Description Usage Arguments Examples
Bulk gene set enrichment analysis
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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) |
1 2 3 4 5 6 7 8 9 | 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)
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