\name{plotPosSamplesInComp}
 \alias{plotPosSamplesInComp}
 \title{Histograms of sample subsets}
 \usage{
   plotPosSamplesInComp(samplesByGroup, labGroups = NULL,
     icaSet, keepComp = indComp(icaSet), file = NULL,
     breaks = 20, colAll = "grey74", colSel = "red",
     titlesup = "", resClus,
     funClus = c("Mclust", "kmeans"), ...)
 }
 \arguments{
   \item{samplesByGroup}{A list whose elements are vector of
   sample names, these sample names must be available in
   \code{sampleNames(icaSet)}. The list should be indexed by
   the name of the corresponding groups.}
 
   \item{labGroups}{A vector of group names, will be used to
   add names to \code{sampleByGroup} if
   \code{names(samplesByGroup)} is NULL.}
 
   \item{icaSet}{An object of class
   \code{\link[MineICA:IcaSet-class]{IcaSet}}}
 
   \item{keepComp}{A subset of components available in
   \code{indComp(icaSet)}, if NULL (default) all components
   are used}
 
   \item{file}{A pdf file}
 
   \item{breaks}{The number of breaks to be used in the
   histograms}
 
   \item{colSel}{The colour of the histogram of the group of
   interest, default is "red"}
 
   \item{colAll}{The colour of the global histogram, default
   is "grey74"}
 
   \item{resClus}{A list containing the outputs of function
   \code{\link{clusterSamplesByComp}}, which consists of
   results of clustering applied to matrix A of argument
   \code{icaSet}.}
 
   \item{funClus}{Specifies the clustering method used,
   either \code{"Mclust"} or \code{"kmeans"}. If
   \code{resClus} is not missing, equals
   \code{resClus$funClus}.}
 
   \item{titlesup}{Additional title for the histograms}
 
   \item{...}{Additional parameters for function
   \code{\link{hist}}}
 }
 \value{
   NULL
 }
 \description{
   This function plots the positions of several groups of
   samples across all the components of an
   \code{\link[MineICA:IcaSet-class]{icaSet}} object.
 }
 \details{
   For each subgroup of samples this function plots their
   positions within the histogram of the global sample
   contributions.
 
   The values of interest are the sample contributions
   across the components, i.e across the columns
   \code{A(icaSet)}.
 
   If argument \code{resClus} is not missing, the
   association between the clusters and the sub-groups of
   samples is tested using a chi-square test. The p-values
   of these tests are available in the title of each plot.
 }
 \examples{
 \dontrun{
 ## load an example of IcaSet
 data(icaSetCarbayo)
 
 ## selection of sample groups according to annotations STAGE
 samplesByGroup <- lapply(split(pData(icaSetCarbayo),pData(icaSetCarbayo)[c("STAGE")]), rownames)
 # select groups including at least 2 samples
 samplesByGroup <- samplesByGroup[which(unlist(lapply(samplesByGroup,length))>1)]
 
 ## clustering of samples according to A using Mclust imposing two Gaussian
 resClus <- clusterSamplesByComp(icaSet=icaSetCarbayo,funClus="Mclust", nbClus=2, clusterOn="A")
 
 ## Plot positions of the groups in 5th component
 pdf(file="stageOnIC5.pdf", height = 8.267717, width = 29.7/2.54, paper = 'a4r', title="stageOnIC5")
 plotPosSamplesInComp(samplesByGroup=samplesByGroup, icaSet=icaSetCarbayo, funClus="Mclust",
                      resClus = resClus, keepComp=5)
 dev.off()
 }
 }
 \author{
   Anne Biton
 }
 \seealso{
   \code{\link{hist}},
   \code{\link[MineICA:IcaSet-class]{IcaSet}}
 }
 \keyword{internal}