run_enrichment: Run feature set Enrichment Analysis

In bioFAM/MOFA2: Multi-Omics Factor Analysis v2

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:

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

bioFAM/MOFA2 documentation built on June 12, 2024, 3:57 p.m.