@@ -5,12 +5,13 @@ Version: 1.1.0

 Date: 2012-03-16

 Author: Anne Biton

 Maintainer: Anne Biton <anne.biton@gmail.com>

-Description: The goal of MineICA is to make easier the interpretation of the

-    interpretation of a decomposition obtained by Independent Component

-    Analysis on transcriptomic data. It helps the biological interpretation of

-    the components by studying their association with variables (e.g sample

-    annotations) and gene sets, and enables the comparison of components from

-    different datasets using correlation-based graph.

+Description: The goal of MineICA is to perform Independent Component Analysis

+    (ICA) on multiple transcriptome datasets, integrating additional data (e.g

+    molecular, clinical and pathological). This Integrative ICA helps the

+    biological interpretation of the components by studying their association

+    with variables (e.g sample annotations) and gene sets, and enables the

+    comparison of components from different datasets using correlation-based

+    graph.

 License: GPL-2

 LazyLoad: yes

 BiocViews: Bioinformatics, Visualizations, MultipleComparisons

@@ -648,9 +648,9 @@ plotDens2classInComp_plotOnly <- function (annot,

                     else if (typePlot == "boxplot") {

                         g <- g + geom_boxplot(aes(x=interest, y = comp, fill = interest),

                                               position = "identity", data = annot,

-                                              colour = "black") + scale_x_discrete(colAnnot) + scale_y_continuous(if (is.null(ylab)) "Sample contributions" else ylab)

+                                              colour = "black", outlier.shape = if (addPoints) NA else 16) + scale_x_discrete(colAnnot) + scale_y_continuous(if (is.null(ylab)) "Sample contributions" else ylab)                        

                         if (addPoints)

-                            g <- g + geom_point(aes(x=interest, y = comp, fill = interest),data = annot,color="#1A1A1A99")   #  + coord_flip()+

+                            g <- g + geom_jitter(aes(x=interest, y = comp, fill = interest), data = annot, color="#1A1A1A99", size=1.9, position=position_jitter(width=.2)) #+ theme_bw()#  + coord_flip()+position="jitter", 

                     }

                 }

@@ -662,8 +662,11 @@ plotDens2classInComp_plotOnly <- function (annot,

 			data_ref$y <- rep(x = -0.02,times = nrow(data_ref))

 			g <- g + geom_point(aes(x=comp,y=y),data = data_ref,  fill = "green", shape = 24, size = 1.7)

                     }

-                    else {

-			data_ref$x <- rep(x = 0.1,times = nrow(data_ref))

+                    else {                        

+                        if (!is.null(geneExpr))

+                            data_ref$x <- rep(x = 0.1,times = nrow(data_ref))

+                        else

+                            data_ref$x <- rep(x = 0.5,times = nrow(data_ref))

 			g <- g + geom_point(aes(y=comp,x=x),data = data_ref,  fill = "green", shape = 23, size = 3)

@@ -315,7 +315,7 @@ runAn <- function(params,

             qualVar <- qualVar[-indDel]

         if (length(annot2col(params))==0)

-            colours <- annot2Color(pData(icaSet)[,qualVar])

+            colours <- annot2Color(pData(icaSet)[,qualVar,drop=FALSE])

         else

             colours <- annot2col(params)

@@ -48,7 +48,7 @@

   \code{\link{annotReciprocal}}.}

   \item{tkplot}{If TRUE, performs interactive plot with

-  function \code{tkplot}, else uses \code{plot.igraph}}

+  function \code{tkplot}, else uses \code{plot.igraph}.}

   \item{\dots}{Additional parameters as required by

   \code{tkplot}.}

@@ -177,8 +177,9 @@ icaSettoy2 <- buildIcaSet(params=params, A=data.frame(resJade2$A), S=data.frame(

                           dat=dat2, alreadyAnnot=TRUE)$icaSet

 ## compare IcaSet objects

+## use tkplot=TRUE to get an interactive graph

 rescomp <- runCompareIcaSets(icaSets=list(icaSettoy1, icaSettoy2), labAn=c("toy1","toy2"),

-                             type.corr="pearson", level="genes")

+                             type.corr="pearson", level="genes", tkplot=FALSE)

 \dontrun{

@@ -42,13 +42,13 @@

 \maketitle

 \begin{abstract}

-  \Rpackage{MineICA} supplies a framework for the storage and the study of a decomposition resulting from the application of independent component analysis (ICA) to transcriptomic data.

+  \Rpackage{MineICA} supplies a framework for the storage and the study of a decomposition resulting from the application of independent component analysis (ICA) to transcriptomic data. It allows to integrate additional data associated with the samples (other molecular data, as well as clinical and pathological data) and data associated with the genes.

   It defines a new class \Rpackage{IcaSet} extending the class \Rpackage{eSet} of the package \Rpackage{Biobase}, which allows to store the inputs (genomic dataset and sample information) and outputs (mixing and source matrix) of ICA.

   %\Rpackage{MineICA} helps to interpret the ICA outputs and to compare ICA decompositions obtained on different data through correlation graphs. 

   \Rpackage{MineICA} helps the biological interpretation of the components by studying their association with variables (e.g sample annotations) and biological processes, and enables the comparison of components from different datasets using correlation-based graph.

   %the mixing matrix $A$, containing the sample contributions on the components. \Rpackage{MineICA} also helps the interpretation of the source matrix $S$, containing the contributions of the features (e.g genes) to component, by annotating the contributing features and study their association with known biological processes.

   In practice, by creating interactive summarization of the results and comprehensive plots, \Rpackage{MineICA} makes much easier the

-  interpretation of the numerous data resulting from the application of ICA to transcriptomic data. 

+  interpretation of the numerous data resulting from the application of ICA to transcriptomic data.   

 \end{abstract}

 \section{Introduction}