\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}},
}
