geneSetEnrichment: Carry out Gene Set Enrichment Analysis

In cansysbio/ConsensusTME: A Consensus Approach To Tumour Microenvironment Cell Estimation

Carry out Gene Set Enrichment Analysis

Description

geneSetEnrichment Combines exisiting signatures and allows additional of a new signature.

Usage

geneSetEnrichment(bulkExp, signatures, statMethod = c("ssgsea", "gsva",
  "plage", "zscore", "singScore"), singScoreDisp = FALSE,
  parallel.sz = 0, parallel.type = "SOCK")

Arguments

bulkExp	bulk tumour gene expression matrix. HUGO gene symbols as row names & sample IDs as column names.
signatures	a list with each element containing genes to represent a cell type. The cell types should be the names of each element of the list.
statMethod	statistical framework to be used in generating gene set enrichment scores. These mirror the parameter options of GSVA::gsva() with the exception of singScore. which leverages singscore::multiScore(). Default: ssgsea
singScoreDisp	Logical, when using singscore method should the dispersion for each gene set be returned with the enrichment scores. Default: FALSE
parallel.sz	parameter for GSVA::gsva() - Number of processors to use when doing the calculations in parallel. This requires to previously load either the parallel or the snow library. If parallel is loaded and this argument is left with its default value (parallel.sz = 0) then it will use all available core processors unless this is set with a smaller number. If snow is loaded then this must be set to a positive integer number that specifies the number of processors to employ in the parallel calculation.
parallel.type	parameter for GSVA::gsva() - Type of cluster architecture when using snow. "SOCK" - works on any system (including Windows). "FORK" - faster but should be only used for POSIX systems (Mac, Linux, Unix, BSD).

Value

returns a list with curated signatures ready to be combined

cansysbio/ConsensusTME documentation built on June 10, 2024, 5:36 p.m.