fgseaCelliD {CelliD} | R Documentation

## Slight change in fgsea for ram and speed efficiency in CelliD

### Description

Slight change in fgsea for ram and speed efficiency in CelliD

### Usage

    fgseaCelliD(
      pathways,
      stats,
      nperm = 1000,
      minSize = 10,
      maxSize = 500,
      gseaParam = 0
    )

### 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. Minimal 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.
`gseaParam` | GSEA parameter value, all gene-level stats are raised to the power of 'gseaParam' before calculation of GSEA enrichment scores

### 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

    seuratPbmc <- RunMCA(seuratPbmc, nmcs = 5)
    ranking <- GetCellGeneRanking(seuratPbmc, reduction = "mca", dims = 1:5)
    fgseaCelliD(pathways = Hallmark, stats = ranking[[1]])

[Package CelliD version 1.2.1 Index]