geneSetEnrich: Gene set enrichment

Description Usage Arguments Value Author(s) Examples

Description

Identify and return significantly-enriched terms for each gene module in a Celda object or a SingleCellExperiment object. Performs gene set enrichment analysis for Celda identified modules using the enrichr.

Usage

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geneSetEnrich(x, ...)

## S4 method for signature 'SingleCellExperiment'
geneSetEnrich(
  x,
  useAssay = "counts",
  altExpName = "featureSubset",
  databases,
  fdr = 0.05
)

## S4 method for signature 'matrix'
geneSetEnrich(x, celdaModel, databases, fdr = 0.05)

Arguments

x

A numeric matrix of counts or a SingleCellExperiment with the matrix located in the assay slot under useAssay. Rows represent features and columns represent cells. Rownames of the matrix or SingleCellExperiment object should be gene names.

...

Ignored. Placeholder to prevent check warning.

useAssay

A string specifying which assay slot to use if x is a SingleCellExperiment object. Default "counts".

altExpName

The name for the altExp slot to use. Default "featureSubset".

databases

Character vector. Name of reference database. Available databases can be viewed by listEnrichrDbs.

fdr

False discovery rate (FDR). Numeric. Cutoff value for adjusted p-value, terms with FDR below this value are considered significantly enriched.

celdaModel

Celda object of class celda_G or celda_CG.

Value

List of length 'L' where each member contains the significantly enriched terms for the corresponding module.

Author(s)

Ahmed Youssef, Zhe Wang

Examples

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library(M3DExampleData)
counts <- M3DExampleData::Mmus_example_list$data
# subset 500 genes for fast clustering
counts <- counts[seq(1501, 2000), ]
# cluster genes into 10 modules for quick demo
sce <- celda_G(x = as.matrix(counts), L = 10, verbose = FALSE)
gse <- geneSetEnrich(sce,
  databases = c("GO_Biological_Process_2018", "GO_Molecular_Function_2018"))

celda documentation built on Nov. 8, 2020, 8:24 p.m.