% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/enrichment.R
\name{run_enrichment}
\alias{run_enrichment}
\title{Run feature set Enrichment Analysis}
\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{
\item{object}{a \code{\link{MOFA}} object.}

\item{view}{a character with the view name, or a numeric vector with the index of the view to use.}

\item{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.}

\item{factors}{character vector with the factor names, or numeric vector with the index of the factors for which to perform the enrichment.}

\item{set.statistic}{the set statistic computed from the feature statistics. Must be one of the following: "mean.diff" (default) or "rank.sum".}

\item{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".}

\item{sign}{use only "positive" or "negative" weights. Default is "all".}

\item{min.size}{Minimum size of a feature set (default is 10).}

\item{nperm}{number of permutations. Only relevant if statistical.test is set to "permutation". Default is 1000}

\item{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.}

\item{alpha}{FDR threshold to generate lists of significant pathways. Default is 0.1}

\item{verbose}{boolean indicating whether to print messages on progress}
}
\value{
a list with five elements: 
\item{\strong{pval}:}{ matrices with nominal p-values. }
\item{\strong{pval.adj}:}{ matrices with FDR-adjusted p-values. }
\item{\strong{feature.statistics}:}{ matrices with the local (feature-wise) statistics.  }
\item{\strong{set.statistics}:}{ matrices with the global (gene set-wise) statistics.  }
\item{\strong{sigPathways}}{ list with significant pathways per factor. }
}
\description{
Method to perform feature set enrichment analysis. Here we use a slightly modified version of the \code{\link[PCGSE]{pcgse}} function.
}
\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. \cr
 This function is particularly useful when a factor is difficult to characterise based only on the genes with the highest weight. \cr
 We provide a few pre-built gene set matrices in the MOFAdata package. See \code{https://github.com/bioFAM/MOFAdata} for details. \cr
 The function we implemented is based on the \code{\link[PCGSE]{pcgse}} function with some modifications. 
 Please read this paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4543476 for details on the math.
}
