RunCellHGT: Run HyperGeometric Test on cells

Description Usage Arguments Value Examples

View source: R/hyper.R

Description

Run HyperGeometric Test on cells

Usage

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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

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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.