gsea: Gene set enrichment analysis

In liger: Lightweight Iterative Geneset Enrichment

Description

Gene set enrichment analysis

Usage

gsea(
  values,
  geneset,
  power = 1,
  rank = FALSE,
  weight = rep(1, length(values)),
  n.rand = 10000,
  plot = TRUE,
  main = "",
  return.details = FALSE,
  quantile.threshold = min(100/n.rand, 0.1),
  random.seed = 1,
  mc.cores = 1
)

Arguments

values	vector of values with associated gene names; values must be named, according to names appearing in set elements
geneset	vector of genes in the gene set
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)
plot	whether to plot (default: TRUE)
main	plot title (default: "")
return.details	whether to return extended details (default: FALSE)
quantile.threshold	threshold used (default: min(100/n.rand,0.1))
random.seed	random seed (default: 1)
mc.cores	number of cores for parallel processing (default: 1)

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
# fake dummy example where everything in gene set is perfectly enriched
vals <- rnorm(length(universe), 0, 10)  
names(vals) <- universe
vals[gs] <- rnorm(length(gs), 100, 10)
# test obviously enriched set, reduce n.rand for speed
gsea(values=vals, geneset=gs, mc.cores=1, n.rand=100, main="GO:Random") 

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