Runs preranked gene set enrichment analysis.
The function takes about O(nk^{3/2}) time, where n is number of permutations and k is a maximal size of the pathways. That means that setting 'maxSize' parameter with a value of ~500 is strongly recommended.
fgseaSimple(
pathways,
stats,
nperm,
minSize = 1,
maxSize = Inf,
scoreType = c("std", "pos", "neg"),
nproc = 0,
gseaParam = 1,
BPPARAM = NULL
)pathways |
List of gene sets to check. |
stats |
Named vector of gene-level stats. Names should be the same as in 'pathways' |
nperm |
Number of permutations to do. Minimial possible nominal p-value is about 1/nperm |
minSize |
Minimal size of a gene set to test. All pathways below the threshold are excluded. |
maxSize |
Maximal size of a gene set to test. All pathways above the threshold are excluded. |
scoreType |
This parameter defines the GSEA score type. Possible options are ("std", "pos", "neg") |
nproc |
If not equal to zero sets BPPARAM to use nproc workers (default = 0). |
gseaParam |
GSEA parameter value, all gene-level statis are raised to the power of 'gseaParam' before calculation of GSEA enrichment scores. |
BPPARAM |
Parallelization parameter used in bplapply. Can be used to specify cluster to run. If not initialized explicitly or by setting 'nproc' default value 'bpparam()' is used. |
A table with GSEA results. Each row corresponds to a tested pathway. The columns are the following:
pathway – name of the pathway as in 'names(pathway)';
pval – an enrichment p-value;
padj – a BH-adjusted p-value;
ES – enrichment score, same as in Broad GSEA implementation;
NES – enrichment score normalized to mean enrichment of random samples of the same size;
nMoreExtreme' – a number of times a random gene set had a more extreme enrichment score value;
size – size of the pathway after removing genes not present in 'names(stats)'.
leadingEdge – vector with indexes of leading edge genes that drive the enrichment, see http://software.broadinstitute.org/gsea/doc/GSEAUserGuideTEXT.htm#_Running_a_Leading.
data(examplePathways) data(exampleRanks) fgseaRes <- fgseaSimple(examplePathways, exampleRanks, nperm=10000, maxSize=500) # Testing only one pathway is implemented in a more efficient manner fgseaRes1 <- fgseaSimple(examplePathways[1], exampleRanks, nperm=10000)
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