\name{runAn}
 \alias{runAn}
 \title{Run analysis of an IcaSet object}
 \usage{
   runAn(params, icaSet, keepVar,
     heatmapCutoff = params["selCutoff"],
     funClus = c("Mclust", "kmeans"), nbClus,
     clusterOn = "A", keepComp, keepSamples,
     adjustBy = c("none", "component", "variable"),
     typePlot = c("boxplot", "density"),
     mart = useMart(biomart = "ensembl", dataset = "hsapiens_gene_ensembl"),
     dbGOstats = c("KEGG", "GO"), ontoGOstats = "BP",
     condGOstats = TRUE,
     cutoffGOstats = params["pvalCutoff"],
     writeGenesByComp = TRUE, writeFeaturesByComp = FALSE,
     selCutoffWrite = 2.5, runVarAnalysis = TRUE,
     onlySign = T, runClustering = FALSE, runGOstats = TRUE,
     plotHist = TRUE, plotHeatmap = TRUE)
 }
 \arguments{
   \item{params}{An object of class
   \code{\link[MineICA:MineICAParams-class]{MineICAParams}}
   containing the parameters of the analysis.}
 
   \item{icaSet}{An object of class
   \code{\link[MineICA:IcaSet-class]{IcaSet}}.}
 
   \item{keepVar}{The variable labels to be considered, i.e
   a subset of the annotation variables available in
   (\code{varLabels(icaSet)}).}
 
   \item{keepSamples}{The samples to be considered, i.e a
   subset of (\code{sampleNames(icaSet)}).}
 
   \item{heatmapCutoff}{The cutoff (applied to the scaled
   feature/gene projections contained in S/SByGene) used to
   select the contributing features/genes.}
 
   \item{funClus}{The function to be used to cluster the
   samples, must be one of
   \code{c("Mclust","kmeans","pam","pamk","hclust","agnes")}.
   Default is \code{"Mclust"}.}
 
   \item{nbClus}{The number of clusters to be computed when
   applying \code{funClus}. Can be missing (default) if
   \code{funClus="Mclust"} or \code{funClus="pamk"}.}
 
   \item{keepComp}{The indices of the components to be
   analyzed, must be included in \code{indComp(icaSet)}. If
   missing, all components are treated.}
 
   \item{adjustBy}{The way the p-values of the Wilcoxon and
   Kruskal-Wallis tests should be corrected for multiple
   testing: \code{"none"} if no p-value correction has to be
   done, \code{"component"} if the p-values have to be
   corrected by component, \code{"annotation"} if the
   p-values have to be corrected by variable}
 
   \item{typePlot}{The type of plot used to show
   distribution of sample-groups contributions, either
   "density" or "boxplot"}
 
   \item{mart}{A mart object used for annotation, see
   function \code{\link[biomaRt]{useMart}}}
 
   \item{dbGOstats}{The used database to use ('GO' and/or
   'KEGG'), default is both.}
 
   \item{ontoGOstats}{A string specifying the GO ontology to
   use. Must be one of 'BP', 'CC', or 'MF', see
   \code{\link[Category:GOHyperGParams-class]{GOHyperGParams}}.
   Only used when argument \code{dbGOstats} is 'GO'.}
 
   \item{condGOstats}{A logical indicating whether the
   calculation should conditioned on the GO structure, see
   \code{\link[Category:GOHyperGParams-class]{GOHyperGParams}}.}
 
   \item{cutoffGOstats}{The p-value threshold used for
   selecting enriched gene sets, default is
   params["pvalCutoff"]}
 
   \item{writeGenesByComp}{If TRUE (default) the gene
   projections (\code{SByGene(icaSet)}) are written in an
   html file and annotated using \code{biomaRt} for each
   component.}
 
   \item{writeFeaturesByComp}{If TRUE (default) the feature
   projections (\code{S(icaSet)}) are written in an html
   file and annotated using \code{biomaRt} for each
   component.}
 
   \item{runGOstats}{If TRUE the enrichment analysis of the
   contributing genes is run for each component using
   package \code{GOstats} (default is TRUE).}
 
   \item{plotHist}{If TRUE the position of the sample
   annotations within the histograms of the sample
   contributions are plotted.}
 
   \item{plotHeatmap}{If TRUE the heatmap of the
   contributing features/genes are plotted for each
   component.}
 
   \item{runClustering}{If TRUE the potential associations
   between a clustering of the samples (performed according
   to the components), and the sample annotations, are
   tested using chi-squared tests.}
 
   \item{runVarAnalysis}{If TRUE the potential associations
   between sample contributions (contained in
   \code{A(icaSet)}) are tested using Wilcoxon or
   Kruskal-Wallis tests.}
 
   \item{onlySign}{If TRUE (default), only the significant
   results are plotted in functions \code{qualVarAnalysis,
   quantVarAnalysis, clusVarAnalysis}, else all plots are
   done.}
 
   \item{selCutoffWrite}{The cutoff applied to the absolute
   feature/gene projection values to select the
   features/genes that will be annotated using package
   \code{biomaRt}, default is 2.5.}
 
   \item{clusterOn}{Specifies the matrix used to apply
   clustering if \code{runClustering=TRUE}: \describe{
   \item{\code{"A"}:}{the clustering is performed in one
   dimension, on the vector of sample contributions,}
   \item{"S":}{the clustering is performed on the original
   data restricted to the contributing individuals,}
   \item{"AS":}{the clustering is performed on the matrix
   formed by the product of the column of A and the row of
   S.}}}
 }
 \value{
   NULL
 }
 \description{
   This function runs the analysis of an ICA decomposition
   contained in an IcaSet object, according to the
   parameters entered by the user and contained in a
   MineICAParams.
 }
 \details{
   This function calls functions of the MineICA package
   depending on the arguments: \describe{
   \item{\code{\link{writeProjByComp}} (if
   \code{writeGenesByComp=TRUE} or
   \code{writeFeaturesByComp})}{which writes in html files
   the description of the features/genes contributing to
   each component, and their projection values on all the
   components.} \item{\code{\link{plot_heatmapsOnSel}} (if
   \code{plotHeatmap=TRUE})}{which plots heatmaps of the
   data restricted to the contributing features/genes of
   each component.} \item{\code{\link{plotPosAnnotInComp}}
   (if \code{plotHist=TRUE})}{which plots, within the
   histogram of the sample contribution values of every
   component, the position of groups of samples formed
   according to the sample annotations contained in
   \code{pData(icaSet)}.}
   \item{\code{\link{clusterSamplesByComp}} (if
   \code{runClustering=TRUE})}{which clusters the samples
   according to each component.}
   \item{\code{\link{clusVarAnalysis}} (if
   \code{runClustering=TRUE})}{which computes the
   chi-squared test of association between a given
   clustering of the samples and each annotation level
   contained in \code{pData(icaSet)}, and summarizes the
   results in an HTML file. } \item{\code{\link{runEnrich}}
   (if \code{runGOstats=TRUE})}{which perforns enrichment
   analysis of the contributing genes of the components
   using package \link{GOstats}.}
   \item{\code{\link{qualVarAnalysis}} and
   \code{\link{quantVarAnalysis}} (if
   \code{varAnalysis=TRUE})}{which tests if the groups of
   samples formed according to sample annotations contained
   in \code{pData(icaSet)} are differently distributed on
   the components, in terms of contribution value. } }
 
   Several directories containing the results of each
   analysis are created by the function: \describe{
   \item{ProjByComp:}{contains the annotations of the
   features or genes, one file per component;}
   \item{varAnalysisOnA:}{contains two directories: 'qual/'
   and 'quant/' which respectively contain the results of
   the association between components qualitative and
   quantitative variables;} \item{Heatmaps:}{contains the
   heatmaps (one pdf file per component) of contributing
   genes by component;} \item{varOnSampleHist:}{contains
   athe histograms of sample contributions superimposed with
   the histograms of the samples grouped by variable;}
   \item{cluster2var:}{contains the association between a
   clustering of the samples performed on the mixing matrix
   \code{A} and the variables.} }
 }
 \examples{
 \dontrun{
 
 ## load an example of IcaSet
 data(icaSetCarbayo)
 ## make sure the 'mart' attribute is correctly defined
 mart(icaSetCarbayo) <- useMart(biomart="ensembl", dataset="hsapiens_gene_ensembl")
 
 ## creation of an object of class MineICAParams
 ## here we use a low threshold because 'icaSetCarbayo' is already
 # restricted to the contributing features/genes
 params <- buildMineICAParams(resPath="~/resMineICACarbayotestRunAn/", selCutoff=2, pvalCutoff=0.05)
 require(hgu133a.db)
 
 runAn(params=params, icaSet=icaSetCarbayo)
 }
 }
 \author{
   Anne Biton
 }
 \seealso{
   \code{\link{writeProjByComp}},
 }