% -*- mode: noweb; noweb-default-code-mode: R-mode; -*- %setwd("c:/sepp/work/fabia/fabia_10/fabiaData/inst/doc") %Sweave("fabiaData.Rnw") %tools::texi2dvi("fabiaData.tex",pdf=TRUE) %Stangle("fabiaData.Rnw") %source("fabiaData.R") \documentclass[article]{bioinf} %\usepackage{sweave} \usepackage{amsmath,amssymb} \usepackage{bm} \usepackage{natbib} \usepackage{hyperref} \hypersetup{colorlinks=false, pdfborder=0 0 0, pdftitle={fabiaData (Gene Expression Data Sets for Biclustering) --- Manual for the R package}, pdfauthor={Sepp Hochreiter}} \newcommand{\R}{\textrm{R} } \newcommand{\Rfunction}[1]{{\texttt{#1}}} \newcommand{\Robject}[1]{{\texttt{#1}}} \newcommand{\Rpackage}[1]{{\texttt{#1}}} \title{fabiaData (Gene Expression Data Sets for Biclustering) \\ \textit{--- Manual for the R package ---}} \author{Sepp Hochreiter} \affiliation{Institute of Bioinformatics, Johannes Kepler University Linz\\Altenberger Str. 69, 4040 Linz, Austria\\ \email{hochreit@bioinf.jku.at}} \usepackage[noae]{Sweave} %\VignetteIndexEntry{fabiaData: Manual for the R Package} %\VignetteDepends{fabiaData} %\VignettePackage{fabiaData} %\VignetteKeywords{fabia, fabiaData, biclustering, factor analysis, sparse coding, non-negative matrix factorization, latent variables, Laplace distribution, EM algorithm, multivariate analysis} \SweaveOpts{eps=FALSE} \begin{document} <>= options(width=75) set.seed(0) library(fabiaData) fabiaDataVer<-packageDescription("fabiaData")$Version @ \newcommand{\fabiaDataVer}{\Sexpr{fabiaDataVer}} \manualtitlepage[Version \fabiaDataVer, \today] \newlength{\auxparskip} \setlength{\auxparskip}{\parskip} \setlength{\parskip}{0pt} \tableofcontents \clearpage \setlength{\parskip}{\auxparskip} \section{Introduction} The \Rpackage{fabiaData} package is part of the Bioconductor (\href{http://www.bioconductor.org}{http://www.bioconductor.org}) project. The package provides gene expression data sets for biclustering demos in the \R package \Rpackage{fabia}. It is automatically loaded by \Rpackage{fabia} when needed. The package \Rpackage{fabia} allows to extract biclusters from data sets based on a generative model according to the FABIA method \citep{Hochreiter:10}. It has been designed especially for microarray data sets, but can be used for other kinds of data sets as well. Please visit for additional information the FABIA homepage\linebreak \href{http://www.bioinf.jku.at/software/fabia/fabia.html}{http://www.bioinf.jku.at/software/fabia/fabia.html}. \section{Getting Started: fabiaData} \label{sec:started} First load the \Rpackage{fabia} package: \begin{Sinput} R> library(fabia) \end{Sinput} Then load the \Rpackage{fabiaData} package \begin{Sinput} R> library(fabiaData) \end{Sinput} Now biclusters can be extracted from these data sets in the fabia demos: \begin{Sinput} R> fabiaDemo() \end{Sinput} \begin{enumerate} \item demo2: Microarray data set of \citep{Veer:02} on breast cancer. \begin{Sinput} R> fabiaDemo() \end{Sinput} Choose ``2'' to extract subclasses in the data set of van't Veer as biclusters. \item demo3: Microarray data set of \citep{Su:02} on different mammalian. \begin{Sinput} R> fabiaDemo() \end{Sinput} Choose ``3'' to check whether the different mouse and human tissue types can be extracted. \item demo4: Microarray data set of \citep{Rosenwald:02} diffuse large-B-cell lymphoma. \citep{Hoshida:07} divided the data set into three classes \begin{itemize} \item{OxPhos:} oxidative phosphorylation \item{BCR:} B-cell response \item{HR:} host response \end{itemize} \begin{Sinput} R> fabiaDemo() \end{Sinput} Choose ``4'' to check whether the different classes can be extracted. \end{enumerate} \section{Data Sets} \subsection{Breast\_A} Microarray data set of van't Veer breast cancer. Microarray data from Broad Institute ``Cancer Program Data Sets'' which was produced by \citep{Veer:02} (\url{http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi}) Array S54 was removed because it is an outlier. Goal was to find a gene signature to predict the outcome of a cancer therapy that is to predict whether metastasis will occur. A 70 gene signature has been discovered. Here we want to find subclasses in the data set. \citep{Hoshida:07} found 3 subclasses and verified that 50/61 cases from class 1 and 2 were ER positive and only in 3/36 from class 3. \texttt{XBreast} is the data set with 97 samples and 1213 genes, \texttt{CBreast} give the three subclasses from \citep{Hoshida:07}. \subsection{DLBCL\_B} Microarray data set of Rosenwald diffuse large-B-cell lymphoma. Microarray data from Broad Institute ``Cancer Program Data Sets'' which was produced by \citep{Rosenwald:02} (\url{http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi}) Goal was to predict the survival after chemotherapy \citep{Hoshida:07} divided the data set into three classes: \begin{itemize} \item{OxPhos:} oxidative phosphorylation \item{BCR:} B-cell response \item{HR:} host response \end{itemize} We want to identify these subclasses. The data has 180 samples and 661 probe sets (genes). \texttt{XDLBCL} is the data set with 180 samples and 661 genes, \texttt{CDLBCL} give the three subclasses from \citep{Hoshida:07}. \subsection{Multi\_A} Microarray data set of Su on different mammalian tissue types. Microarray data from Broad Institute ``Cancer Program Data Sets'' which was produced by \citep{Su:02} (\url{http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi}) Gene expression from human and mouse samples across a diverse array of tissues, organs, and cell lines have been profiled. The goal was to have a reference for the normal mammalian transcriptome. Here we want to identify the subclasses which correspond to the tissue types. The data has 102 samples and 5565 probe sets (genes). \texttt{XMulti} is the data set with 102 samples and 5565 genes, \texttt{CMulti} give the four subclasses corresponding to the tissue types. \section{Demos} \label{sec:demos} \begin{Sinput} library(fabiaData) #------------------------------------------ ########################################### # fabia Demos ########################################### #------------------------------------------ #------------------------------------------ # DEMO1: Laura van't Veer's gene expression # data set for breast cancer #------------------------------------------ avail <- require(fabia) if (!avail) { message("") message("") message("#####################################################") message("Package 'fabia' is not available: please install.") message("#####################################################") } else { data(Breast_A) X <- as.matrix(XBreast) resBreast1 <- fabia(X,5,0.1,400,1.0,1.0) rBreast1 <- extractPlot(resBreast1,ti="FABIA Breast cancer(Veer)") raBreast1 <- extractBic(resBreast1) if ((raBreast1$bic[[1]][1]>1) && (raBreast1$bic[[1]][2])>1) { plotBicluster(raBreast1,1) } if ((raBreast1$bic[[2]][1]>1) && (raBreast1$bic[[2]][2])>1) { plotBicluster(raBreast1,2) } if ((raBreast1$bic[[3]][1]>1) && (raBreast1$bic[[3]][2])>1) { plotBicluster(raBreast1,3) } if ((raBreast1$bic[[4]][1]>1) && (raBreast1$bic[[4]][2])>1) { plotBicluster(raBreast1,4) } plot(resBreast1,dim=c(1,2),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast1,dim=c(1,3),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast1,dim=c(1,4),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast1,dim=c(1,5),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast1,dim=c(2,3),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast1,dim=c(2,4),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast1,dim=c(2,5),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast1,dim=c(3,4),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast1,dim=c(3,5),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast1,dim=c(4,5),label.tol=0.03,col.group=CBreast,lab.size=0.6) } #----------------------------------- # DEMO2: Su's multiple tissue types # gene expression data set #----------------------------------- avail <- require(fabia) if (!avail) { message("") message("") message("#####################################################") message("Package 'fabia' is not available: please install.") message("#####################################################") } else { data(Multi_A) X <- as.matrix(XMulti) resMulti1 <- fabia(X,5,0.1,300,1.0,1.0) rMulti1 <- extractPlot(resMulti1,ti="FABIA Multiple tissues(Su)") raMulti1 <- extractBic(resMulti1) if ((raMulti1$bic[[1]][1]>1) && (raMulti1$bic[[1]][2])>1) { plotBicluster(raMulti1,1) } if ((raMulti1$bic[[2]][1]>1) && (raMulti1$bic[[2]][2])>1) { plotBicluster(raMulti1,2) } if ((raMulti1$bic[[3]][1]>1) && (raMulti1$bic[[3]][2])>1) { plotBicluster(raMulti1,3) } if ((raMulti1$bic[[4]][1]>1) && (raMulti1$bic[[4]][2])>1) { plotBicluster(raMulti1,4) } plot(resMulti1,dim=c(1,2),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti1,dim=c(1,3),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti1,dim=c(1,4),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti1,dim=c(1,5),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti1,dim=c(2,3),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti1,dim=c(2,4),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti1,dim=c(2,5),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti1,dim=c(3,4),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti1,dim=c(3,5),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti1,dim=c(4,5),label.tol=0.01,col.group=CMulti,lab.size=0.6) } #----------------------------------------- # DEMO3: Rosenwald's diffuse large-B-cell # lymphoma gene expression data set #----------------------------------------- avail <- require(fabia) if (!avail) { message("") message("") message("#####################################################") message("Package 'fabia' is not available: please install.") message("#####################################################") } else { data(DLBCL_B) X <- as.matrix(XDLBCL) resDLBCL1 <- fabia(X,5,0.1,400,1.0,1.0) rDLBCL1 <- extractPlot(resDLBCL1,ti="FABIA Lymphoma(Rosenwald)") raDLBCL1 <- extractBic(resDLBCL1) if ((raDLBCL1$bic[[1]][1]>1) && (raDLBCL1$bic[[1]][2])>1) { plotBicluster(raDLBCL1,1) } if ((raDLBCL1$bic[[2]][1]>1) && (raDLBCL1$bic[[2]][2])>1) { plotBicluster(raDLBCL1,2) } if ((raDLBCL1$bic[[3]][1]>1) && (raDLBCL1$bic[[3]][2])>1) { plotBicluster(raDLBCL1,3) } if ((raDLBCL1$bic[[4]][1]>1) && (raDLBCL1$bic[[4]][2])>1) { plotBicluster(raDLBCL1,4) } plot(resDLBCL1,dim=c(1,2),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL1,dim=c(1,3),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL1,dim=c(1,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL1,dim=c(1,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL1,dim=c(2,3),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL1,dim=c(2,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL1,dim=c(2,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL1,dim=c(3,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL1,dim=c(3,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL1,dim=c(4,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) } #------------------------------------------ ########################################### # fabias Demos ########################################### #------------------------------------------ #------------------------------------------ # DEMO1: Laura van't Veer's gene expression # data set for breast cancer #------------------------------------------ avail <- require(fabia) if (!avail) { message("") message("") message("#####################################################") message("Package 'fabia' is not available: please install.") message("#####################################################") } else { data(Breast_A) X <- as.matrix(XBreast) resBreast2 <- fabias(X,5,0.6,300,1.0) rBreast2 <- extractPlot(resBreast2,ti="FABIAS Breast cancer(Veer)") raBreast2 <- extractBic(resBreast2) if ((raBreast2$bic[[1]][1]>1) && (raBreast2$bic[[1]][2])>1) { plotBicluster(raBreast2,1) } if ((raBreast2$bic[[2]][1]>1) && (raBreast2$bic[[2]][2])>1) { plotBicluster(raBreast2,2) } if ((raBreast2$bic[[3]][1]>1) && (raBreast2$bic[[3]][2])>1) { plotBicluster(raBreast2,3) } if ((raBreast2$bic[[4]][1]>1) && (raBreast2$bic[[4]][2])>1) { plotBicluster(raBreast2,4) } plot(resBreast2,dim=c(1,2),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast2,dim=c(1,3),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast2,dim=c(1,4),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast2,dim=c(1,5),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast2,dim=c(2,3),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast2,dim=c(2,4),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast2,dim=c(2,5),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast2,dim=c(3,4),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast2,dim=c(3,5),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast2,dim=c(4,5),label.tol=0.03,col.group=CBreast,lab.size=0.6) } #----------------------------------- # DEMO2: Su's multiple tisse types # gene expression data set #----------------------------------- avail <- require(fabia) if (!avail) { message("") message("") message("#####################################################") message("Package 'fabia' is not available: please install.") message("#####################################################") } else { data(Multi_A) X <- as.matrix(XMulti) resMulti2 <- fabias(X,5,0.6,300,1.0) rMulti2 <- extractPlot(resMulti2,ti="FABIAS Multiple tissues(Su)") raMulti2 <- extractBic(resMulti2) if ((raMulti2$bic[[1]][1]>1) && (raMulti2$bic[[1]][2])>1) { plotBicluster(raMulti2,1) } if ((raMulti2$bic[[2]][1]>1) && (raMulti2$bic[[2]][2])>1) { plotBicluster(raMulti2,2) } if ((raMulti2$bic[[3]][1]>1) && (raMulti2$bic[[3]][2])>1) { plotBicluster(raMulti2,3) } if ((raMulti2$bic[[4]][1]>1) && (raMulti2$bic[[4]][2])>1) { plotBicluster(raMulti2,4) } plot(resMulti2,dim=c(1,2),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti2,dim=c(1,3),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti2,dim=c(1,4),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti2,dim=c(1,5),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti2,dim=c(2,3),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti2,dim=c(2,4),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti2,dim=c(2,5),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti2,dim=c(3,4),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti2,dim=c(3,5),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti2,dim=c(4,5),label.tol=0.01,col.group=CMulti,lab.size=0.6) } #----------------------------------------- # DEMO3: Rosenwald's diffuse large-B-cell # lymphoma gene expression data set #----------------------------------------- avail <- require(fabia) if (!avail) { message("") message("") message("#####################################################") message("Package 'fabia' is not available: please install.") message("#####################################################") } else { data(DLBCL_B) X <- as.matrix(XDLBCL) resDLBCL2 <- fabias(X,5,0.6,300,1.0) rDLBCL2 <- extractPlot(resDLBCL2,ti="FABIAS Lymphoma(Rosenwald)") raDLBCL2 <- extractBic(resDLBCL2) if ((raDLBCL2$bic[[1]][1]>1) && (raDLBCL2$bic[[1]][2])>1) { plotBicluster(raDLBCL2,1) } if ((raDLBCL2$bic[[2]][1]>1) && (raDLBCL2$bic[[2]][2])>1) { plotBicluster(raDLBCL2,2) } if ((raDLBCL2$bic[[3]][1]>1) && (raDLBCL2$bic[[3]][2])>1) { plotBicluster(raDLBCL2,3) } if ((raDLBCL2$bic[[4]][1]>1) && (raDLBCL2$bic[[4]][2])>1) { plotBicluster(raDLBCL2,4) } plot(resDLBCL2,dim=c(1,2),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL2,dim=c(1,3),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL2,dim=c(1,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL2,dim=c(1,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL2,dim=c(2,3),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL2,dim=c(2,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL2,dim=c(2,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL2,dim=c(3,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL2,dim=c(3,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL2,dim=c(4,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) } #------------------------------------------ ########################################### # MFSC Demos ########################################### #------------------------------------------ #------------------------------------------ # DEMO1: Laura van't Veer's gene expression # data set for breast cancer #------------------------------------------ avail <- require(fabia) if (!avail) { message("") message("") message("#####################################################") message("Package 'fabia' is not available: please install.") message("#####################################################") } else { data(Breast_A) X <- as.matrix(XBreast) resBreast4 <- mfsc(X,5,100,0.6,0.6) rBreast4 <- extractPlot(resBreast4,ti="MFSC Breast cancer(Veer)") raBreast4 <- extractBic(resBreast4,thresZ=0.01,thresL=0.05) if ((raBreast4$bic[[1]][1]>1) && (raBreast4$bic[[1]][2])>1) { plotBicluster(raBreast4,1) } if ((raBreast4$bic[[2]][1]>1) && (raBreast4$bic[[2]][2])>1) { plotBicluster(raBreast4,2) } if ((raBreast4$bic[[3]][1]>1) && (raBreast4$bic[[3]][2])>1) { plotBicluster(raBreast4,3) } if ((raBreast4$bic[[4]][1]>1) && (raBreast4$bic[[4]][2])>1) { plotBicluster(raBreast4,4) } plot(resBreast4,dim=c(1,2),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast4,dim=c(1,3),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast4,dim=c(1,4),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast4,dim=c(1,5),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast4,dim=c(2,3),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast4,dim=c(2,4),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast4,dim=c(2,5),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast4,dim=c(3,4),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast4,dim=c(3,5),label.tol=0.03,col.group=CBreast,lab.size=0.6) plot(resBreast4,dim=c(4,5),label.tol=0.03,col.group=CBreast,lab.size=0.6) } #----------------------------------- # DEMO2: Su's multiple tissue types # gene expression data set #----------------------------------- avail <- require(fabia) if (!avail) { message("") message("") message("#####################################################") message("Package 'fabia' is not available: please install.") message("#####################################################") } else { data(Multi_A) X <- as.matrix(XMulti) resMulti4 <- mfsc(X,5,100,0.6,0.6) rMulti4 <- extractPlot(resMulti4,ti="MFSC Multiple tissues(Su)") raMulti4 <- extractBic(resMulti4,thresZ=0.01,thresL=0.05) if ((raMulti4$bic[[1]][1]>1) && (raMulti4$bic[[1]][2])>1) { plotBicluster(raMulti4,1) } if ((raMulti4$bic[[2]][1]>1) && (raMulti4$bic[[2]][2])>1) { plotBicluster(raMulti4,2) } if ((raMulti4$bic[[3]][1]>1) && (raMulti4$bic[[3]][2])>1) { plotBicluster(raMulti4,3) } if ((raMulti4$bic[[4]][1]>1) && (raMulti4$bic[[4]][2])>1) { plotBicluster(raMulti4,4) } plot(resMulti4,dim=c(1,2),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti4,dim=c(1,3),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti4,dim=c(1,4),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti4,dim=c(1,5),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti4,dim=c(2,3),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti4,dim=c(2,4),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti4,dim=c(2,5),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti4,dim=c(3,4),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti4,dim=c(3,5),label.tol=0.01,col.group=CMulti,lab.size=0.6) plot(resMulti4,dim=c(4,5),label.tol=0.01,col.group=CMulti,lab.size=0.6) } #----------------------------------------- # DEMO3: Rosenwald's diffuse large-B-cell # lymphoma gene expression data set #----------------------------------------- avail <- require(fabia) if (!avail) { message("") message("") message("#####################################################") message("Package 'fabia' is not available: please install.") message("#####################################################") } else { data(DLBCL_B) X <- as.matrix(XDLBCL) resDLBCL4 <- mfsc(X,5,100,0.6,0.6) rDLBCL4 <- extractPlot(resDLBCL4,ti="MFSC Lymphoma(Rosenwald)") raDLBCL4 <- extractBic(resDLBCL4,thresZ=0.01,thresL=0.05) if ((raDLBCL4$bic[[1]][1]>1) && (raDLBCL4$bic[[1]][2])>1) { plotBicluster(raDLBCL4,1) } if ((raDLBCL4$bic[[2]][1]>1) && (raDLBCL4$bic[[2]][2])>1) { plotBicluster(raDLBCL4,2) } if ((raDLBCL4$bic[[3]][1]>1) && (raDLBCL4$bic[[3]][2])>1) { plotBicluster(raDLBCL4,3) } if ((raDLBCL4$bic[[4]][1]>1) && (raDLBCL4$bic[[4]][2])>1) { plotBicluster(raDLBCL4,4) } plot(resDLBCL4,dim=c(1,2),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL4,dim=c(1,3),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL4,dim=c(1,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL4,dim=c(1,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL4,dim=c(2,3),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL4,dim=c(2,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL4,dim=c(2,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL4,dim=c(3,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL4,dim=c(3,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) plot(resDLBCL4,dim=c(4,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6) } \end{Sinput} \bibliographystyle{natbib} \bibliography{fabia} \end{document}