RunCellHGT: Run HyperGeometric Test on cells

In Cortalak/cellID: Unbiased Extraction of Single Cell Identity using Multiple Correspondence Analysis

Description

Run HyperGeometric Test on cells

Usage

RunCellHGT(X, pathways, reduction, n.features, features, dims, minSize,
  log.trans, p.adjust)

## S3 method for class 'SingleCellExperiment'
RunCellHGT(X, pathways, reduction = "MCA",
  n.features = 200, features = NULL, dims = 1:50, minSize = 10,
  log.trans = T, p.adjust = T)

## S3 method for class 'Seurat'
RunCellHGT(X, pathways, reduction = "mca",
  n.features = 200, features = NULL, dims = 1:50, minSize = 10,
  log.trans = T, p.adjust = T)

Arguments

X	Seurat or SingleCellExperiment object with mca performed
pathways	geneset to perform hypergeometric test on (named list of genes)
reduction	name of the MCA reduction
n.features	integer of top n features to consider for hypergeometric test
features	vector of features to calculate the gene ranking by default will take everything in the selected mca reduction.
dims	MCA dimensions to use to compute n.features top genes.
minSize	minimum number of overlapping genes in geneset and
log.trans	if TRUE tranform the pvalue matrix with -log10 and convert it to sparse matrix
p.adjust	if TRUE apply Benjamini Hochberg correctionto p-value

Value

a matrix of benjamini hochberg adjusted pvalue pvalue or a sparse matrix of (-log10+1) benjamini hochberg adjusted pvalue

Examples

seuratPbmc <- RunMCA(seuratPbmc, nmcs = 5)
seuratPbmc <- RunCellHGT(X = seuratPbmc, pathways = Hallmark, dims = 1:5)

Cortalak/cellID documentation built on Aug. 3, 2020, 9:01 p.m.