\name{enrichment}
\alias{anrichment}
\alias{ISAEnrichment}
\concept{Enrichment analysis}
\title{Enrichment analysis for transcription modules, based on
  user-defined categories} 
\description{
  This function performs enrichment analysis for each ISA module
  separately, comparing it to user-defined categories. It is useful to
  test against other databases and annotations than the Gene Ontology
  or KEGG pathways.
}
\usage{
ISAEnrichment (modules, categories, ann = annotation(modules),
    features = featureNames(modules), hgCutoff = 0.05,
    correction = TRUE, correction.method = "holm")
}
\arguments{
  \item{modules}{An \code{ISAModules} object, a set of ISA modules.}
  \item{categories}{A named list of gene categories. The names of the
    entries are used as category names. Each entry of the list must be a
    character vector containing Entrez gene ids.}
  \item{ann}{Character scalar. The annotation package to be used. By
    default it is taken from the \code{modules} argument.}
  \item{features}{Character vector. The names of the features. By
    default it is taken from the \code{modules} argument.}
  \item{hgCutoff}{Numeric scalar. The cutoff value to be used for the
    enrichment significance. This can be changed later, without
    recalculating the test.}
  \item{correction}{Logical scalar, whether to perform multiple
    hypothesis testing correction.}
  \item{correction.method}{Character scalar, the multiple testing
    correction method to use. Possible values: \dQuote{holm},
    \dQuote{hochberg}, \dQuote{hommel}, \dQuote{bonferroni},
    \dQuote{BH}, \dQuote{BY}, \dQuote{fdr}, \dQuote{none}. See the
    \code{\link[stats]{p.adjust}} function for details on these. }
}
\details{
  This function performs enrichment analysis, based on user defined gene
  labels. It is useful if one want to test ISA modules against
  databases, other than GO and KEGG.
  
  The hypergeometric test, a version Fisher's exact test, takes a gene
  label and a gene set (in our case coming from an ISA module) and asks
  whether the number of genes in the set labelled by the label is
  significantly more (or less) than what one would expect by chance.
  
  \code{ISAEnrichment} performs the hypergeometric test for every
  module, for all user supplied gene labels. The mapping from the probe
  ids on the array to Entrez Ids is done using the appropriate chip
  annotation package.

  \code{ISAEnrichment} currently cannot test for under-representation.
}
\value{
  A \code{\link{GeneralListHyperGResult}} object.
}
\author{ Gabor Csardi \email{Gabor.Csardi@unil.ch} }
\references{
  Bergmann S, Ihmels J, Barkai N: Iterative signature algorithm for the
  analysis of large-scale gene expression data \emph{Phys Rev E Stat
    Nonlin Soft Matter Phys.} 2003 Mar;67(3 Pt 1):031902. Epub 2003 Mar 11.  
}
\seealso{
  \code{\link{ISAGO}}, \code{\link{ISACHR}}, \code{\link{ISAKEGG}} and
  \code{\link{ISAmiRNA}} for other enrichment calculations.

  The \code{Category} package.
}
\examples{
data(ALLModulesSmall)
library(hgu95av2.db)
entrez <- unique(unlist(mget(featureNames(ALLModulesSmall), hgu95av2ENTREZID)))
categories <- lapply(letters, function(x) sample(entrez, 100))
names(categories) <- letters
fakeEnrichment1 <- ISAEnrichment(ALLModulesSmall, categories, correction=FALSE)
fakeEnrichment2 <- ISAEnrichment(ALLModulesSmall, categories, correction=TRUE)
}
\keyword{cluster}