gene_set_analysis: Gene Set Analysis (GSA)

In singha53/amritr: Accessory Functions Used by Amrit Singh

Description

gene_set_analysis Perform gene set analysis using the hypergeometric test

Usage

gene_set_analysis(genes, genesets, study_universe, min.set.size = 1)

Arguments

genesets	list of gene sets
genelist	vector of gene symbols
universe	background list of genes: all the genes used in the study

Details

This function uses the hypergeometric function to test for over-representation of genesets in a user-specified list of genes. The unfiltered genesets contain all genes that are part of certain databases, whereas filtered genesets only contain genes that were used in the experiment (in order prevent sampling bias, https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0761-7).

Value

The output is a dataframe with 4 columns (gene sets (genesets), p-value (pval), bhfdr (BH-FDR), Type (unfiltered or filtered)

Examples

library(dplyr)
data(pathways)
# gene list supplied by the user
set.seed(1)
genelist <- pathways %>%
 filter(Database == "KEGG") %>%
 dplyr::select(Genes) %>%
 unlist %>%
 as.character %>%
 sample(., 50)

set.seed(2)
universe <- pathways %>%
 filter(Database == "KEGG") %>%
 dplyr::select(Genes) %>%
 unlist %>%
 as.character %>%
 sample(., 50) %>%
 c(., genelist) %>%
 unique

# Perform gene set enrichment analysis
result <- gene_set_analysis(genes = genelist,
 genesets = pathways,
 study_universe = universe,
 min.set.size = 1)

result$toptable %>% group_by(Type, Database) %>% slice(1:5)

singha53/amritr documentation built on July 21, 2019, 3:46 p.m.