fgseaSimple {fgsea} | R Documentation

## Runs preranked gene set enrichment analysis.

### Description

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.

### Usage

    fgseaSimple(
      pathways,
      stats,
      nperm,
      minSize = 1,
      maxSize = Inf,
      scoreType = c("std", "pos", "neg"),
      nproc = 0,
      gseaParam = 1,
      BPPARAM = NULL
    )

### Arguments

`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.

### Value

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.

### Examples

    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)

[Package fgsea version 1.16.0 Index]