massiveGST {massiveGST}

massive Gene-Sets Test with Mann-Whitney-Wilcoxon statistics.

Description

Perform a competitive gene set enrichment analysis by applying the Mann-Withney-Wilcoxon test.

Usage

massiveGST(gene_profile, gene_sets, 
  cols_to_remove = NULL, 
  alternative = c("two.sided", "less", "greater")
  )

Arguments

gene_profile

a named list of values; the names have to match the names of genes in the gene-set.

gene_sets

a character vector of gene-sets.

cols_to_remove

a list of colnames to eventually remove from the output.

alternative

a character string specifying the alternative hypothesis of the MWW test; must be one of "two.sided" (default), "greater" or "less".

Value

A data frame with columns

size

Original size of the gene-set.

actualSize

Size of the gene-set after the match with the gene-profile.

NES

(Normalized Enrichment Score) the strength of the association of the gene-set with the gene profile; also the percentile rank of the gene-set in the universe of the genes ouside the gene-set.

odd

odd transformation of the NES.

logit2NES

logit transformation of the NES.

abs_logit2NES

absolute value of the logit2NES in the case of "two.sided" alternative.

p.value

p-values associated with the gene-set.

BH.value

Benijamini and Hockberg adjustment of the p.values.

B.value

Bonferroni adjustment of the p.values.

relevance

marginal ordering of the table.

Author(s)

Stefano M. Pagnotta

References

Cerulo, Pagnotta (2022) doi:10.3390/e24050739

See Also

summary.mGST, plot.mGST, cut_by_logit2NES, cut_by_NES, cut_by_significance

Examples

library(massiveGST)

# get the gene profile
fname <- system.file("extdata", package="massiveGST")
fname <- file.path(fname, "pre_ranked_list.txt")
geneProfile <- get_geneProfile(fname)

# get the gene-sets
geneSets <- get_geneSets_from_msigdbr(category = "H", what = "gene_symbol")

# run the function
ans <- massiveGST(geneProfile, geneSets, alternative = "two.sided")

ans