ACSNMineR (version 0.16.8.25)

multisample_enrichment: Automated gene set analysis for multiple sets

Description

Automated gene set analysis for multiple sets

Usage

multisample_enrichment(Genes_by_sample = NULL, maps = c("Apoptosis", "CellCycle", "DNA_repair", "EMT_motility", "Survival"), correction_multitest = "BH", statistical_test = "fisher", min_module_size = 5, universe = "map_defined", Remove_from_universe = NULL, threshold = 0.05, cohort_threshold = TRUE, alternative = "greater")

Arguments

Genes_by_sample

List of character vectors. Each list element name should be a sample name, and each character vector the set of genes to test for the sample.

maps

list of maps generated by format_from_gmt. Default: tests on all acsn maps

correction_multitest

either FALSE, "bonferroni", "holm", "hochberg", "hommel", "BH", "fdr" (identical to BH), or "BY"

statistical_test

one of "fisher", "hypergeom"

min_module_size

will remove from the analysis all modules which are (strictly) smaller than threshold

universe

Universe on which the statistical analysis should be performed. Can be either "HUGO","ACSN" ,"map_defined", or a character vector of genes.

Remove_from_universe

Default is NULL. A list of genes that should not be considered for enrichment (will be removed from input, maps, and universe). The size of universe and map will be updated after removal.

threshold

maximal p-value (corrected if correction is enabled) that will be displayed

cohort_threshold

if TRUE modules will be kept in all samples if at least one sample has p-value lower than threshold, otherwise the threshold is applied for each sample independently.

alternative

One of "greater", "less", "both", or "two.sided" (only for fisher test). Greater will check for enrichment, less will check for depletion, and both will look for both.

Value

Output is a list of dataframes with names the names given in `Genes_by_sample` with the following columns:

Examples

multisample_enrichment(Genes_by_sample = list(set1 = genes_test,set2=c(genes_test,"PTPRD")),
maps = list(cellcycle = ACSNMineR::ACSN_maps$CellCycle),
min_module_size = 10,
universe = "ACSN",cohort_threshold = FALSE)