fgseaMultilevel {fgsea} | R Documentation

## Runs preranked gene set enrichment analysis.

### Description

This feature is based on the adaptive multilevel splitting Monte Carlo approach. This allows us to exceed the results of simple sampling and calculate arbitrarily small P-values.

### Usage

    fgseaMultilevel(
      pathways,
      stats,
      sampleSize = 101,
      minSize = 1,
      maxSize = Inf,
      eps = 1e-50,
      scoreType = c("std", "pos", "neg"),
      nproc = 0,
      gseaParam = 1,
      BPPARAM = NULL,
      nPermSimple = 1000,
      absEps = NULL
    )

### Arguments

`pathways` | List of gene sets to check.
`stats` | Named vector of gene-level stats. Names should be the same as in 'pathways'
`sampleSize` | The size of a random set of genes which in turn has size = pathwaySize
`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.
`eps` | This parameter sets the boundary for calculating the p value.
`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.
`nPermSimple` | Number of permutations in the simple fgsea implementation for preliminary estimation of P-values.
`absEps` | deprecated, use 'eps' parameter instead

### 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;

- log2err – the expected error for the standard deviation of the P-value logarithm.

- ES – enrichment score, same as in Broad GSEA implementation;

- NES – enrichment score normalized to mean enrichment of random samples of the same size;

- 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)
    fgseaMultilevelRes <- fgseaMultilevel(examplePathways, exampleRanks, maxSize=500)

[Package fgsea version 1.20.0 Index]