run_enrichment {MOFA2} | R Documentation

## Run feature set Enrichment Analysis

### Description

Method to perform feature set enrichment analysis. Here we use a slightly modified version of the `pcgse` function.

### Usage

    run_enrichment(
      object,
      view,
      feature.sets,
      factors = "all",
      set.statistic = c("mean.diff", "rank.sum"),
      statistical.test = c("parametric", "cor.adj.parametric", "permutation"),
      sign = c("all", "positive", "negative"),
      min.size = 10,
      nperm = 1000,
      p.adj.method = "BH",
      alpha = 0.1,
      verbose = TRUE
    )

### Arguments

`object` | a `MOFA` object.
`view` | a character with the view name, or a numeric vector with the index of the view to use.
`feature.sets` | data structure that holds feature set membership information. Must be a binary membership matrix (rows are feature sets and columns are features). See details below for some pre-built gene set matrices.
`factors` | character vector with the factor names, or numeric vector with the index of the factors for which to perform the enrichment.
`set.statistic` | the set statisic computed from the feature statistics. Must be one of the following: "mean.diff" (default) or "rank.sum".
`statistical.test` | the statistical test used to compute the significance of the feature set statistics under a competitive null hypothesis. Must be one of the following: "parametric" (default), "cor.adj.parametric", "permutation".
`sign` | use only "positive" or "negative" weights. Default is "all".
`min.size` | Minimum size of a feature set (default is 10).
`nperm` | number of permutations. Only relevant if statistical.test is set to "permutation". Default is 1000
`p.adj.method` | Method to adjust p-values factor-wise for multiple testing. Can be any method in p.adjust.methods(). Default uses Benjamini-Hochberg procedure.
`alpha` | FDR threshold to generate lists of significant pathways. Default is 0.1
`verbose` | boolean indicating whether to print messages on progress

### Details

The aim of this function is to relate each factor to pre-defined biological pathways by performing a gene set enrichment analysis on the feature weights.
This function is particularly useful when a factor is difficult to characterise based only on the genes with the highest weight.
We provide a few pre-built gene set matrices in the MOFAdata package. See `https://github.com/bioFAM/MOFAdata` for details.
The function we implemented is based on the `pcgse` function with some modifications. Please read this paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4543476 for details on the math.

### Value

a list with five elements:

`pval:` | matrices with nominal p-values.
`pval.adj:` | matrices with FDR-adjusted p-values.
`feature.statistics:` | matrices with the local (feature-wise) statistics.
`set.statistics:` | matrices with the global (gene set-wise) statistics.
`sigPathways` | list with significant pathways per factor.

[Package MOFA2 version 1.2.2 Index]