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.
geneSetEnrich( x, celdaModel, useAssay = "counts", altExpName = "featureSubset", databases, fdr = 0.05 ) # S4 method for SingleCellExperiment geneSetEnrich( x, useAssay = "counts", altExpName = "featureSubset", databases, fdr = 0.05 ) # S4 method for matrix geneSetEnrich(x, celdaModel, databases, fdr = 0.05)
| x | A numeric matrix of counts or a
SingleCellExperiment
with the matrix located in the assay slot under |
|---|---|
| celdaModel | Celda object of class |
| useAssay | A string specifying which assay
slot to use if |
| 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. |
List of length 'L' where each member contains the significantly enriched terms for the corresponding module.
Ahmed Youssef, Zhe Wang
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"))#>#>#>#>#>#>#>#>#>#>