bulk.gsea: Bulk gene set enrichment analysis

In liger: Lightweight Iterative Geneset Enrichment

Description

Bulk gene set enrichment analysis

Usage

bulk.gsea(
  values,
  set.list,
  power = 1,
  rank = FALSE,
  weight = rep(1, length(values)),
  n.rand = 10000,
  mc.cores = 1,
  quantile.threshold = min(100/n.rand, 0.1),
  return.details = FALSE,
  skip.qval.estimation = FALSE
)

Arguments

values	vector of values with associated gene names; values must be named, according to names appearing in set.list elements
set.list	list of gene sets
power	an exponent to control the weight of the step (default: 1)
rank	whether to use ranks as opposed to values (default: FALSE)
weight	additional weights associated with each value (default: rep(1,length(values)))
n.rand	number of random permutations used to assess significance (default: 1e4)
mc.cores	number of cores for parallel processing (default: 1)
quantile.threshold	threshold used (default: min(100/n.rand,0.1))
return.details	whether to return extended details (default: FALSE)
skip.qval.estimation	whether to skip q-value estimation for multiple testing (default: FALSE)

Examples

data("org.Hs.GO2Symbol.list")  
universe <- unique(unlist(org.Hs.GO2Symbol.list))  # get universe
gs <- org.Hs.GO2Symbol.list[[1]]  # get a gene set
vals <- rnorm(length(universe), 0, 10)  # simulate values
names(vals) <- universe
vals[gs] <- rnorm(length(gs), 100, 10)  
gs.list <- org.Hs.GO2Symbol.list # get gene sets
# reduce n.rand for speed
bulk.gsea(values = vals, set.list = gs.list[1:3], mc.cores = 1, n.rand=100)

liger documentation built on Jan. 25, 2021, 9:07 a.m.