/CelliD documentation built on Jan. 12, 2024, 3:44 a.m.