@@ -19,11 +19,11 @@

 }

 \arguments{

   \item{params}{An object of class

-  \code{\link[MineICA:MineICAParams-class]{MineICAParams}}

+  \code{\link[MineICA:class-MineICAParams]{MineICAParams}}

   containing the parameters of the analysis.}

   \item{icaSet}{An object of class

-  \code{\link[MineICA:IcaSet-class]{IcaSet}}.}

+  \code{\link[MineICA:class-IcaSet]{IcaSet}}.}

   \item{keepVar}{The variable labels to be considered, i.e

   a subset of the annotation variables available in

@@ -68,12 +68,12 @@

   \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}}.

+  \code{\link[Category:class-GOHyperGParams]{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}}.}

+  \code{\link[Category:class-GOHyperGParams]{GOHyperGParams}}.}

   \item{cutoffGOstats}{The p-value threshold used for

   selecting enriched gene sets, default is

new file mode 100644

@@ -0,0 +1,218 @@

+\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}},

+}

+