
% -*- mode: noweb; noweb-default-code-mode: R-mode; -*-
%\VignetteIndexEntry{FABIA: Manual for the R package}
%\VignetteKeywords{fabia, biclustering, factor analysis, sparse coding, non-negative matrix factorization, latent variables, Laplace distribution, EM algorithm, multivariate analysis}
%\VignetteDepends{fabia}
%\VignettePackage{fabia}
%setwd("c:/sepp/work/fabia/fabia_13/fabia/inst/doc")
%Sweave("fabia.Rnw")
%tools::texi2dvi("fabia.tex",pdf=TRUE)
%Stangle("fabia.Rnw")
%source("fabia.R")


\documentclass[article]{bioinfR}

\newcommand{\fabiaVer}{0.1.3}
%\usepackage{sweave}
\usepackage{amsmath,amssymb}
\usepackage{bm}
\usepackage{natbib}
\usepackage{url}
\usepackage{rotating}

\usepackage{fabia_defs}

\usepackage{hyperref}

\hypersetup{colorlinks=false,
   pdfborder=0 0 0,
   pdftitle={FABIA: Factor Analysis for Bicluster Acquisition - Manual for the R package},
   pdfauthor={Sepp Hochreiter}}



\title{FABIA: Factor Analysis for Bicluster Acquisition\\ \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}
\SweaveOpts{engine=R}
\begin{document}

\manualtitlepage[Version \fabiaVer, \today]

\newlength{\auxparskip}
\setlength{\auxparskip}{\parskip}
\setlength{\parskip}{0pt}
\tableofcontents
\clearpage
\setlength{\parskip}{\auxparskip}
\section{Introduction}


The \Rpackage{fabia} package is part of the
Bioconductor
(\href{http://www.bioconductor.org}{http://www.bioconductor.org}) project.
The package 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.



% <<echo=F,results=hide,fig=F,eval=T>>=
% results=verbatim, tex, hide


%<<label=fig1plot,include=FALSE>>=
%plot(x, y)
%abline(out1)
%@



%\begin{figure}
%\begin{center}
%<<label=fig1,fig=TRUE, term=FALSE,echo=FALSE>>=
%<<fig1plot>>
%@
%\end{center}
%\caption{Scatter Plot with Regression Line}
%\label{fig:one}
%\end{figure}

%\begin{figure}
%\begin{center}
%<<label=fig2,fig=TRUE, term=FALSE,echo=FALSE>>=
%out3 <- lm(y ~ x + I(x^2) + I(x^3))
%plot(x, y)
%curve(predict(out3, newdata=data.frame(x=x)), add = TRUE)
%@
%\end{center}
%\caption{Scatter Plot with Cubic Regression Curve}
%\label{fig:two}
%\end{figure}


%<<label=tab1,echo=FALSE,results=tex>>=
%library(xtable)
%xtable(foo, caption = "ANOVA Table", label = "tab:one",
%digits = c(0, 0, 2, 0, 2, 3, 3)), table.placement = "tbp",
%caption.placement = "top")
%@


\section{Getting Started: FABIA}
\label{sec:started}

First load the \Rpackage{fabia} package:
\begin{Sinput}
R> library(fabia) ##load the fabia package
\end{Sinput}


The \Rpackage{fabia} package imports the package
\Rpackage{methods} and the function
\Rfunction{plot} from the package \Rpackage{graphics}.



\subsection{Quick start : without outputs and plots}

Assume your data is in the file \texttt{datafile.csv} in a matrix like
format then you can try out the following steps to extract biclusters.

\noindent
Examples with outputs and plot can be found at
\href{http://www.bioinf.jku.at/software/fabia/fabia.html}{http://www.bioinf.jku.at/software/fabia/fabia.html}).



\begin{enumerate}
\item Create a working directory, e.g. \texttt{c:/fabia/data} in Windows or
\texttt{/home/myself/fabia/data} in Unix. Move the data file
 \texttt{datafile.csv} to
 that directory, e.g. under Unix
 \begin{quote}
 \texttt{cp datafile.csv /home/myself/fabia/data/}
 \end{quote}
 or drag the file
 \texttt{datafile.csv} into that directory under Windows.
\item Start \R and change to the working directory. Under Windows
 \begin{Sinput}
R> setwd("c:/fabia/data")
\end{Sinput}
and under Unix
 \begin{Sinput}
R> setwd("/home/myself/fabia/data")
\end{Sinput}
You can also start \R in that directory under Unix.
\item Load the library:
\begin{Sinput}
R> library(fabia) ##load the fabia package
\end{Sinput}
\item Read the data file ``datafile.csv'':
\begin{Sinput}
R> X <- read.table("datafile.csv",header = TRUE, sep = ",")
\end{Sinput}
\item Select the model based on the data: 5 biclusters; sparseness
  0.1; 400 cycles
\begin{Sinput}
R> res <- fabia(X,400,0.1,5)
\end{Sinput}
\item Give summary:
\begin{Sinput}
R> summary(res)
\end{Sinput}
\item Plot some statistics:
\begin{Sinput}
R> show(res)
\end{Sinput}
\item Plot the factorization results:
\begin{Sinput}
R> rr <- extract_plot(res,ti="FABIA")
\end{Sinput}
\item Plot the result as a biplot:
\begin{Sinput}
R> plot(res)
\end{Sinput}
\item Extract biclusters:
\begin{Sinput}
R> rb <- extract_bic(res)
\end{Sinput}
\item Show information content of the biclusters:
\begin{Sinput}
R> res@avini
\end{Sinput}
\item List bicluster 1:
\begin{Sinput}
R> rb$bic[1,]
\end{Sinput}
\item List bicluster 2:
\begin{Sinput}
R> rb$bic[2,]
\end{Sinput}
\item Show bicluster 3:
\begin{Sinput}
R> rb$bic[3,]
\end{Sinput}
\item List bicluster 4:
\begin{Sinput}
R> rb$bic[4,]
\end{Sinput}
\item List bicluster 5:
\begin{Sinput}
R> rb$bic[5,]
\end{Sinput}
\item Plot bicluster 1:
\begin{Sinput}
R> plotBicluster(X,unlist(rb$bic[1,5]),unlist(rb$bic[1,3]))
\end{Sinput}
\item Plot bicluster 2:
\begin{Sinput}
R> plotBicluster(X,unlist(rb$bic[2,5]),unlist(rb$bic[2,3]))
\end{Sinput}
\item Plot bicluster 3:
\begin{Sinput}
R> plotBicluster(X,unlist(rb$bic[3,5]),unlist(rb$bic[3,3]))
\end{Sinput}
\item Plot bicluster 4:
\begin{Sinput}
R> plotBicluster(X,unlist(rb$bic[4,5]),unlist(rb$bic[4,3]))
\end{Sinput}
\item Plot bicluster 5:
\begin{Sinput}
R> plotBicluster(X,unlist(rb$bic[5,5]),unlist(rb$bic[5,3]))
\end{Sinput}
\item List opposite bicluster 1:
\begin{Sinput}
R> rb$bicopp[1,]
\end{Sinput}
\item Plot opposite bicluster 1:
\begin{Sinput}
R> plotBicluster(X,unlist(rb$bicopp[1,5]),unlist(rb$bicopp[1,3]))
\end{Sinput}
\end{enumerate}


\subsection{Test on Toy Data Set}

In the following, we describe how you can test
the package \Rpackage{fabia} on a toy data set that is generated on-line.

\begin{enumerate}
\item generate bicluster data, where biclusters are in block format in
  order to obtain a better visualization of the results. 1000
  observations, 100 samples, 10 biclusters:

\begin{Sinput}
R> dat <- make_fabi_data_blocks(n = 1000,l= 100,p = 10,f1 = 5,
    f2 = 5,of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,
    mean_z = 2.0,sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)
\end{Sinput}

\item store the generated data in variables:
\begin{Sinput}
R> X <- dat[[1]]
R> Y <- dat[[2]]
\end{Sinput}

\item perform  \Rfunction{fabia} (sparseness by Laplace prior)
  to extract biclusters; 400 cycles,
  sparseness 0.1 (Laplace), 13 biclusters:
\begin{Sinput}
R> resToy1 <- fabia(X,400,0.1,1.0,1.0,13)
\end{Sinput}
\item Plot some results:
\begin{Sinput}
R> rToy1 <- extract_plot(resToy1,ti="FABIA",Y=Y)
\end{Sinput}
\item perform  \Rfunction{fabias} (sparseness by projection)
  to extract biclusters; 200 cycles,
  sparseness 0.5 (projection), 13 biclusters:
\begin{Sinput}
R> resToy2 <- fabias(X,200,0.5,1.0,13)
\end{Sinput}
\item  Plot some results:
\begin{Sinput}
R> rToy2 <- extract_plot(resToy2,ti="FABIAS",Y=Y)
\end{Sinput}

\item perform  \Rfunction{fabiap} (Laplace prior then projection)
  to extract biclusters; 200 cycles,
  sparseness 0.1 (Laplace), 13 biclusters, 0.7 sparseness loading
  (projection), 0.7 sparseness factors (projection):
\begin{Sinput}
R> resToy3 <- fabiap(X,200,0.1,1.0,1.0,13,0.7,0.7)
\end{Sinput}
\item Plot some results:
\begin{Sinput}
R> rToy3 <- extract_plot(resToy3,ti="FABIAP",Y=Y)
\end{Sinput}
\item perform  \Rfunction{mfsc} (sparse matrix factorization),
  13 biclusters, 0.7 sparseness loading
  (projection), 0.7 sparseness factors (projection):
\begin{Sinput}
R> resToy4 <- mfsc(X,13,sL=0.7,sZ=0.7)
\end{Sinput}
\item Plot some results:
\begin{Sinput}
R> rToy4 <- extract_plot(resToy4,ti="MFSC",Y=Y)
\end{Sinput}
\end{enumerate}



\subsection{Demos}

The package \Rpackage{fabia} has some demos which can be
demonstrated by \Rfunction{fabiaDemo}.

\begin{enumerate}
\item demo1: toy data.

\begin{Sinput}
R> fabiaDemo()
\end{Sinput}
Choose ``1'' and you get above toy data demonstration.

\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}



\newpage

\section{The FABIA Model}\label{sec:model}

For a detailed model describtion see the FABIA model 
\citep{Hochreiter:10}.
 
 
 \subsection{Model Assumptions}

We define a {\em bicluster} as a pair of a row (gene) set and a
column (sample) set for which the rows are similar to each other on
the columns and vice versa. In a multiplicative model, two vectors
are similar if one is a multiple of the other, that is
the angle between them is zero or as realization of random variables their
correlation coefficient is one.
\begin{figure}[!t]
\centerline{\includegraphics[width=0.9\textwidth]{vectors_1}}
\caption{The outer product  $\la \ \z^T$ of two sparse vectors
results in a matrix with a bicluster. Note, that the non-zero
entries in the vectors are adjacent to each other for visualization
purposes only.}\label{fig:vectors}
\end{figure}
It is clear that such
a linear dependency on subsets of rows and columns can be
represented as an outer product $\la \ \z^T$ of two vectors
$\la$ and $\z$. The vector $\la$ corresponds to a {\em prototype
column vector} that contains zeros for genes not participating in
the bicluster, whereas $\z$ is a vector of {\em factors} with which
the prototype column vector is scaled for each sample; clearly $\z$
contains zeros for samples not participating in the bicluster.
Vectors containing many zeros or values close to
zero are called {\em sparse vectors}.
Fig.\ \ref{fig:vectors} visualizes this representation by sparse
vectors schematically.


The overall model for $p$ biclusters and additive noise is
\begin{align}
\X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ + \ \Up \ = \ \La \ \Z \ +
\ \Up \ ,\label{eq:expgen}
\end{align}
where $\Up \in \Rb^{n \times l}$ is additive noise and $\la_i  \in
\Rb^n$  and $\z_i  \in \Rb^l$ are the sparse prototype vector and
the sparse vector of factors of the $i$-th bicluster, respectively.
The second formulation above holds if $\La \in \Rb^{n \times p}$ is
the sparse prototype matrix containing the prototype vectors $\la_i$
as columns and $\Z \in \Rb^{p \times l}$ is the sparse factor matrix
containing the transposed factors $\z_i^T$ as rows. Note that
Eq.~\eqref{eq:expgen} formulates biclustering as sparse matrix
factorization.

According to Eq.~\eqref{eq:expgen}, the $j$-th sample $\x_j$, i.e.,
the $j$-th column of $\X$, is
\begin{align}
\x_j\ = \ \sum_{i=1}^{p} \la_i \ z_{ij} \ + \ \epp_j \ = \ \La \
\tilde\z_j\ + \ \epp_j \ ,\label{eq:expgen2}
\end{align}
where $\epp_j$ is the $j$-th column of the noise matrix $\Up$ and
$\tilde\z_j=(z_{1j},\dots,z_{pj})^T$ denotes the $j$-th column of
the matrix $\Z$. Recall that $\z_i^T=(z_{i1},\dots,z_{il})$ is the
vector of values that constitutes the $i$-th bicluster (one value
per sample), while $\tilde\z_j$ is the vector of values that
contribute to the $j$-th sample (one value per bicluster).

The formulation in Eq.~\eqref{eq:expgen2} facilitates a generative
interpretation by a factor analysis model with $p$ factors
\begin{align}
\x \ = \ \sum_{i=1}^{p} \la_i \ \tilde z_i  \ + \
    \epp \ = \ \La \ \tilde\z \ + \
    \epp \ ,\label{eq:factormodel}
\end{align}
where $\x$ are the observations, $\La$ is the loading matrix,
$\tilde z_i$ is the value of the \mbox{$i$-th} factor,
$\tilde\z=(\tilde z_1,\ldots,\tilde z_p)^T$ is the vector of
factors, and $\epp\in \Rb^{n}$ is the additive noise. Standard factor
analysis assumes that the noise is independent of $\tilde\z$, that
$\tilde\z$ is $\Ncal(\0,\I)$-distributed, and that $\epp$ is
$\Ncal(\0,\pp)$-distributed, where the covariance matrix $\pp \in
\Rb^{n \times n}$ is a diagonal matrix expressing independent
Gaussian noise. The parameter $\La$ explains the dependent (common)
and $\pp$ the independent variance in the observations $\x$.
Normality of the additive noise in gene expression is justified by
the findings in \citep{Hochreiter:06}.

The unity matrix as covariance matrix for $\tilde\z$ may be violated
by overlapping biclusters, however we want to avoid to divide a real
bicluster into two factors. Thus, we prefer uncorrelated factors
over more sparseness. The factors can be decorrelated by
setting
$\hat{\z} := \A^{-1}\ \tilde\z$ and $\hat{\La} := \La \ \A$ with the
symmetric invertible matrix $\A^2=\E\big(\tilde\z \ \tilde\z^T\big)$:
\begin{align*}
& \La \ \z \ = \  \La \ \A \ \A^{-1} \ \z \ \ = \ \hat{\La}  \
\hat{\z} \ \ \ \ \text{~and}\\
& \E\big(\hat{\z} \ \hat{\z}^T\big) \ = \ \A^{-1}\
\E\big(\tilde\z \ \tilde\z^T\big) \
\A^{-1} \ = \ \A^{-1}  \A^2 \A^{-1} \ = \I \ .
\end{align*}

Standard factor analysis does not consider sparse factors and sparse
loadings which are essential in our formulation to represent biclusters.
Sparseness is obtained by a
component-wise independent {\em Laplace}
distribution \citep{Hyvarinen:99a}, which is now used as
a prior on the factors $\tilde\z$ instead of the Gaussian:
\begin{align*}
p(\tilde\z) \ = \ \left( {\textstyle\frac{1}{\sqrt{2}}} \right)^p
\prod_{i=1}^{p}
    e^{-\ \sqrt{2} \ |\tilde z_i|}
\end{align*}
Sparse loadings $\la_i$ and, therefore sparse $\La$,
are achieved by two
alternative strategies. In the first model, called \textsf{FABIA},
we assume a component-wise independent {\em Laplace}
prior for the loadings (like for the factors):
\begin{align}
p(\la_i) \ = \ \left( {\textstyle\frac{1}{\sqrt{2}}} \right)^n
\prod_{k=1}^{n} e^{-\ \sqrt{2} \
|\lambda_{ki}|}\label{eq:laplaceprior}
\end{align}
The \textsf{FABIA} model contains the product of Laplacian variables
which is distributed proportionally to the $0$-th order modified
Bessel function of the second kind \citep{Bithas:07}. For large
values, this Bessel function is a negative exponential function of
the square root of the random variable. Therefore, the tails of the
distribution are heavier than those of the Laplace distribution. The
Gaussian noise, however, reduces the heaviness of the tails such
that the heaviness is between Gaussian and Bessel function tails ---
about as heavy as the tails of the Laplacian distribution. These
{\em heavy tails} are exactly the desired model characteristics.

The second model, called \textsf{FABIAS}, applies a
prior with parameter spL on the loadings that has only support at sparse regions.
Following \citep{Hoyer:04}, we define sparseness as
\begin{align*}
\mathrm{sp}(\la_i) \ = \ \frac{\sqrt{n} \ - \
    \sum_{k=1}^{n} \left|\lambda_{ki} \right|\ / \  \sum_{k=1}^{n}
    \lambda_{ki}^2}{\sqrt{n} \ - \ 1}
\end{align*}
leading to the prior
\begin{align}
p(\la_i) \ = \
\begin{cases}
c &  \text{for~~~~} \mathrm{sp}(\la_i) \ \leq \ \mathrm{spL} \\
0 &  \text{for~~~~} \mathrm{sp}(\la_i) \ > \ \mathrm{spL}
\end{cases}
\ .  \label{eq:sparseness}
\end{align}


\subsection{Sparse Coding and Laplace Prior}

Laplace prior enforces sparse codes on the factors.
\emph{Sparse coding}
is the representation of items by the
\emph{strong activation}
of a relatively
\emph{small set}
of hidden factors while the factors are
\emph{almost constant}
if not activated.

Laplace prior is suited for modeling
strong activation for few samples while being otherwise
almost constant.
Fig.\ \ref{fig:lap1} left shows the Laplacian mode compared to a Gaussian
mode. The Laplacian mode is higher and narrower.
Fig.\ \ref{fig:lap1} right shows the tails of Gaussian and
Laplace distribution, where the latter has higher values. This means
for the Laplace distribution large values are more likely than for a
Gaussian.
\begin{figure}
\begin{center}
\includegraphics[width=0.45\textwidth]{fabia-lap1}
\includegraphics[width=0.45\textwidth]{fabia-lap2}
\end{center}
\caption{Left: The mode of a Laplace (red, solid) vs. a Gaussian
  (dashed, blue) distribution. Right:
    The tails of a Laplace (red, solid) vs. a Gaussian (dashed, blue) distribution}
\label{fig:lap1}
\end{figure}


% \begin{figure}
% \begin{center}
% <<label=lap1,echo=F,fig=TRUE>>=
% t<-0.2
% s<-1
% curve(1/(sqrt(2)*t)*exp(-sqrt(2)/t*abs(x)),from=-6,to=6,ylab="",col="red",lty=1,lwd=3)
% curve(1/(sqrt(2*pi)*s)*exp(-0.5*1/s^2*x^2),add=TRUE,col="blue",lty=2,lwd=3)
% @
% \end{center}
% \caption{Mode: Laplace (red, solid) vs. Gaussian (dashed, blue)}
% \label{fig:lap1}
% \end{figure}

% Fig.\ \ref{fig:lap2} shows the tails
% where it can be seen that Laplace has higher likelihood
% for large values than Gaussian.
% \begin{figure}
% \begin{center}
% <<label=lap2,echo=F,fig=TRUE>>=
% t<-0.2
% s<-1
% curve(1/(sqrt(2)*t)*exp(-sqrt(2)/t*abs(x)),from=14.2,to=16,ylab="",col="red",lty=1,lwd=3)
% curve(1/(sqrt(2*pi)*s)*exp(-0.5*1/s^2*x^2),add=TRUE,col="blue",lty=2,lwd=3)
% @
% \end{center}
% \caption{Tails: Laplace (red, solid) vs. Gaussian (dashed, blue)}
% \label{fig:lap2}
% \end{figure}



\subsection{Model Selection}
\label{sec:selection}


The free parameters $\La$ and $\pp$ can be estimated by
Expectation-Maximization (EM; \citealp{Dempster:77}). With a prior
probability on the loadings, the a posteriori of the parameters is
maximized like in \citep{Hochreiter:06,Talloen:07}.

\subsubsection{Variational Approach for Sparse Factors}

Model selection is not straightforward because the likelihood
\begin{align*}
p( \x \mid \La, \pp ) \ = \ \int p( \x \mid \tilde\z, \La, \pp) \ p(
\tilde\z) \ d\tilde\z
\end{align*}
cannot be computed analytically for a Laplacian prior $p(\tilde\z)$.
We employ a variational approach according to \citet{Girolami:01}
and \citet{Palmer:06} for model selection. They introduce a model
family that is parametrized by $\xii$, where the maximum over models
in this family is the true likelihood:
\begin{align*}
\underset{\xii}{\arg\max} \ p(\x|\xii) \ = \ \log p(\x) \ .
\end{align*}
Using an EM algorithm, not only the likelihood with respect to the
parameters $\La$ and $\pp$ is maximized, but also with respect to
$\xii$.

In the following, $\La$ and $\pp$ denote the actual parameter
estimates. According to \citet{Girolami:01} and \citet{Palmer:06},
we obtain
\begin{align*}
\E \big(\tilde\z_j   \mid \x_j \big) \ = \ & \big( \La^T \
  \pp^{-1} \ \La \
  + \  \Xii_j^{-1} \big)^{-1} \ \La^T \  \pp^{-1} \ \x_j \ \ \mbox{~~and} \\
\E \big(\tilde\z_j \ \tilde\z_j^T  \mid  \x_j \big) \ = \ & \big(
\La^T \
  \pp^{-1} \ \La \
  + \  \Xii_j^{-1} \big)^{-1} \ + \\
& \E \big(\tilde\z_j   \mid  \x_j \big)  \ \E (\tilde\z_j  \mid
\x_j)^T \ ,
\end{align*}
where $\Xii_j$ stands for $\mathrm{diag} \left(\xii_j \right)$. The update for
$\xii_j$ is
\begin{align*}
\xii_{j} \ = \  \mathrm{diag}\left( \sqrt{\E (\tilde\z_j \
\tilde\z_j^T \mid \x_j)} \right) \ .
\end{align*}

\subsubsection{New Update Rules for Sparse Loadings}

The update rules for \textsf{FABIA} (Laplace prior on
loadings) are
\begin{align}
\La^{\nn} \ = \  \frac{\frac{1}{l} \ \sum_{j=1}^{l} \x_j \ \E
(\tilde\z_j \mid  \x_j)^T  \ - \ \frac{\alpha}{l} \  \pp \ \sign
(\La
  )}{\frac{1}{l} \sum_{j=1}^{l} \E (\tilde\z_j \ \tilde\z_j^T \mid \x_j
  )} \label{eq:update}\\
\mathrm{diag} \left(\pp^{\nn}  \right) \ = \ \pp^{\mathrm{EM}} \ +
\ \mathrm{diag} \Big(\frac{\alpha}{l} \  \pp \ \sign (\La
  ) ( \La^{\nn} )^T \Big) \nonumber
\end{align}
where
\begin{align*}
\pp^{\mathrm{EM}} \  = \ \mathrm{diag} \bigg( \frac{1}{l}
\sum_{j=1}^l \x_j  \x_j^T \ - \ \La^{\nn}  \frac{1}{l} \sum_{j=1}^l
 \E \left(  \tilde\z_j   \mid  \x_j \right) \ \x_{j}^{T} \bigg)  .
\end{align*}

The update rules for
\textsf{FABIAS} must take into account that
each $\la_i$ from $\La$ has a prior with restricted
support. Therefore the sparseness constraints $\mathrm{sp}(\la_i) \leq \mathrm{spL}$
from Eq.\ \eqref{eq:sparseness} hold.
These constraints are ensured by a
projection of $\la_i$ after each $\La$ update according to \citet{Hoyer:04}.
The projection is a convex quadratic problem which
minimizes the Euclidean distance to the
original vector subject to the
constraints. The projection problem can be solved fast by an iterative
procedure where the $l_2$-norm of the vectors is fixed to 1.
We update $\mathrm{diag} (\pp^{\nn}) = \pp^{\mathrm{EM}}$ and
project each updated prototype vector to a sparse vector with
sparseness spL giving the
overall projection:
\begin{align*}
\La^{\mathrm{new}} \ = \   \mathrm{proj} \left(\frac{\frac{1}{l} \
\sum_{j=1}^{l}  \x_j \ \E \left(\tilde\z_j  \mid \x_j
  \right)^T }{\frac{1}{l} \sum_{j=1}^{l} \E (\tilde\z_j \ \tilde\z_j^T \mid \x_j
  )}, \mathrm{spL} \right)
\end{align*}

\subsubsection{Extremely Sparse Priors}

Some gene expression data sets are sparser than Laplacian. For
example, during estimating DNA copy numbers with Affymetrix SNP 6
arrays, we observed a kurtosis larger than 30 (FABIA results shown
at
\href{http://www.bioinf.jku.at/software/fabia/fabia.html}{http://www.bioinf.jku.at/software/fabia/fabia.html}).
We want to adapt our model class to deal with such sparse data sets.
Toward this end, we define extremely sparse priors both on the
factors and the loadings utilizing the following (pseudo)
distributions:
\begin{quote}
\vspace{-0.2cm}
\begin{tabular}{ll}
Generalized Gaussians: & $p(z) \ \propto \ \exp \left( - \
  |z|^{\beta} \right)$ \\
Jeffrey's prior: &  $p(z) \ \propto \ \exp \left( - \ \ln|z|
\right)  \  =  \ 1/|z|$ \\
Improper prior: &  $p(z) \ \propto \ \exp \left( |z|^{-\beta}
\right)$
\end{tabular}
\end{quote}
\vspace{-0.2cm}
For the first distribution, we assume $0<\beta\leq 1$ and for the
third $0<\beta$. Note, the third distribution may only exist on the
interval $[\epsilon,a]$ with $0<\epsilon<a$. We assume that
$\epsilon$ is sufficiently small.

For the {\em loadings}, we need the derivatives of the negative
log-distributions for optimizing the log-posterior. These
derivatives are proportional to $|z|^{-\mathrm{spl}}$, where
$\mathrm{spl}=0$ corresponds to the Laplace prior and
$\mathrm{spl}>0$ to sparser priors. The update rule is as in
Eq.~\eqref{eq:update}, where $\sign(\La)$ is replaced by
$|\La|^{-\mathrm{spl}} \ \sign (\La)$ with element-wise operations
(absolute value, sign, exponentiation, multiplication).

For the {\em factors}, we represent the priors through a convex
variational form according to \citet{Palmer:06}. That is possible
because $g(z)=-\ln p(\sqrt{z})$ is increasing and concave for $z>0$
(first order derivatives are larger and second order smaller than
zero). According to \citet{Palmer:06}, the update for $\xii_j$ is
\begin{align*}
\xii_{j} \ \propto \ \mathrm{diag} \Big( \E \big(\tilde\z_j \
\tilde\z_j^T \mid  \x_j \big)^{\mathrm{spz}} \Big)
\end{align*}
for all $\mathrm{spz}\geq 1/2$, where $\mathrm{spz}=1/2$ ($\beta=1$)
represents the Laplace prior and $\mathrm{spz}>1/2$ leads to sparser
priors.

\subsection{Data Preprocessing and Initialization}

The data $\x$ may be centered either to zero mean or to
zero median which we prefer to obtain sparser raw data.
Then the data should be scaled to unit second moments
to allow initialization of the parameters in the same range.
See the supplementary for justification of these
preprocessing steps.

The iterative model selection procedure requires initialization of
the parameters $\La$, $\pp$, and  $\xii_{j}$. The simplest strategy
is to initialize $\La$ randomly while ensuring that
\begin{align*}
\pp\ =\ \mathrm{diag} \Big( \mathrm{covar}(\x)\ -\ \La\ \La^T \Big)\ \geq\ \delta\
>\ 0.
\end{align*}
The variational parameter vectors $\xii_{j}$ are initialized
as vectors of ones. An alternative initialization strategy can be
based on ICA. The ICA solution supplies factors $\Z_{\mathrm{ICA}}$
that are sparse and decorrelated.



\subsection{Information Content of Biclusters}
\label{sec:info}

A highly desired property for biclustering algorithms is the ability
to rank the extracted biclusters analogously to principal components
which are ranked according to the data variance they explain. We
rank biclusters according to the information they contain about the
data. The information content of $\tilde\z_j$ for the $j$-th
observation $\x_j$ is the mutual information between $\tilde\z_j$
and $\x_j$:
\begin{align*}
\mathrm{I} (\x_j;\tilde\z_j) \ = \  \mathrm{H} (\tilde\z_j) \ - \
\mathrm{H} (\tilde\z_j \mid \x_j) \ = \frac{1}{2} \ \ln\big| \I_p \
+ \ \Xii_j \  \La^T \  \pp^{-1} \ \La \big|
\end{align*}
The independence of $\x_j$ and $\tilde\z_j$ across $j$ gives
\begin{align*}
\mathrm{I} (\X;\Z) \ = \ \frac{1}{2} \ \sum_{j=1}^{l}  \ln\big| \I_p
\ + \   \Xii_j \ \La^T \
  \pp^{-1} \ \La\big|\ .
\end{align*}
For the FARMS summarization algorithm ($p=1$ and  $\Xii_j=1$), this
information is the negative logarithm of the I/NI call
\citep{Talloen:07}.

To assess the information content of one factor, we consider the
case that factor $\tilde z_i$ is removed from the final model. This
corresponds to setting $\xi_{ij}=0$ (by $\xi_{ij}$, we denote the
$i$-th entry in $\xii_j$) and therefore the explained covariance
$\xi_{ji} \ \la_i \ \la_i^T$ is removed:
\begin{align*}
\x_j \mid (\tilde\z_j \setminus z_{ij}) \ \sim \ \Ncal\big( \La \
\tilde\z_j|_{z_{ij}=0} \ , \ \pp \ + \
  \xi_{ij} \ \la_i \ \la_i^T \big)
\end{align*}
The information of $z_{ij}$ given the other factors is
\begin{align*}
\mathrm{I}(\x_j;z_{ij} \mid (\tilde\z_j \setminus z_{ij}) ) \ &= \
\mathrm{H}(z_{ij} \mid (\tilde\z_j \setminus z_{ij})) -
\mathrm{H}(z_{ij} \mid (\tilde\z_j \setminus z_{ij}),\x_j ) \\
& = \ \frac{1}{2} \ \ln\big(1 \ + \ \xi_{ij} \ \la_i^T \pp^{-1}
  \la_i \big)  \ .
\end{align*}
Again independence across $j$ gives
\begin{align*}
\mathrm{I} (\X;\z_i^T \mid (\Z \setminus \z_{i}^T) ) \ = \
\frac{1}{2} \ \sum_{j=1}^{l}  \ \ln\left(  1 \ + \   \xi_{ij} \
\la_i^T \pp^{-1} \la_i \right) \ .
\end{align*}
This information content gives that part of information in $\x$ that
$\z_i^T$ conveys across all examples. Note that also the number of
nonzero $\la_i$'s (size of the bicluster) enters the information
content.

\subsection{Extracting Members of Biclusters}
\label{sec:extract}

After model selection in Section \ref{sec:selection} and ranking of
bicluster in Section \ref{sec:info}, the $i$-th bicluster has soft
gene memberships given by the absolute values of $\la_i$
and soft sample memberships given by the absolute values of $\z_i^T$.

However, applications may need hard memberships. We
determine the members of bicluster $i$ by selecting absolute values
$\lambda_{ki}$ and $z_{ij}$ above thresholds $\mathrm{thresL}$ and
$\mathrm{thresZ}$, respectively.

First, the second moment of each factor is normalized to 1 resulting
in a factor matrix $\hat{\Z}$ (in accordance with
$\E(\tilde\z\tilde\z^T)=\I$). Consequently, $\La$ is rescaled to
$\hat{\La}$ such that $\La \Z = \hat{\La} \hat{\Z}$. Now the
threshold $\mathrm{thresZ}$ can be chosen to determine which
percentage of samples will on average belong to a bicluster. For a
Laplace prior, this percentage can be computed by $\frac{1}{2}
\exp(-\sqrt{2}/\mathrm{thresZ})$.

In the default setting, for each factor $\hat\z_i$, only one
bicluster is extracted. In gene expression, an expression pattern is
either absent or present but not negatively present. Therefore, the
$i$-th bicluster is either determined by the positive or negative
values of $\hat z_{ij}$. Which one of these two possibilities is
chosen is decided by whether the sum over $\big| \hat z_{ij} \big| >
\mathrm{thresZ}$ is larger for the positive or negative $\hat
z_{ij}$.

The threshold $\mathrm{thresL}$ for the loadings is more difficult
to determine, because normalization would lead to a rescaling of the
already normalized factors. Since biclusters may overlap, the
contribution of $\lambda_{ki} z_{ij}$ that are relevant must be
estimated. Therefore, we first estimate the standard deviation of
$\La\Z$ by
\begin{align*}
\mathrm{sdLZ} \ = \ \sqrt{\frac{1}{p \ l \ n}
\sum_{(i,j,k)=(1,1,1)}^{(p,l,n)}  \big(\hat{\lambda}_{ki} \
\hat{z}_{ij}\big)^2} \ .
\end{align*}
We set this standard deviation to the product of
both thresholds which is solved for $\mathrm{thresL}$:
$\mathrm{thresL}=\mathrm{sdLZ}\ /\
\mathrm{thresZ}$.
However, an optimal $\mathrm{thresL}$ depends on
the sparseness parameters and on the characteristics of the
biclustering problem.



\subsection{C implementation of FABIA}

The functions \Rfunction{fabia} and  \Rfunction{fabias} are
implemented in C. It turned out that these implementations are not
only faster, but also more precise. Especially we use an efficient Cholesky
decomposition to compute the inverse of positive definite matrices.
Some \R functions for computing the inverse like  \Rfunction{solve}
were inferior to that implementation.

The interface between \R and C is realized by the package \Rpackage{Rcpp}
\citep{Samperi:06}.

\appendix

\section{Class Factorization}

\texttt{Factorization} is the class structure for results of matrix
  factorization. Especially it is designed for factor anaylsis used for
  biclustering. 
  
  The \texttt{summary} method shows information about biclusters.
  The \texttt{show} method plots information about biclusters.
  The \texttt{showSelected} method plots selected information about biclusters.
  The \texttt{plot} method produces biplots of biclusters.
  
 
  Objects can be created by \texttt{fabia}, \texttt{fabias}, \texttt{fabiap},
  \texttt{fabiasp}, \texttt{mfsc},  \texttt{nmfsc},  
  \texttt{nmfdiv}, and \texttt{nmfeu}.



  Objects of class \texttt{Factorization} have the following slots:
  \begin{enumerate}    
    \item{\texttt{parameters}:} Saves parameters of the factorization
    method in a list:\\ 
    \noindent
    ``method'',\\ 
    \noindent
    ``number of cycles'',\\ 
    \noindent
    ``sparseness weight'',\\ 
    \noindent
    ``sparseness prior for loadings'',\\ 
    \noindent
    ``sparseness prior for factors'',\\ 
    \noindent
    ``number biclusters'',\\ 
    \noindent
    ``projection sparseness loadings'',\\ 
    \noindent
    ``projection sparseness factors'',\\ 
    \noindent
    ``initialization range'',\\ 
    \noindent
    ``are loadings rescaled after each iterations'',\\ 
    \noindent
    ``normalization = scaling of rows'',\\ 
    \noindent
    ``centering method of rows'',\\ 
    \noindent
    ``parameter for method''.
    
           \item{\texttt{n}:} number of rows, left dimension.
           \item{\texttt{p1}:} right dimension of left matrix.
           \item{\texttt{p2}:} left dimension of right matrix.
           \item{\texttt{l}:} number of columns, right dimension.
           \item{\texttt{center}:} vector of the centers.
           \item{\texttt{scaleData}:} vector of the scaling factors.
	   \item{\texttt{X}:} centered and scaled data matrix $n \times l$.
           \item{\texttt{L}:} left matrix $n \times p1$.
           \item{\texttt{Z}:} right matrix $p2 \times l$.
           \item{\texttt{M}:} middle matrix $p1 \times p2$.
           \item{\texttt{LZ}:} matrix $\La \bf{M} \Z$ .
           \item{\texttt{U}:} noise matrix.
           \item{\texttt{avini}:}  information of each bicluster, vector
           of length p2.
           \item{\texttt{xavini}:}  information extracted 
             from each sample, vector of length $l$.
           \item{\texttt{ini}:} information of each bicluster in each
           sample, matrix $p2 \times l$.
           \item{\texttt{Psi}:} noise variance per row, vector of length $n$.
           \item{\texttt{lapla}:} prior information for each sample,
           vector of length $l$.   
   \end{enumerate}

  
  This class contains the result of different matrix factorization
  methods. The methods may be generative or not.

  Methods my be ``singular value decomposition'' ($\bf{M}$ 
  contains singular values
  as well as avini, $\La$ and $\Z$ are orthonormal matrices),
  ``independent component analysis'' ($\Z$ contains the projection/sources, 
  $\La$ is the
  mixing matrix, $\bf{M}$ is unity), ``factor analysis'' 
  ($\Z$ contains factors, $\La$ the loadings,
  $\bf{M}$ is unity, $\bf{U}$ the noise, $\pp$ the noise covariance, lapla is a
  variational parameter for non-Gaussian factors, avini and ini are the
  information the factors convey about the observations).      
  



\section{Biclustering and Matrix Factorization Methods}


\subsection{fabi}

Factor Analysis for Bicluster Acquisition: Laplace Prior (FABI) \citep{Hochreiter:10}.

\R implementation of  \texttt{fabia}, therefore it is \emph{slow}.

\begin{enumerate}

\item Usage:
\texttt{fabi(X,p=5,alpha=0.1,cyc=500,spl=1.0,spz=1.0,center=2,norm=1)}

\item Arguments:
 \begin{itemize}
  \item{X:} the data matrix.
  \item{p:} number of hidden factors = number of biclusters; default = 5.
  \item{alpha:} sparseness loadings (0.1 - 1.0); default = 0.1.
  \item{cyc:} number of iterations; default = 500.
  \item{spl:} sparseness prior loadings (0.5 - 4.0): default = 1.0.
  \item{spz:} sparseness factors (0.5 - 4.0); default = 1.0.
  \item{center:} data centering: 1 (mean), 2
    (median), > 2 (mode), 0 (no); default = 2.
  \item{norm:} data normalization:  1 (0.75-0.25 quantile), >1 (var=1), 0 (no); default = 1.
\end{itemize}

\item Return Values:
\begin{itemize}
 \item object of the class \texttt{Factorization}. Containing
  \texttt{LZ} (estimated noise free data $\La  \Z$),
  \texttt{L} (loadings  $\La$),
  \texttt{Z} (factors $\Z$),
  \texttt{U} (noise  $\X- \La \Z$),
  \texttt{center} (centering vector),
  \texttt{scaleData} (scaling vector),
  \texttt{X} (centered and scaled data $\X$),
  \texttt{Psi} (noise variance $\pp$),
  \texttt{lapla} (variational parameter),
  \texttt{avini} (the information which the factor
    $z_{ij}$ contains about $\x_j$ averaged over $j$)
  \texttt{xavini} (the information which the factor
    $\tilde\z_j$ contains about $\x_j$ averaged over $j$)
  \texttt{ini} (for each $j$ the information which the factor
    $z_{ij}$ contains about $\x_j$).

\end{itemize}

\end{enumerate}

  Biclusters are found by sparse factor analysis where \emph{both} the factors
  and the loadings are sparse.

  Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ + \ \Up \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z \ + \ \Up \ .
  \end{align*}

   Here $\X \in \Rb^{n \times l}$ and $\Up \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.

  If the nonzero components of
  the sparse vectors are grouped together then the outer product results in
  a matrix with a nonzero block and zeros elsewhere.


  For a single data vector  $\x$ that is
  \begin{align*}\x \ = \ \sum_{i=1}^{p} \la_i z_i  \ + \
    \epp \ = \ \La \tilde\z \ + \
    \epp\end{align*}

  The model assumptions are:

  \emph{Factor Prior is Independent Laplace:}
  \begin{align*}p(\tilde\z) \ = \ \left( \frac{1}{\sqrt{2}} \right)^p \prod_{i=1}^{p}
    e^{-\ \sqrt{2} \ |z_i|}\end{align*}

  \emph{Loading Prior is Independent Laplace:}
   \begin{align*}p(\la_i) \ = \ \left( \frac{1}{\sqrt{2}} \right)^n \prod_{k=1}^{n}
    e^{-\ \sqrt{2} \ |\lambda_{ki}|}\end{align*}


  \emph{Noise: Gaussian independent}
  \begin{align*}p(\epp) \ = \ \left( \frac{1}{\sqrt{2 \ \pi}} \right)^n
    \prod_{k=1}^{n}  \frac{1}{\sigma_k}
     e^{\sum_{k=1}^{n}  \frac{\epsilon_k^2}{\sigma_k^2}} \end{align*}

  \emph{Data Mean:}

    \begin{align*}\E(\x) \ = \ \E(\La \ \tilde\z \ +
      \ \epp ) \ = \ \La \ \E( \tilde\z) \ +
      \ \E(\epp) \ = \ \0 \end{align*}


      \emph{Data Covariance:}

 \begin{align} \nonumber
    \E(\x \ \x^T) \ &= \
      \La \E( \tilde\z  \tilde\z^T) \La^T \ + \
       \La \E( \tilde\z)\E(\epp^T) \ + \
       \E( \tilde\z)\E(\epp) \La^T \ + \
       \E(\epp \ \epp^T) \\\nonumber &= \
       \La \La^T \ + \ \mathrm{diag}(\sigma_k^2)
\end{align}



      Normalizing the data to variance one for each component gives
       \begin{align*}\sigma_k^2 \ + \ \left(\la^{k}\right)^T \la^{k} \ =
     \ 1\end{align*}
      Here $\la^{k}$ is the $k$-th row of $\La$ (which is a row vector of length $p$).
      We recommend to \emph{normalize the components to variance one} in order
      to make the signal and noise comparable across components.


      \emph{Estimated Parameters:}
       $\La$ and $\pp$ ($\sigma_k$)

       \emph{Estimated Latent Variables:}
        $\Z$

    \emph{Estimated Noise Free Data:}
        $\La \ \Z$

    \emph{Estimated Biclusters:}
        $\la_i \ \z_i^T $
     Larges values give the bicluster (ideal the nonzero values).



       The model selection is performed by a variational approach according
  to \citep{Girolami:01} and \citep{Palmer:06}.

  We included a prior on the parameters and minimize
  a lower bound on the posterior
  of the parameters given the data.
  The update of the loadings includes an additive term which pushes the
  loadings toward zero (Gaussian prior leads to an multiplicative factor).

  The code is implemented in \R, therefore it is \emph{slow}.



\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]


resEx <-  fabi(X,3,0.1,20,1.0,1.0)



#---------------
# DEMO1
#---------------

dat <- make_fabi_data_blocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]


resToy <- fabi(X,13,0.1,200,1.0,1.0)

rToy <- extract_plot(resToy,ti="FABI",Y=Y)

#---------------
# DEMO2
#---------------

data(Breast_A)

X <- as.matrix(XBreast)


resBreast <- fabi(X,5,0.1,200,1.0,1.0)

rBreast <- extract_plot(resBreast,ti="FABI Breast cancer(Veer)")

#sorting of predefined labels
CBreast%*%rBreast$pmZ

#---------------
# DEMO3
#---------------


data(Multi_A)

X <- as.matrix(XMulti)

resMulti <- fabi(X,5,0.1,200,1.0,1.0)

rMulti <- extract_plot(resMulti,ti="FABI Multiple tissues(Su)")

#sorting of predefined labels
CMulti%*%rMulti$pmZ

#---------------
# DEMO4
#---------------


data(DLBCL_B)

X <- as.matrix(XDLBCL)


resDLBCL <- fabi(X,5,0.1,200,1.0,1.0)

rDLBCL <- extract_plot(resDLBCL,ti="FABI Lymphoma(Rosenwald)")

#sorting of predefined labels
CDLBCL%*%rDLBCL$pmZ


\end{Sinput}




\subsection{fabia}

Factor Analysis for Bicluster Acquisition: Laplace Prior (FABIA) \citep{Hochreiter:10}.

C implementation of  \texttt{fabia}.

\begin{enumerate}
\item Usage:
\texttt{fabia(X,p=5,alpha=0.1,cyc=500,spl=1.0,spz=1.0,random=1.0,center=2,norm=1,scale=0.0,lap=1.0)}

\item Arguments:
 \begin{itemize}
  \item{X:} the data matrix.
  \item{p:} number of hidden factors = number of biclusters; default = 5.
  \item{alpha:} sparseness loadings (0.1 - 1.0); default = 0.1.
  \item{cyc:} number of iterations; default = 500.
  \item{spl:} sparseness prior loadings (0.5 - 4.0); default = 1.0.
  \item{spz:} sparseness factors (0.5 - 4.0); default = 1.0.
  \item{random} <=0: by SVD, >0: random initialization of loadings in
    [-random,random]; default = 1.0.
  \item{center:} data centering: 1 (mean), 2
    (median), > 2 (mode), 0 (no); default = 2.
  \item{norm:} data normalization:  1 (0.75-0.25 quantile), >1 (var=1), 0 (no); default = 1.
  \item{scale:} loading vectors are scaled in each iteration to the given
    variance. 0.0 indicates non scaling; default = 0.0.
  \item{lap:} minimal value of the variational parameter, default = 1.
 \end{itemize}

\item Return values:
\begin{itemize}
 \item object of the class \texttt{Factorization}. Containing
  \texttt{LZ} (estimated noise free data $\La  \Z$),
  \texttt{L} (loadings  $\La$),
  \texttt{Z} (factors $\Z$),
  \texttt{U} (noise  $\X- \La \Z$),
  \texttt{center} (centering vector),
  \texttt{scaleData} (scaling vector),
  \texttt{X} (centered and scaled data $\X$),
  \texttt{Psi} (noise variance $\pp$),
  \texttt{lapla} (variational parameter),
  \texttt{avini} (the information which the factor
    $z_{ij}$ contains about $\x_j$ averaged over $j$)
  \texttt{xavini} (the information which the factor
    $\tilde\z_j$ contains about $\x_j$ averaged over $j$)
  \texttt{ini} (for each $j$ the information which the factor
    $z_{ij}$ contains about $\x_j$).
\end{itemize}

\end{enumerate}

  Biclusters are found by sparse factor analysis where \emph{both} the factors
  and the loadings are sparse.

  Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ + \ \Up \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z \ + \ \Up \ .
  \end{align*}

  Here $\X \in \Rb^{n \times l}$ and $\Up \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.

  If the nonzero components of
  the sparse vectors are grouped together then the outer product results in
  a matrix with a nonzero block and zeros elsewhere.


  For a single data vector  $\x$ that is
  \begin{align*}\x \ = \ \sum_{i=1}^{p} \la_i z_i  \ + \
    \epp \ = \ \La \tilde\z \ + \
    \epp\end{align*}

  The model assumptions are:

  \emph{Factor Prior is Independent Laplace:}
  \begin{align*}p(\tilde\z) \ = \ \left( \frac{1}{\sqrt{2}} \right)^p \prod_{i=1}^{p}
    e^{-\ \sqrt{2} \ |z_i|}\end{align*}

  \emph{Loading Prior is Independent Laplace:}
   \begin{align*}p(\la_i) \ = \ \left( \frac{1}{\sqrt{2}} \right)^n \prod_{k=1}^{n}
    e^{-\ \sqrt{2} \ |\lambda_{ki}|}\end{align*}


  \emph{Noise: Gaussian independent}
  \begin{align*}p(\epp) \ = \ \left( \frac{1}{\sqrt{2 \ \pi}} \right)^n
    \prod_{k=1}^{n}  \frac{1}{\sigma_k}
     e^{\sum_{k=1}^{n}  \frac{\epsilon_k^2}{\sigma_k^2}} \end{align*}

  \emph{Data Mean:}

    \begin{align*}\E(\x) \ = \ \E(\La \ \tilde\z \ +
      \ \epp ) \ = \ \La \ \E( \tilde\z) \ +
      \ \E(\epp) \ = \ \0\end{align*}


      \emph{Data Covariance:}

  \begin{align} \nonumber
    \E(\x \ \x^T) \ &= \
      \La \E( \tilde\z  \tilde\z^T) \La^T \ + \
       \La \E( \tilde\z)\E(\epp^T) \ + \
       \E( \tilde\z)\E(\epp) \La^T \ + \
       \E(\epp \ \epp^T) \\\nonumber &= \
      \La \La^T \ + \ \mathrm{diag}(\sigma_k^2)
\end{align}

      Normalizing the data to variance one for each component gives
       \begin{align*}\sigma_k^2 \ + \ \left(\la^{k}\right)^T \la^{k} \ =
     \ 1\end{align*}
      Here $\la^{k}$ is the $k$-th row of $\La$ (which is a row vector of length $p$).
      We recommend to \emph{normalize the components to variance one} in order
      to make the signal and noise comparable across components.


      \emph{Estimated Parameters:}
       $\La$ and $\pp$ ($\sigma_k$)

       \emph{Estimated Latent Variables:}
        $\Z$

    \emph{Estimated Noise Free Data:}
        $\La \ \Z$

    \emph{Estimated Biclusters:}
        $\la_i \ \z_i^T $
     Larges values give the bicluster (ideal the nonzero values).


       The model selection is performed by a variational approach according
  to \citep{Girolami:01} and \citep{Palmer:06}.

  We included a prior on the parameters and minimize
  a lower bound on the posterior
  of the parameters given the data.
  The update of the loadings includes an additive term which pushes the
  loadings toward zero (Gaussian prior leads to an multiplicative factor).

  The code is implemented in C using the Rcpp package.



\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]



resEx <- fabia(X,3,0.1,50,1.0,1.0)


#-----------------
# DEMO1: Toy Data
#-----------------

n = 1000
l= 100
p = 10

dat <- make_fabi_data_blocks(n = n,l= l,p = p,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]
ZC <- dat[[3]]
LC <- dat[[4]]

gclab <- rep.int(0,l)
gllab <- rep.int(0,n)
clab <- as.character(1:l)
llab <- as.character(1:n)
for (i in 1:p){
 for (j in ZC[i]){
     clab[j] <- paste(as.character(i),"_",clab[j],sep="")
 }
 for (j in LC[i]){
     llab[j] <- paste(as.character(i),"_",llab[j],sep="")
 }
 gclab[unlist(ZC[i])] <- gclab[unlist(ZC[i])] + p^i
 gllab[unlist(LC[i])] <- gllab[unlist(LC[i])] + p^i
}


groups <- gclab



#### FABIA

resToy1 <- fabia(X,13,0.1,400,1.0,1.0)

rToy1 <- extract_plot(resToy1,ti="FABIA",Y=Y)

raToy1 <- extract_bic(resToy1)

plotBicluster(raToy1,1)
plotBicluster(raToy1,2)
plotBicluster(raToy1,3)
plotBicluster(raToy1,4)



colnames(resToy1@X) <- clab

rownames(resToy1@X) <- llab


plot(resToy1,dim=c(1,2),label.tol=0.1,col.group = groups,lab.size=0.6)
plot(resToy1,dim=c(1,3),label.tol=0.1,col.group = groups,lab.size=0.6)
plot(resToy1,dim=c(2,3),label.tol=0.1,col.group = groups,lab.size=0.6)


#------------------------------------------
# DEMO2: Laura van' Veer's gene expression  
#        data set for breast cancer 
#------------------------------------------

data(Breast_A)

X <- as.matrix(XBreast)


resBreast1 <- fabia(X,5,0.1,400,1.0,1.0)

rBreast1 <- extract_plot(resBreast1,ti="FABIA Breast cancer(Veer)")


raBreast1 <- extract_bic(resBreast1)

plotBicluster(raBreast1,1)
plotBicluster(raBreast1,2)
plotBicluster(raBreast1,3)
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)


#-----------------------------------
# DEMO3: Su's multiple tisse types
#        gene expression data set 
#-----------------------------------


data(Multi_A)

X <- as.matrix(XMulti)

resMulti1 <- fabia(X,5,0.1,300,1.0,1.0)

rMulti1 <- extract_plot(resMulti1,ti="FABIA Multiple tissues(Su)")

raMulti1 <- extract_bic(resMulti1)

plotBicluster(raMulti1,1)
plotBicluster(raMulti1,2)
plotBicluster(raMulti1,3)
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)


#-----------------------------------------
# DEMO4: Rosenwald's diffuse large-B-cell
#        lymphoma gene expression data set 
#-----------------------------------------


data(DLBCL_B)

X <- as.matrix(XDLBCL)

resDLBCL1 <- fabia(X,5,0.1,400,1.0,1.0)

rDLBCL1 <- extract_plot(resDLBCL1,ti="FABIA Lymphoma(Rosenwald)")

raDLBCL1 <- extract_bic(resDLBCL1)

plotBicluster(raDLBCL1,1)
plotBicluster(raDLBCL1,2)
plotBicluster(raDLBCL1,3)
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)


\end{Sinput}

\subsection{fabiap}

Factor Analysis for Bicluster Acquisition: Post-Projection (FABIAP) \citep{Hochreiter:10}.

\begin{enumerate}

\item Usage: \verb+fabiap(X,p=5,alpha=0.1,cyc=500,spl=1.0,spz=1.0,sL=0.6,sZ=0.6,+\\
\hspace*{6em}\verb+   random=1.0,center=2,norm=1,scale=0.0,lap=1.0)+

\item Arguments:
 \begin{itemize}
  \item{X:} the data matrix.
  \item{p:} number of hidden factors = number of biclusters; default = 5.
  \item{alpha:} sparseness loadings (0.1 - 1.0); default = 0.1.
  \item{cyc:} number of iterations; default = 500.
  \item{spl:} sparseness prior loadings (0.5 - 4.0); default = 1.0.
  \item{spz:} sparseness factors (0.5 - 4.0); default = 1.0.
  \item{sL:} final sparseness loadings; default = 0.6.
  \item{sZ:} final sparseness factors; default = 0.6.
  \item{random} <=0: by SVD, >0: random initialization of loadings in
    [-random,random]; default = 1.0.
  \item{center:} data centering: 1 (mean), 2
    (median), > 2 (mode), 0 (no); default = 2.
  \item{norm:} data normalization:  1 (0.75-0.25 quantile), >1 (var=1), 0 (no); default = 1.
  \item{scale:} loading vectors are scaled in each iteration to the given
    variance. 0.0 indicates non scaling; default = 0.0.
  \item{lap:} minimal value of the variational parameter, default = 1.
\end{itemize}

\item  Return Values:
\begin{itemize}
 \item object of the class \texttt{Factorization}. Containing
  \texttt{LZ} (estimated noise free data $\La  \Z$),
  \texttt{L} (loadings  $\La$),
  \texttt{Z} (factors $\Z$),
  \texttt{U} (noise  $\X- \La \Z$),
  \texttt{center} (centering vector),
  \texttt{scaleData} (scaling vector),
  \texttt{X} (centered and scaled data $\X$),
  \texttt{Psi} (noise variance $\pp$),
  \texttt{lapla} (variational parameter),
  \texttt{avini} (the information which the factor
    $z_{ij}$ contains about $\x_j$ averaged over $j$)
  \texttt{xavini} (the information which the factor
    $\tilde\z_j$ contains about $\x_j$ averaged over $j$)
  \texttt{ini} (for each $j$ the information which the factor
    $z_{ij}$ contains about $\x_j$).
\end{itemize}



\end{enumerate}

  Biclusters are found by sparse factor analysis where \emph{both} the factors
  and the loadings are sparse. Post-processing by projecting the final
  results to a given sparseness criterion.

  Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ + \ \Up \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z \ + \ \Up \ .
  \end{align*}

  Here $\X \in \Rb^{n \times l}$ and $\Up \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.


  If the nonzero components of
  the sparse vectors are grouped together then the outer product results in
  a matrix with a nonzero block and zeros elsewhere.


  For a single data vector  $\x$ that is
  \begin{align*}\x \ = \ \sum_{i=1}^{p} \la_i z_i  \ + \
    \epp \ = \ \La \tilde\z \ + \
    \epp\end{align*}

  The model assumptions are:

  \emph{Factor Prior is Independent Laplace:}
  \begin{align*}p(\tilde\z) \ = \ \left( \frac{1}{\sqrt{2}} \right)^p \prod_{i=1}^{p}
    e^{-\ \sqrt{2} \ |z_i|}\end{align*}

  \emph{Loading Prior is Independent Laplace:}
   \begin{align*}p(\la_i) \ = \ \left( \frac{1}{\sqrt{2}} \right)^n \prod_{k=1}^{n}
    e^{-\ \sqrt{2} \ |\lambda_{ki}|}\end{align*}


  \emph{Noise: Gaussian independent}
  \begin{align*}p(\epp) \ = \ \left( \frac{1}{\sqrt{2 \ \pi}} \right)^n
    \prod_{k=1}^{n}  \frac{1}{\sigma_k}
     e^{\sum_{k=1}^{n}  \frac{\epsilon_k^2}{\sigma_k^2}} \end{align*}

  \emph{Data Mean:}

    \begin{align*}\E(\x) \ = \ \E(\La \ \tilde\z \ +
      \ \epp ) \ = \ \La \ \E( \tilde\z) \ +
      \ \E(\epp) \ = \ \0 \end{align*}


      \emph{Data Covariance:}

   \begin{align} \nonumber
    \E(\x \ \x^T) \ &= \
      \La \E( \tilde\z  \tilde\z^T) \La^T \ + \
       \La \E( \tilde\z)\E(\epp^T) \ + \
       \E( \tilde\z)\E(\epp) \La^T \ + \
       \E(\epp \ \epp^T) \\\nonumber &= \
    \La \La^T \ + \ \mathrm{diag}(\sigma_k^2)
\end{align}

      Normalizing the data to variance one for each component gives
       \begin{align*}\sigma_k^2 \ + \ \left(\la^{k}\right)^T \la^{k} \ =
     \ 1\end{align*}
      Here $\la^{k}$ is the $k$-th row of $\La$ (which is a row vector of length $p$).
      We recommend to \emph{normalize the components to variance one} in order
      to make the signal and noise comparable across components.


      \emph{Estimated Parameters:}
       $\La$ and $\pp$ ($\sigma_k$)

       \emph{Estimated Latent Variables:}
        $\Z$

    \emph{Estimated Noise Free Data:}
        $\La \ \Z$

    \emph{Estimated Biclusters:}
        $\la_i \ \z_i^T $
     Larges values give the bicluster (ideal the nonzero values).



       The model selection is performed by a variational approach according
  to \citep{Girolami:01} and \citep{Palmer:06}.

  We included a prior on the parameters and minimize
  a lower bound on the posterior
  of the parameters given the data.
  The update of the loadings includes an additive term which pushes the
  loadings toward zero (Gaussian prior leads to an multiplicative factor).

  \emph{Post-processing:}
  The final results of the loadings and the factors are projected to
  a sparse vector according to Hoyer, 2004: given an
  $l_1$-norm and an $l_2$-norm minimize the Euclidean distance
  to the original vector (currently the $l_2$-norm is fixed to 1).
  The projection is a convex quadratic problem which is solved
  iteratively where at each iteration at least one component is set to
  zero. Instead of the  $l_1$-norm a sparseness measurement is used
  which relates the $l_1$-norm to the $l_2$-norm:


  The code is implemented in C using the Rcpp package. The projection is
  implemented in \R.




\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}


#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]


resEx <- fabiap(X,3,0.1,50,1.0,1.0,0.6,0.6)


#-----------------
# DEMO1: Toy Data
#-----------------

n = 1000
l= 100
p = 10

dat <- make_fabi_data_blocks(n = n,l= l,p = p,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]
ZC <- dat[[3]]
LC <- dat[[4]]

gclab <- rep.int(0,l)
gllab <- rep.int(0,n)
clab <- as.character(1:l)
llab <- as.character(1:n)
for (i in 1:p){
 for (j in ZC[i]){
     clab[j] <- paste(as.character(i),"_",clab[j],sep="")
 }
 for (j in LC[i]){
     llab[j] <- paste(as.character(i),"_",llab[j],sep="")
 }
 gclab[unlist(ZC[i])] <- gclab[unlist(ZC[i])] + p^i
 gllab[unlist(LC[i])] <- gllab[unlist(LC[i])] + p^i
}


groups <- gclab

#### FABIAP

resToy3 <- fabiap(X,13,0.1,400,1.0,1.0,0.6,0.6)

rToy3 <- extract_plot(resToy3,ti="FABIAP",Y=Y)

raToy3 <- extract_bic(resToy3)

plotBicluster(raToy3,1)
plotBicluster(raToy3,2)
plotBicluster(raToy3,3)
plotBicluster(raToy3,4)

colnames(resToy3@X) <- clab

rownames(resToy3@X) <- llab


plot(resToy3,dim=c(1,2),label.tol=0.1,col.group = groups,lab.size=0.6)
plot(resToy3,dim=c(1,3),label.tol=0.1,col.group = groups,lab.size=0.6)
plot(resToy3,dim=c(2,3),label.tol=0.1,col.group = groups,lab.size=0.6)

#------------------------------------------
# DEMO2: Laura van' Veer's gene expression  
#        data set for breast cancer 
#------------------------------------------


data(Breast_A)

X <- as.matrix(XBreast)


resBreast3 <- fabiap(X,5,0.1,400,1.0,1.0,0.6,0.6)

rBreast3 <- extract_plot(resBreast3,ti="FABIAP Breast cancer(Veer)")

raBreast3 <- extract_bic(resBreast3)

plotBicluster(raBreast3,1)
plotBicluster(raBreast3,2)
plotBicluster(raBreast3,3)
plotBicluster(raBreast3,4)

plot(resBreast3,dim=c(1,2),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast3,dim=c(1,3),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast3,dim=c(1,4),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast3,dim=c(1,5),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast3,dim=c(2,3),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast3,dim=c(2,4),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast3,dim=c(2,5),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast3,dim=c(3,4),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast3,dim=c(3,5),label.tol=0.03,col.group=CBreast,lab.size=0.6)
plot(resBreast3,dim=c(4,5),label.tol=0.03,col.group=CBreast,lab.size=0.6)

#-----------------------------------
# DEMO3: Su's multiple tisse types
#        gene expression data set 
#-----------------------------------


data(Multi_A)

X <- as.matrix(XMulti)

resMulti3 <- fabiap(X,5,0.1,300,1.0,1.0,0.6,0.6)

rMulti3 <- extract_plot(resMulti3,ti="FABIAP Multiple tissues(Su)")


raMulti3 <- extract_bic(resMulti3)

plotBicluster(raMulti3,1)
plotBicluster(raMulti3,2)
plotBicluster(raMulti3,3)
plotBicluster(raMulti3,4)

plot(resMulti3,dim=c(1,2),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti3,dim=c(1,3),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti3,dim=c(1,4),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti3,dim=c(1,5),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti3,dim=c(2,3),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti3,dim=c(2,4),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti3,dim=c(2,5),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti3,dim=c(3,4),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti3,dim=c(3,5),label.tol=0.01,col.group=CMulti,lab.size=0.6)
plot(resMulti3,dim=c(4,5),label.tol=0.01,col.group=CMulti,lab.size=0.6)

#-----------------------------------------
# DEMO4: Rosenwald's diffuse large-B-cell
#        lymphoma gene expression data set 
#-----------------------------------------

data(DLBCL_B)

X <- as.matrix(XDLBCL)


resDLBCL3 <- fabiap(X,5,0.1,400,1.0,1.0,0.6,0.6)

rDLBCL3 <- extract_plot(resDLBCL3,ti="FABIAP Lymphoma(Rosenwald)")
raDLBCL3 <- extract_bic(resDLBCL3)

plotBicluster(raDLBCL3,1)
plotBicluster(raDLBCL3,2)
plotBicluster(raDLBCL3,3)
plotBicluster(raDLBCL3,4)

plot(resDLBCL3,dim=c(1,2),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL3,dim=c(1,3),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL3,dim=c(1,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL3,dim=c(1,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL3,dim=c(2,3),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL3,dim=c(2,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL3,dim=c(2,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL3,dim=c(3,4),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL3,dim=c(3,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)
plot(resDLBCL3,dim=c(4,5),label.tol=0.03,col.group=CDLBCL,lab.size=0.6)


\end{Sinput}



\subsection{fabias}

Factor Analysis for Bicluster Acquisition: Sparseness Projection (FABIAS) \citep{Hochreiter:10}.

C implementation of  \texttt{fabias}.

\begin{enumerate}

\item  Usage:
\texttt{fabias(X,p=5,alpha=0.6,cyc=500,spz=1.0,random=1.0,center=2,norm=1,lap=1.0)} 


\item Arguments:
 \begin{itemize}
  \item{X:} the data matrix.
  \item{p:} number of hidden factors = number of biclusters; default = 5.
  \item{alpha:} sparseness loadings (0.1 - 1.0); default = 0.1.
  \item{cyc:} number of iterations; default = 500.
  \item{spz:} sparseness factors (0.5 - 4.0); default = 1.0.
  \item{random} <=0: by SVD, >0: random initialization of loadings in
    [-random,random]; default = 1.0.
  \item{center:} data centering: 1 (mean), 2
    (median), > 2 (mode), 0 (no); default = 2.
  \item{norm:} data normalization: 1 (0.75-0.25 quantile), >1 (var=1), 0 (no); default = 1.
  \item{lap:} minimal value of the variational parameter, default = 1.
 \end{itemize}

\item Return Values:
\begin{itemize}
 \item object of the class \texttt{Factorization}. Containing
  \texttt{LZ} (estimated noise free data $\La  \Z$),
  \texttt{L} (loadings  $\La$),
  \texttt{Z} (factors $\Z$),
  \texttt{U} (noise  $\X- \La \Z$),
  \texttt{center} (centering vector),
  \texttt{scaleData} (scaling vector),
  \texttt{X} (centered and scaled data $\X$),
  \texttt{Psi} (noise variance $\pp$),
  \texttt{lapla} (variational parameter),
  \texttt{avini} (the information which the factor
    $z_{ij}$ contains about $\x_j$ averaged over $j$)
  \texttt{xavini} (the information which the factor
    $\tilde\z_j$ contains about $\x_j$ averaged over $j$)
  \texttt{ini} (for each $j$ the information which the factor
    $z_{ij}$ contains about $\x_j$).
\end{itemize}


\end{enumerate}



  Biclusters are found by sparse factor analysis where \emph{both} the factors
  and the loadings are sparse.

  Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ + \ \Up \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z \ + \ \Up \ .
  \end{align*}

  Here $\X \in \Rb^{n \times l}$ and $\Up \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.



  If the nonzero components of
  the sparse vectors are grouped together then the outer product results in
  a matrix with a nonzero block and zeros elsewhere.


  For a single data vector  $\x$ that is
  \begin{align*}\x \ = \ \sum_{i=1}^{p} \la_i z_i  \ + \
    \epp \ = \ \La \tilde\z \ + \
    \epp\end{align*}

  The model assumptions are:

  \emph{Factor Prior is Independent Laplace:}
  \begin{align*}p(\tilde\z) \ = \ \left( \frac{1}{\sqrt{2}} \right)^p \prod_{i=1}^{p}
    e^{-\ \sqrt{2} \ |z_i|}\end{align*}

  \emph{Loading Prior has Finite Support:}
  \begin{align*}p(\la_i) \ = \  c \ \ \ \
    \mathrm{for} \ \ \ \|\la_i\|_1 \ \leq \ k\end{align*}
  \begin{align*}p(\la_i) \ = \  0 \ \ \ \
    \mathrm{for} \ \ \ \|\la_i\|_1 \ > \ k\end{align*}


  \emph{Noise: Gaussian independent}
  \begin{align*}p(\epp) \ = \ \left( \frac{1}{\sqrt{2 \ \pi}} \right)^n
    \prod_{k=1}^{n}  \frac{1}{\sigma_k}
     e^{\sum_{k=1}^{n}  \frac{\epsilon_k^2}{\sigma_k^2}} \end{align*}

  \emph{Data Mean:}

    \begin{align*}\E(\x) \ = \ \E(\La \ \tilde\z \ +
      \ \epp ) \ = \ \La \ \E( \tilde\z) \ +
      \ \E(\epp) \ = \ \0 \end{align*}


      \emph{Data Covariance:}

    \begin{align} \nonumber
    \E(\x \ \x^T) \ &= \
      \La \E( \tilde\z  \tilde\z^T) \La^T \ + \
       \La \E( \tilde\z)\E(\epp^T) \ + \
       \E( \tilde\z)\E(\epp) \La^T \ + \
       \E(\epp \ \epp^T) \\\nonumber &= \
     \La \La^T \ + \ \mathrm{diag}(\sigma_k^2)
\end{align}


      Normalizing the data to variance one for each component gives
       \begin{align*}\sigma_k^2 \ + \ \left(\la^{k}\right)^T \la^{k} \ =
     \ 1\end{align*}
      Here $\la^{k}$ is the $k$-th row of $\La$ (which is a row vector of length $p$).
      We recommend to \emph{normalize the components to variance one} in order
      to make the signal and noise comparable across components.


      \emph{Estimated Parameters:}
       $\La$ and
       $\sigma_k$

       \emph{Estimated Latent Variables:}
        $\Z$

    \emph{Estimated Noise Free Data:}
        $\La \ \Z$

    \emph{Estimated Biclusters:}
        $\la_i \ \z_i^T $
     Larges values give the bicluster (ideal the nonzero values).



  The model selection is performed by a variational approach according
  to \citep{Girolami:01} and \citep{Palmer:06}.

  The prior has finite support, therefore after each
  update of the loadings they are projected to the finite support.
  The projection is done according to \citep{Hoyer:04}: given an
  $l_1$-norm and an $l_2$-norm minimize the Euclidean distance
  to the original vector (currently the $l_2$-norm is fixed to 1).
  The projection is a convex quadratic problem which is solved
  iteratively where at each iteration at least one component is set to
  zero. Instead of the  $l_1$-norm a sparseness measurement is used
  which relates the $l_1$-norm to the $l_2$-norm:

\begin{align*}\mathrm{sparseness}(\la_i) \ = \ \frac{\sqrt{n} \ - \
    \sum_{k=1}^{n} \left|\lambda_{ki} \right|\ / \  \sum_{k=1}^{n}
    \lambda_{ki}^2}{\sqrt{n} \ - \ 1}\end{align*}



  The code is implemented in C using the Rcpp package.





\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]




resEx <- fabias(X,3,0.6,50,1.0)

#-----------------
# DEMO1: Toy Data
#-----------------

n = 1000
l= 100
p = 10

dat <- make_fabi_data_blocks(n = n,l= l,p = p,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]
ZC <- dat[[3]]
LC <- dat[[4]]

gclab <- rep.int(0,l)
gllab <- rep.int(0,n)
clab <- as.character(1:l)
llab <- as.character(1:n)
for (i in 1:p){
 for (j in ZC[i]){
     clab[j] <- paste(as.character(i),"_",clab[j],sep="")
 }
 for (j in LC[i]){
     llab[j] <- paste(as.character(i),"_",llab[j],sep="")
 }
 gclab[unlist(ZC[i])] <- gclab[unlist(ZC[i])] + p^i
 gllab[unlist(LC[i])] <- gllab[unlist(LC[i])] + p^i
}


groups <- gclab

#### FABIAS

resToy2 <- fabias(X,13,0.6,400,1.0)

rToy2 <- extract_plot(resToy2,ti="FABIAS",Y=Y)


raToy2 <- extract_bic(resToy2)

plotBicluster(raToy2,1)
plotBicluster(raToy2,2)
plotBicluster(raToy2,3)
plotBicluster(raToy2,4)

colnames(resToy2@X) <- clab

rownames(resToy2@X) <- llab


plot(resToy2,dim=c(1,2),label.tol=0.1,col.group = groups,lab.size=0.6)
plot(resToy2,dim=c(1,3),label.tol=0.1,col.group = groups,lab.size=0.6)
plot(resToy2,dim=c(2,3),label.tol=0.1,col.group = groups,lab.size=0.6)

#------------------------------------------
# DEMO2: Laura van' Veer's gene expression  
#        data set for breast cancer 
#------------------------------------------

data(Breast_A)

X <- as.matrix(XBreast)

resBreast2 <- fabias(X,5,0.6,300,1.0)

rBreast2 <- extract_plot(resBreast2,ti="FABIAS Breast cancer(Veer)")


raBreast2 <- extract_bic(resBreast2)

plotBicluster(raBreast2,1)
plotBicluster(raBreast2,2)
plotBicluster(raBreast2,3)
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)

#-----------------------------------
# DEMO3: Su's multiple tisse types
#        gene expression data set 
#-----------------------------------

data(Multi_A)

X <- as.matrix(XMulti)

resMulti2 <- fabias(X,5,0.6,300,1.0)

rMulti2 <- extract_plot(resMulti2,ti="FABIAS Multiple tissues(Su)")

raMulti2 <- extract_bic(resMulti2)

plotBicluster(raMulti2,1)
plotBicluster(raMulti2,2)
plotBicluster(raMulti2,3)
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)


#-----------------------------------------
# DEMO4: Rosenwald's diffuse large-B-cell
#        lymphoma gene expression data set 
#-----------------------------------------


data(DLBCL_B)

X <- as.matrix(XDLBCL)

resDLBCL2 <- fabias(X,5,0.6,300,1.0)

rDLBCL2 <- extract_plot(resDLBCL2,ti="FABIAS Lymphoma(Rosenwald)")

raDLBCL2 <- extract_bic(resDLBCL2)

plotBicluster(raDLBCL2,1)
plotBicluster(raDLBCL2,2)
plotBicluster(raDLBCL2,3)
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)



\end{Sinput}





\subsection{fabiasp}

Factor Analysis for Bicluster Acquisition: Sparseness Projection (FABIASP) \citep{Hochreiter:10}.

\R implementation of  \texttt{fabias}, therefore it is \emph{slow}.

\begin{enumerate}

\item  Usage:
\texttt{fabiasp(X,p=5,alpha=0.6,cyc=500,spz=1.0,center=2,norm=1)}

\item Arguments:
  \begin{itemize}
  \item{X:} the data matrix.
  \item{p:} number of hidden factors = number of biclusters; default = 5.
  \item{alpha:} sparseness loadings (0.1 - 1.0); default = 0.1.
  \item{cyc:} number of iterations; default = 500.
  \item{spz:} sparseness factors (0.5 - 4.0); default = 1.0.
  \item{center:} data centering: 1 (mean), 2
    (median), > 2 (mode), 0 (no); default = 2.
  \item{norm:} data normalization: 1 (0.75-0.25 quantile), >1 (var=1),
    0 (no); default = 1.
 \end{itemize}

\item Return Values:
\begin{itemize}
 \item object of the class \texttt{Factorization}. Containing
  \texttt{LZ} (estimated noise free data $\La  \Z$),
  \texttt{L} (loadings  $\La$),
  \texttt{Z} (factors $\Z$),
  \texttt{U} (noise  $\X- \La \Z$),
  \texttt{center} (centering vector),
  \texttt{scaleData} (scaling vector),
  \texttt{X} (centered and scaled data $\X$),
  \texttt{Psi} (noise variance $\pp$),
  \texttt{lapla} (variational parameter),
  \texttt{avini} (the information which the factor
    $z_{ij}$ contains about $\x_j$ averaged over $j$)
  \texttt{xavini} (the information which the factor
    $\tilde\z_j$ contains about $\x_j$ averaged over $j$)
  \texttt{ini} (for each $j$ the information which the factor
    $z_{ij}$ contains about $\x_j$).
\end{itemize}

\end{enumerate}


  Biclusters are found by sparse factor analysis where \emph{both} the factors
  and the loadings are sparse.


  Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ + \ \Up \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z \ + \ \Up \ .
  \end{align*}

  Here $\X \in \Rb^{n \times l}$ and $\Up \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.


  If the nonzero components of
  the sparse vectors are grouped together then the outer product results in
  a matrix with a nonzero block and zeros elsewhere.


  For a single data vector  $\x$ that is
  \begin{align*}\x \ = \ \sum_{i=1}^{p} \la_i z_i  \ + \
    \epp \ = \ \La \tilde\z \ + \
    \epp\end{align*}

  The model assumptions are:

  \emph{Factor Prior is Independent Laplace:}
  \begin{align*}p(\z) \ = \ \left( \frac{1}{\sqrt{2}} \right)^p \prod_{i=1}^{p}
    e^{-\ \sqrt{2} \ |z_i|}\end{align*}

  \emph{Loading Prior has Finite Support:}
  \begin{align*}p(\la_i) \ = \  c \ \ \ \
    \mathrm{for} \ \ \ \|\la_i\|_1 \ \leq \ k\end{align*}
  \begin{align*}p(\la_i) \ = \  0 \ \ \ \
    \mathrm{for} \ \ \ \|\la_i\|_1 \ > \ k\end{align*}


  \emph{Noise: Gaussian independent}
  \begin{align*}p(\epp) \ = \ \left( \frac{1}{\sqrt{2 \ \pi}} \right)^n
    \prod_{k=1}^{n}  \frac{1}{\sigma_k}
     e^{\sum_{k=1}^{n}  \frac{\epsilon_k^2}{\sigma_k^2}} \end{align*}

  \emph{Data Mean:}

    \begin{align*}\E(\x) \ = \ \E(\La \ \tilde\z \ +
      \ \epp ) \ = \ \La \ \E( \tilde\z) \ +
      \ \E(\epp) \ = \ \0 \end{align*}


      \emph{Data Covariance:}

      \begin{align} \nonumber
    \E(\x \ \x^T) \ &= \
      \La \E(  \tilde\z   \tilde\z^T) \La^T \ + \
       \La \E(  \tilde\z)\E(\epp^T) \ + \
       \E(  \tilde\z)\E(\epp) \La^T \ + \
       \E(\epp \ \epp^T) \\\nonumber &= \
   \La \La^T \ + \ \mathrm{diag}(\sigma_k^2)
\end{align}

      Normalizing the data to variance one for each component gives
       \begin{align*}\sigma_k^2 \ + \ \left(\la^{k}\right)^T \la^{k} \ =
     \ 1\end{align*}
      Here $\la^{k}$ is the $k$-th row of $\La$ (which is a row vector of length $p$).
      We recommend to \emph{normalize the components to variance one} in order
      to make the signal and noise comparable across components.


      \emph{Estimated Parameters:}
       $\La$ and $\pp$ ($\sigma_k$)

       \emph{Estimated Latent Variables:}
        $\Z$

    \emph{Estimated Noise Free Data:}
        $\La \ \Z$

    \emph{Estimated Biclusters:}
        $\la_i \ \z_i^T $
     Larges values give the bicluster (ideal the nonzero values).




  The model selection is performed by a variational approach according
  to \citep{Girolami:01} and \citep{Palmer:06}.

  The prior has finite support, therefore after each
  update of the loadings they are projected to the finite support.
  The projection is done according to \citep{Hoyer:04}: given an
  $l_1$-norm and an $l_2$-norm minimize the Euclidean distance
  to the original vector (currently the $l_2$-norm is fixed to 1).
  The projection is a convex quadratic problem which is solved
  iteratively where at each iteration at least one component is set to
  zero. Instead of the  $l_1$-norm a sparseness measurement is used
  which relates the $l_1$-norm to the $l_2$-norm:

\begin{align*}\mathrm{sparseness}(\la_i) \ = \ \frac{\sqrt{n} \ - \
    \sum_{k=1}^{n} \left|\lambda_{ki} \right|\ / \  \sum_{k=1}^{n}
    \lambda_{ki}^2}{\sqrt{n} \ - \ 1}\end{align*}


   The code is implemented in \R, therefore it is \emph{slow}.





\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]




resEx <- fabiasp(X,3,0.6,50,1.0)


\dontrun{
#---------------
# DEMO1
#---------------

dat <- make_fabi_data_blocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]


resToy <- fabiasp(X,13,0.6,200,1.0)

rToy <- extract_plot(resToy,ti="FABIASP",Y=Y)

#---------------
# DEMO2
#---------------


data(Breast_A)

X <- as.matrix(XBreast)

resBreast <- fabiasp(X,5,0.6,200,1.0)

rBreast <- extract_plot(resBreast,ti="FABIASP Breast cancer(Veer)")

#sorting of predefined labels
CBreast%*%rBreast$pmZ

#---------------
# DEMO3
#---------------


data(Multi_A)

X <- as.matrix(XMulti)

resMulti <- fabiasp(X,5,0.6,200,1.0)

rMulti <- extract_plot(resMulti,"ti=FABIASP Multiple tissues(Su)")

#sorting of predefined labels
CMulti%*%rMulti$pmZ

#---------------
# DEMO4
#---------------


data(DLBCL_B)

X <- as.matrix(XDLBCL)


resDLBCL <- fabiasp(X,5,0.6,200,1.0)

rDLBCL <- extract_plot(resDLBCL,ti="FABIASP Lymphoma(Rosenwald)")

#sorting of predefined labels
CDLBCL%*%rDLBCL$pmZ



\end{Sinput}


\subsection{mfsc}

Sparse Matrix Factorization for bicluster analysis (MFSC) \citep{Hochreiter:10}.

\begin{enumerate}
\item Usage:
\texttt{mfsc(X,p=5,cyc=100,sL=0.6,sZ=0.6,center=2,norm=1)}

\item Arguments:
 \begin{itemize}
  \item{X:} the data matrix.
  \item{p:} number of hidden factors = number of biclusters; default = 5.
  \item{cyc:} maximal number of iterations; default = 100.
  \item{sL:} final sparseness loadings; default = 0.6.
  \item{sZ:} final sparseness factors; default = 0.6.
  \item{center:} data centering: 1 (mean), 2
    (median), > 2 (mode), 0 (no); default = 2.
  \item{norm:} data normalization: 1 (0.75-0.25 quantile), >1 (var=1),
    0 (no); default = 1.
 \end{itemize}

\item Return Values:
\begin{itemize}
 \item object of the class \texttt{Factorization}. Containing
  \texttt{LZ} (estimated noise free data $\La  \Z$),
  \texttt{L} (loadings  $\La$),
  \texttt{Z} (factors $\Z$),
  \texttt{U} (noise  $\X - \La \Z$),
  \texttt{center} (centering vector),
  \texttt{scaleData} (scaling vector),
  \texttt{X} (centered and scaled data $\X$).
\end{itemize}


\end{enumerate}

Biclusters are found by sparse matrix factorization
  where \emph{both} factors are sparse.

  Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T  \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z  \ .
  \end{align*}

 Here $\X \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.

\emph{No noise assumption:}
  In contrast to factor analysis there is no noise assumption.

  If the nonzero components of
  the sparse vectors are grouped together then the outer product results in
  a matrix with a nonzero block and zeros elsewhere.


  For a single data vector  $\x$ that is
  \begin{align*}
    \x \ = \ \sum_{i=1}^{p} \la_i z_i  \ = \ \La  \tilde\z
  \end{align*}


      \emph{Estimated Parameters:}
       $\La$ and $\Z$

    \emph{Estimated Biclusters:}
        $\la_i \ \z_i^T $
     Larges values give the bicluster (ideal the nonzero values).



     The model selection is performed by a constraint optimization
     according
  to  \citep{Hoyer:04}. The Euclidean distance (the Frobenius norm) is
  minimized subject to sparseness constraints:
  \begin{align*}\min_{ \La, \Z}
    \left\|\X \ - \ \La \ \Z\right\|_{F}^{2} \end{align*}
  \begin{align*}\mathrm{subject} \ \mathrm{to} \ \ \left\|\La\right\|_{F}^{2} \ =
  \ 1\end{align*}
  \begin{align*}\mathrm{subject} \ \mathrm{to} \ \ \left\|\La\right\|_{1} \ =
  \ k_L\end{align*}
  \begin{align*}\mathrm{subject} \ \mathrm{to} \ \ \left\|\Z\right\|_{F}^{2} \ =
  \ 1\end{align*}
  \begin{align*}\mathrm{subject} \ \mathrm{to} \ \ \left\|\Z\right\|_{1} \ =
  \ k_Z\end{align*}


  Model selection is done by gradient descent on the Euclidean
  objective and thereafter projection of single vectors of
  $\La$ and single vectors of $\Z$
  to fulfill the sparseness constraints.

  The projection minimize the Euclidean distance
  to the original vector  given an
  $l_1$-norm and an $l_2$-norm.

  The projection is a convex quadratic problem which is solved
  iteratively where at each iteration at least one component is set to
  zero. Instead of the  $l_1$-norm a sparseness measurement is used
  which relates the $l_1$-norm to the $l_2$-norm:

  \begin{align*}\mathrm{sparseness}(\la_i) \ = \ \frac{\sqrt{n} \ - \
    \sum_{k=1}^{n} \left|\lambda_{ki} \right|\ / \  \sum_{k=1}^{n}
    \lambda_{ki}^2}{\sqrt{n} \ - \ 1}\end{align*}

  The code is implemented in \R.




\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]


resEx <- mfsc(X,3,30,0.6,0.6)


#-----------------
# DEMO1: Toy Data
#-----------------

n = 1000
l= 100
p = 10

dat <- make_fabi_data_blocks(n = n,l= l,p = p,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]
ZC <- dat[[3]]
LC <- dat[[4]]

gclab <- rep.int(0,l)
gllab <- rep.int(0,n)
clab <- as.character(1:l)
llab <- as.character(1:n)
for (i in 1:p){
 for (j in ZC[i]){
     clab[j] <- paste(as.character(i),"_",clab[j],sep="")
 }
 for (j in LC[i]){
     llab[j] <- paste(as.character(i),"_",llab[j],sep="")
 }
 gclab[unlist(ZC[i])] <- gclab[unlist(ZC[i])] + p^i
 gllab[unlist(LC[i])] <- gllab[unlist(LC[i])] + p^i
}


groups <- gclab

#### MFSC

resToy4 <- mfsc(X,13,100,0.6,0.6)

rToy4 <- extract_plot(resToy4,ti="MFSC",Y=Y)

raToy4 <- extract_bic(resToy4,thresZ=0.01,thresL=0.05)

plotBicluster(raToy4,1)
plotBicluster(raToy4,2)
plotBicluster(raToy4,3)
plotBicluster(raToy4,4)

colnames(resToy4@X) <- clab

rownames(resToy4@X) <- llab


plot(resToy4,dim=c(1,2),label.tol=0.1,col.group = groups,lab.size=0.6)
plot(resToy4,dim=c(1,3),label.tol=0.1,col.group = groups,lab.size=0.6)
plot(resToy4,dim=c(2,3),label.tol=0.1,col.group = groups,lab.size=0.6)


#------------------------------------------
# DEMO2: Laura van' Veer's gene expression  
#        data set for breast cancer 
#------------------------------------------

data(Breast_A)

X <- as.matrix(XBreast)

resBreast4 <- mfsc(X,5,100,0.6,0.6)

rBreast4 <- extract_plot(resBreast4,ti="MFSC Breast cancer(Veer)")


raBreast4 <- extract_bic(resBreast4,thresZ=0.01,thresL=0.05)

plotBicluster(raBreast4,1)
plotBicluster(raBreast4,2)
plotBicluster(raBreast4,3)
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)

#-----------------------------------
# DEMO3: Su's multiple tisse types
#        gene expression data set 
#-----------------------------------


data(Multi_A)

X <- as.matrix(XMulti)

resMulti4 <- mfsc(X,5,100,0.6,0.6)

rMulti4 <- extract_plot(resMulti4,ti="MFSC Multiple tissues(Su)")


raMulti4 <- extract_bic(resMulti4,thresZ=0.01,thresL=0.05)

plotBicluster(raMulti4,1)
plotBicluster(raMulti4,2)
plotBicluster(raMulti4,3)
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)

#-----------------------------------------
# DEMO4: Rosenwald's diffuse large-B-cell
#        lymphoma gene expression data set 
#-----------------------------------------

data(DLBCL_B)

X <- as.matrix(XDLBCL)


resDLBCL4 <- mfsc(X,5,100,0.6,0.6)

rDLBCL4 <- extract_plot(resDLBCL4,ti="MFSC Lymphoma(Rosenwald)")

raDLBCL4 <- extract_bic(resDLBCL4,thresZ=0.01,thresL=0.05)

plotBicluster(raDLBCL4,1)
plotBicluster(raDLBCL4,2)
plotBicluster(raDLBCL4,3)
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}



\subsection{nmfdiv}

Non-negative Matrix Factorization with Kullaback-Leibler divergence
as objective.

\begin{enumerate}

\item Usage:
\texttt{nmfdiv(X,p=5,cyc=100)}


\item Arguments:
 \begin{itemize}
  \item{X:} the data matrix.
  \item{p:} number of hidden factors = number of biclusters; default = 5.
  \item{cyc:} maximal number of iterations; default = 100.
 \end{itemize}


\item Return Values:
\begin{itemize}
 \item object of the class \texttt{Factorization}. Containing
  \texttt{LZ} (estimated noise free data $\La  \Z$),
  \texttt{L} (loadings  $\La$),
  \texttt{Z} (factors $\Z$),
  \texttt{U} (noise  $\X- \La \Z$),
  \texttt{X} (scaled data $\X$).
\end{itemize}

\end{enumerate}

  \begin{align*}\X \ = \ \La \ \Z \end{align*}

  \begin{align*}\X \ = \ \sum_{i=1}^{p} \la_i \
                  \z_i^T\end{align*}




      \emph{Estimated Parameters:}
       $\La$ and $\Z$



     The model selection is performed
     according  to  \citep{Lee:99,Lee:01}.

\emph{objective:}
\begin{align*}\mathrm{D} ( \A \parallel \B ) \ = \
  \sum_{ij} \left( A_{ij} \ \log \frac{ A_{ij}}{B_{ij}} \ + \  A_{ij} \
  - \  B_{ij}  \right)\end{align*}


\emph{update:}
\begin{align*}L_{ik} \ = \ L_{ik} \frac{\sum_{j=1}^{n} Z_{ji} \  V_{jk} \ / \
    \left(\La \ \Z\right)_{jk}}{\sum_{j=1}^{n} Z_{ji}}\end{align*}

\begin{align*}Z_{ji} \ = \ Z_{ji} \frac{\sum_{k=1}^{l} L_{ik} \  V_{jk} \ / \
    \left(\La \ \Z\right)_{jk}}{\sum_{k=1}^{l} L_{ik}}\end{align*}

or in matrix notation with ``$*$'' and  ``$/$'' as
    element-wise operators:
    \begin{align*}\La \ = \  \La*((\X \ / \ (\La \ \Z))\ t(\Z)) \
    / \ \mathrm{rowSums}(\Z)\end{align*}
    \begin{align*}\Z \ = \  \Z*(t(\La) \ (\X \ / \ (\La \
    \Z))) \ / \ \mathrm{colSums}(\La)\end{align*}



  The code is implemented in \R.




\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]
X <- abs(X)


resEx <- nmfdiv(X,3)



#---------------
# DEMO
#---------------

dat <- make_fabi_data_blocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]
X <- abs(X)


resToy <- nmfdiv(X,13)

rToy <- extract_plot(resToy,ti="NMFDIV",Y=Y)


\end{Sinput}





\subsection{nmfeu}

Non-negative Matrix Factorization with Euclidean distance as
objective.

\begin{enumerate}
\item Usage:
\texttt{nmfeu(X,p=5,cyc=100)}

\item Arguments:
 \begin{itemize}
  \item{X:} the data matrix.
  \item{p:} number of hidden factors = number of biclusters; default = 5.
  \item{cyc:} maximal number of iterations; default = 100.
 \end{itemize}

\item Return Values:
\begin{itemize}
 \item object of the class \texttt{Factorization}. Containing
  \texttt{LZ} (estimated noise free data $\La  \Z$),
  \texttt{L} (loadings  $\La$),
  \texttt{Z} (factors $\Z$),
  \texttt{U} (noise  $\X - \La \Z$),
  \texttt{X} (scaled data $\X$).
\end{itemize}

\end{enumerate}

  \begin{align*}\X \ = \ \La \ \Z \end{align*}

  \begin{align*}\X \ = \ \sum_{i=1}^{p} \la_i \
                   \z_i^T\end{align*}




      \emph{Estimated Parameters:}
       $\La$ and $\Z$





     The model selection is performed
     according  to  \citep{Lee:01,Paatero:97}.

\emph{objective:}
\begin{align*}\| \A \ - \ \B \|_{F}^2 \ = \
  \sum_{ij} \left( A_{ij} \ - \  B_{ij} \right)^2\end{align*}


\emph{update:}
\begin{align*}L_{ik} \ = \ L_{ik} \frac{\left( \La^T  \ \X \right)_{ik}}{
  \left(  \La^T  \ \La \ \Z\right)_{ik}} \end{align*}

\begin{align*}Z_{ji} \ = \ Z_{ji} \frac{\left( \X  \ \Z^T \right)_{ji}}{
  \left(  \La \ \Z \ \Z^T \right)_{ji}} \end{align*}

or in matrix notation with ``$*$'' and  ``$/$'' as
    element-wise operators:

    \begin{align*}\Z \ = \ \Z*(t(\La) \ \X)\ / \ (t(\La) \
    \La \ \Z)\end{align*}
    \begin{align*}\La \ = \ \La*(\X \ t(\Z)) \ / \ (\La \
    \Z \ t(\Z))\end{align*}


  The code is implemented in \R.




\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]
X <- abs(X)


resEx <- nmfeu(X,3)


#---------------
# DEMO
#---------------

dat <- make_fabi_data_blocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]
X <- abs(X)


resToy <- nmfeu(X,13)

rToy <- extract_plot(resToy,ti="NMFEU",Y=Y)



\end{Sinput}



\subsection{nmfsc}

Non-negative Sparse Matrix Factorization with sparseness constraints.


\begin{enumerate}
\item Usage:
\texttt{nmfsc(X,p=5,cyc=100,sL=0.6,sZ=0.6)}


\item Arguments:
 \begin{itemize}
  \item{X:} the data matrix.
  \item{p:} number of hidden factors = number of biclusters; default = 5.
  \item{cyc:} maximal number of iterations; default = 100.
  \item{sL:} sparseness loadings; default = 0.6.
  \item{sZ:} sparseness factors; default = 0.6.
 \end{itemize}

\item Return Values:
\begin{itemize}
 \item object of the class \texttt{Factorization}. Containing
  \texttt{LZ} (estimated noise free data $\La  \Z$),
  \texttt{L} (loadings  $\La$),
  \texttt{Z} (factors $\Z$),
  \texttt{U} (noise  $\X - \La \Z$),
  \texttt{X} (data $\X$).
\end{itemize}

\end{enumerate}

   Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z \ .
  \end{align*}

  Here $\X \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.

 If the nonzero components of
  the sparse vectors are grouped together then the outer product results in
  a matrix with a nonzero block and zeros elsewhere.


  For a single data vector  $\x$ that is
  \begin{align*}\x \ = \ \sum_{i=1}^{p} \la_i z_i  \ = \ \La \z \end{align*}


      \emph{Estimated Parameters:}
       $\La$ and $\Z$

    \emph{Estimated Biclusters:}
        $\la_i \ \z_i^T $
     Larges values give the bicluster (ideal the nonzero values).




     The model selection is performed by a constraint optimization
     according
  to  \citep{Hoyer:04}. The Euclidean distance (the Frobenius norm) is
  minimized  subject to sparseness and non-negativity constraints:
  \begin{align*}\min_{ \La, \Z}
    \left\|\x \ - \ \La \ \Z\right\|_{F}^{2} \end{align*}
  \begin{align*}\mathrm{subject} \ \mathrm{to} \ \ \left\|\La\right\|_{F}^{2} \ =
  \ 1\end{align*}
  \begin{align*}\mathrm{subject} \ \mathrm{to} \ \ \left\|\La\right\|_{1} \ =
  \ k_L\end{align*}
  \begin{align*}\mathrm{subject} \ \mathrm{to} \ \ \La \ \geq \ \0  \end{align*}
  \begin{align*}\mathrm{subject} \ \mathrm{to} \ \ \left\|\Z\right\|_{F}^{2} \ =
  \ 1\end{align*}
  \begin{align*}\mathrm{subject} \ \mathrm{to} \ \ \left\|\Z\right\|_{1} \ =
  \ k_Z\end{align*}
  \begin{align*}\mathrm{subject} \ \mathrm{to} \ \ \Z \ \geq \ \0  \end{align*}


  Model selection is done by gradient descent on the Euclidean
  objective and thereafter projection of single vectors of
  $\La$ and single vectors of $\Z$
  to fulfill the sparseness and non-negativity constraints.

  The projection minimize the Euclidean distance
  to the original vector  given an
  $l_1$-norm and an $l_2$-norm and enforcing non-negativity.

  The projection is a convex quadratic problem which is solved
  iteratively where at each iteration at least one component is set to
  zero. Instead of the  $l_1$-norm a sparseness measurement is used
  which relates the $l_1$-norm to the $l_2$-norm:

  \begin{align*}\mathrm{sparseness}(\la_i) \ = \ \frac{\sqrt{n} \ - \
    \sum_{k=1}^{n} \left|\lambda_{ki} \right|\ / \  \sum_{k=1}^{n}
    \lambda_{ki}^2}{\sqrt{n} \ - \ 1} \end{align*}

  The code is implemented in \R.





\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]
X <- abs(X)

resEx <- nmfsc(X,3,30,0.6,0.6)


#---------------
# DEMO
#---------------

dat <- make_fabi_data_blocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]
X <- abs(X)


resToy <- nmfsc(X,13,100,0.6,0.6)

rToy <- extract_plot(resToy,ti="NMFSC",Y=Y)

\end{Sinput}



\section{Analyzing the Results of Biclustering / Matrix Factorization}


\subsection{plot}


Plot of a Matrix Factorization.

Produces a (biplot) of a Matrix Factorization result.


\begin{enumerate}

\item Usage:
\texttt{plot(x,y, ...)
  }

\item Arguments:
 \begin{itemize}
  \item{x:} object of the class \texttt{Factorization}.
  \item{y:} missing, not used.
    \item{...:} with following parameters
    \begin{itemize}

  \item{Rm:} row weighting vector.
  \item{Cm:} column weighting vector.
  \item{dim:} optional principal factors that are plotted along the
    horizontal and vertical axis. Defaults to \texttt{c(1,2)}.
   \item{zoom:} optional zoom factor for row and column items. Defaults to
    \texttt{c(1,1)}.
  \item{col.group:} optional vector (character or numeric) indicating the
    different groupings of the columns. Defaults to 1.
  \item{colors:} vector specifying the colors for the annotation of the plot;
    the first two elements concern the rows; the third till the last element
    concern the columns; the first element will be used to color the unlabeled
    rows; the second element for the labeled rows and the remaining elements to
    give different colors to different groups of columns.
  \item{col.areas:} logical value indicating whether columns should be
    plotted as squares with areas proportional to their marginal mean
    and colors representing the different groups (\texttt{TRUE}), or with
    symbols representing the groupings and identical size
    (\texttt{FALSE}). Defaults to \texttt{TRUE}.
  \item{col.symbols:} vector of symbols when \texttt{col.areas=FALSE}
    corresponds to the \texttt{pch} argument of the function \texttt{plot}.
  \item{sampleNames:} Either a logical vector of length one or a character vector
    of length equal to the number of samples in the dataset. If a logical is provided,
    sample names will be displayed on the plot (\texttt{TRUE}; default) or not (\texttt{FALSE});
    if a character vector is provided, the names provided will be used to label the samples
    instead of the default column names.
  \item{rot:} rotation of plot. Defaults to \texttt{c(-1,-1)}.
  \item{labels:} character vector to be used for labeling points on the graph;
    if \texttt{NULL}, the row names of \texttt{x} are used instead.
  \item{label.tol:} numerical value specifying either the percentile
    (\texttt{label.tol<=1})of rows or the number of rows
    (\texttt{label.tol>1}) most distant from the plot-center \texttt{(0,0)} that
    are labeled and are plotted as circles with area proportional to the
    marginal means of the original data.
  \item{lab.size:} size of identifying labels for row- and column-items
    as \texttt{cex} parameter of the \texttt{text} function.
  \item{col.size:} size in mm of the column symbols.
  \item{row.size:} size in mm of the row symbols.
  \item{do.smoothScatter:} use smoothScatter or not instead of plotting
                          individual points.
  \item{do.plot:} produce a plot or not.
  \item{...:} further arguments to \texttt{eqscaleplotLoc} which draws
    the canvasfor the plot; useful for adding a \texttt{main} or a custom \texttt{sub}.
  \end{itemize}
   and with the default values
   \begin{itemize}
     \item Rm=rep(1,nrow(X)),
    \item Cm=rep(1,ncol(X)),
    \item dim = c(1, 2),
   \item  zoom = rep(1, 2),
    \item col.group = rep(1, ncol(X)),
    \item colors = c("orange1", "red", rainbow(length(unique(col.group)),
                start=2/6, end=4/6)),
    \item col.areas = TRUE,
    \item col.symbols = c(1, rep(2, length(unique(col.group)))),
    \item sampleNames = TRUE,
    \item rot = rep(-1, length(dim)),
    \item labels = NULL,
    \item label.tol = 1,
    \item lab.size = 0.725,
    \item col.size = 10,
    \item row.size = 10,
    \item do.smoothScatter = FALSE,
    \item do.plot = TRUE,
   \end{itemize}

 \end{itemize}

\item Return Values:
 \begin{itemize}
  \item{Rows:} a list with the X and Y coordinates of the rows and
    an indication \texttt{Select} of whether the row was selected
    according to \texttt{label.tol}.
  \item{Columns:} a list with the X and Y coordinates of the columns.

 \end{itemize}

\end{enumerate}

The function \texttt{plot.fabia} is based on
the function \texttt{plot.mpm} in the \R package \texttt{mpm}
(Version: 1.0-16, Date: 2009-08-26, Title: Multivariate Projection
Methods, Maintainer: Tobias Verbeke <tobias.verbeke@openanalytics.be>,
Author: Luc Wouters <wouters\_luc@telenet.be>).


  Biclusters are found by sparse factor analysis where \emph{both} the factors
  and the loadings are sparse.

  Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ + \ \Up \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z \ + \ \Up \ .
  \end{align*}

  Here $\X \in \Rb^{n \times l}$ and $\Up \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.




   For noise free projection like independent component analysis
   we set the noise term to zero: $\Up=\0$.


   The argument \texttt{label.tol} can be used to select the
   most informative rows, i.e.\ rows that are most distant from the
   center of the plot
   (smaller 1: percentage of rows, larger 1: number of rows).

   Only these row-items are then labeled and represented as circles with
   their areas proportional to the row weighting.

   If the column-items are grouped these groups can be visualized by
   colors given  by \texttt{col.group}.

  Implementation in \R.

\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}



n=200
l=100
p=4

dat <- make_fabi_data_blocks(n = n,l= l,p = p,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
ZC <- dat[[3]]
LC <- dat[[4]]


resEx <- fabia(X,p,0.1,400,1.0,1.0)


gclab <- rep.int(0,l)
gllab <- rep.int(0,n)
clab <- as.character(1:l)
llab <- as.character(1:n)
for (i in 1:p){
 for (j in ZC[i]){
     clab[j] <- paste(as.character(i),"_",clab[j],sep="")
 }
 for (j in LC[i]){
     llab[j] <- paste(as.character(i),"_",llab[j],sep="")
 }
 gclab[unlist(ZC[i])] <- gclab[unlist(ZC[i])] + p^i
 gllab[unlist(LC[i])] <- gllab[unlist(LC[i])] + p^i
}


labels <- paste(as.character(clab) ,"_",as.character(1:l),sep="")

groups <- gclab

colnames(resEx@X) <- clab

rlabels <- paste(as.character(llab) ,"_",as.character(1:l),sep="")

rownames(resEx@X) <- llab


plot(resEx,dim=c(1,2),label.tol=0.1,col.group = groups,lab.size=0.6)

plot(resEx,dim=c(1,3),label.tol=0.1,col.group = groups,lab.size=0.6)
plot(resEx,dim=c(2,3),label.tol=0.1,col.group = groups,lab.size=0.6)


\end{Sinput}




\subsection{show}

Plots Statistics of a Matrix Factorization.

  This function plots statistics on a matrix factorization
  which is stored as an instance of
  \texttt{Factorization-class}.

\begin{enumerate}

\item Usage:
\texttt{show(object)}

\item Arguments:
 \begin{itemize}
\item{object:} An instance of \texttt{Factorization-class}.
 \end{itemize}

\item Return Values:
 \begin{itemize}
 \item no value.
 \end{itemize}

\end{enumerate}

  Following is plotted:
  
    1) the information content of biclusters.
    
    2) the information content of samples.
    
    3) the loadings per bicluster.
    
    4) the factors per bicluster.
  


  Implementation in \R.

\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}



#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]


resEx <- fabia(X,3,0.1,100,1.0,1.0)

show(resEx)



\end{Sinput}



\subsection{showSelected}

Plots selected Statistics of a Matrix Factorization.

  This function plots selected statistics on a matrix factorization
  which is stored as an instance of
  \texttt{Factorization-class}.

\begin{enumerate}

\item Usage:
\texttt{showSelected(object, which=c(1,2,3,4))}

\item Arguments:
 \begin{itemize}
\item{object:} An instance of \texttt{Factorization-class}.
   \item{which:} A list of which plots should be generated: 1=the
     information content of biclusters, 2=the information content of
     samples, 3=the loadings per bicluster, 4=the factors per bicluster,
     default c(1,2,3,4).
 \end{itemize}

\item Return Values:
 \begin{itemize}
 \item no value.
 \end{itemize}

\end{enumerate}

   Following is plotted depending on the plot selection variable "which":
  
    1) the information content of biclusters.
    
    2) the information content of samples.
    
    3) the loadings per bicluster.
    
    4) the factors per bicluster.
  


  Implementation in \R.

\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}



#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]


resEx <- fabia(X,3,0.1,100,1.0,1.0)

showSelected(resEx,which=1)
showSelected(resEx,which=2)


\end{Sinput}



\subsection{summary}

Summary of Matrix Factorization.

  This function gives information on a matrix factorization
  which is stored as an instance of \texttt{Factorization-class}.


\begin{enumerate}

\item Usage:
\texttt{summary(object, ...)}

\item Arguments:
 \begin{itemize}
 \item{object:} An instance of \texttt{Factorization-class}.
 \item{...:} further arguments.
 \end{itemize}

\item Return Values:
 \begin{itemize}
\item no value.
 \end{itemize}

\end{enumerate}


  First, it gives the number or rows and columns of the original matrix.

  Then the number of clusters for rows and columns is given.

  Then for the row cluster the information content is given.

  Then for each column its information is given.

  Then for each column cluster a summary is given.

  Then for each row cluster a summary is given.


  Implementation in \R.

\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}


#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]


resEx <- fabia(X,3,0.1,100,1.0,1.0)

summary(resEx)

\end{Sinput}


\subsection{extract\_bic}

Extraction of Biclusters.

\begin{enumerate}

\item Usage:
\texttt{extract\_bic(fact,thresZ=0.5,thresL=NULL)}


\item Arguments:
\begin{itemize}
  \item{fact:} object of class \texttt{Factorization}.
  \item{thresZ:} threshold for sample belonging to bicluster; default 0.5.
  \item{thresL:} threshold for loading belonging to bicluster (if not
    given it is estimated).

\end{itemize}

\item Return Values:
 \begin{itemize}
  \item{bic:} extracted biclusters.
  \item{numn:} indexes for the extracted biclusters.
  \item{bicopp:} extracted opposite biclusters.
  \item{numnopp:} indexes for the extracted opposite biclusters.
  \item{X:} scaled and centered data matrix.
  \item{np:} number of biclusters.
\end{itemize}
\end{enumerate}

  Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ + \ \Up \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z \ + \ \Up \ .
  \end{align*}

  Here $\X \in \Rb^{n \times l}$ and $\Up \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.

   $\Up$ is the Gaussian noise with a diagonal covariance matrix
   which entries are given by \texttt{Psi}.

   The $\Z$ is locally approximated by a Gaussian with inverse
   variance given by  \texttt{lapla}.






   The $\la_i $ and $\z_i $ are used to
   extract the bicluster $i$, where a threshold determines which
   observations and which samples belong the the bicluster.


   In \texttt{bic} the biclusters are extracted according to the
   largest absolute values of the component $i$, i.e.
   the largest values of $\la_i $ and the
   largest values of $\z_i $. The factors  $\z_i $
   are normalized to variance 1.


   The components of \texttt{bic} are
   \texttt{binp}, \texttt{bixv},
   \texttt{bixn}, \texttt{biypv}, and \texttt{biypn}.


   \texttt{binp} give the size of the bicluster: number observations and
   number samples.
   \texttt{bixv} gives the values of the extracted
   observations that have absolute
   values above a threshold. They are sorted.
   \texttt{bixn} gives  the extracted observation names (e.g. gene names).
   \texttt{biypv} gives the values of the extracted samples that have
   absolute values above a threshold.  They are sorted.
   \texttt{biypn} gives  the names of the extracted samples (e.g. sample names).

   In \texttt{bicopp} the opposite cluster to the biclusters are
   give. Opposite means that the negative pattern is present.

   The components of opposite clusters \texttt{bicopp} are
   \texttt{binn}, \texttt{bixv},
   \texttt{bixn}, \texttt{biypnv}, and \texttt{biynn}.

    \texttt{binp} give the size of the opposite bicluster: number observations and
   number samples.
   \texttt{bixv} gives the values of the extracted
   observations that have absolute
   values above a threshold. They are sorted.
   \texttt{bixn} gives  the extracted observation names (e.g. gene names).
   \texttt{biynv} gives the values of the opposite extracted samples that have
   absolute values above a threshold.  They are sorted.
   \texttt{biynn} gives  the names of the opposite
   extracted samples (e.g.\ sample names).

   That means the samples are divided into two groups where one group
   shows large positive values and the other group has negative values
   with large absolute values. That means a observation pattern can be
   switched on or switched off relative to the average value.

   \texttt{numn} gives the indexes of \texttt{bic} with components:
   \texttt{numng} = \texttt{bix} and \texttt{numnp}  =  \texttt{biypn}.

   \texttt{numn} gives the indexes of \texttt{bicopp} with components:
   \texttt{numng} = \texttt{bix} and \texttt{numnn}  =  \texttt{biynn}.


  Implementation in \R.


\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]


resEx <- fabia(X,3,0.1,20,1.0,1.0)

rEx <- extract_bic(resEx)

rEx$bic[1,]
rEx$bic[2,]
rEx$bic[3,]


#---------------
# DEMO1
#---------------

dat <- make_fabi_data_blocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]


resToy <- fabia(X,13,0.1,200,1.0,1.0)

rToy <- extract_bic(resToy)

resToy@avini

rToy$bic[1,]
rToy$bic[2,]
rToy$bic[3,]

#---------------
# DEMO2
#---------------

data(Breast_A)

X <- as.matrix(XBreast)

resBreast <- fabia(X,5,0.1,200,1.0,1.0)

rBreast <- extract_bic(resBreast)

resBreast@avini

rBreast$bic[1,]
rBreast$bic[2,]
rBreast$bic[3,]

\end{Sinput}


\subsection{extract\_plot}

Plotting of Biclustering Results.

\begin{enumerate}

\item Usage:
\texttt{extract\_plot(fact,thresZ=0.5,ti="",thresL=NULL,Y=NULL,which=c(1,2,3,4,5,6,7,8))}


\item Arguments:
\begin{itemize}
  \item{fact:} object of class \texttt{Factorization}.
  \item{thresZ:} threshold for sample belonging to bicluster; default 0.5.
  \item{thresL:} threshold for loading belonging to bicluster (estimated
    if not given).
  \item{ti:} plot title; default "".
  \item{Y:} noise free data matrix.
  \item{which:} which plot is shown: 1=noise free data (if available),
    2=data, 3=reconstructed data, 4=error, 5=absolute factors,
    6=absolute loadings, 7=reconstructed matrix sorted,8=original matrix
    sorted; default c(1,2,3,4,5,6,7,8).
\end{itemize}

\item Return Values:
 \begin{itemize}
  \item{biclust:} clusters of kmeans clustering.
  \item{pmZ:} permutation matrix of $z$ from kmeans clustering.
  \item{pmL:} permutation matrix of $L$ from kmeans clustering.
  \item{nL:} normalized loadings (left matrix).
  \item{nZ} normalized factors (right matrix).
  \item{Xord:} sorted original matrix according to kmeans on $Z$ and
  kmeans on  $L$.
\end{itemize}

\item The method produces following plots depending what plots are chosen
   by the "which" variable:
\begin{itemize}
   \item{$\Y$}: noise free data (if available),
   \item{$\X$}: data,
   \item{$\La \Z$}: reconstructed data,
   \item{$\La \Z - \X$}: error,
   \item{$\mathrm{abs}(\Z)$}: absolute factors,
   \item{$\mathrm{abs}(\L)$}: absolute loadings,
   \item{$\bm{\mathrm{pmL}} \L \Z \bm{\mathrm{pmZ}}$}: reconstructed matrix sorted,
   \item{$\bm{\mathrm{pmL}} \X \bm{\mathrm{pmZ}}$}: original matrix sorted.
\end{itemize}
\end{enumerate}

  Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ + \ \Up \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z \ + \ \Up \ .
  \end{align*}

  Here $\X \in \Rb^{n \times l}$ and $\Up \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.


   The $\la_i $ and $\z_i^T$ are used to
   extract the bicluster $i$, where a threshold determines which
   observations and which samples belong the the bicluster.


   The method produces the plots given above at ``Plots''.



    For sorting first \texttt{kmeans} is performed
   on the $p$ dimensional space and then the vectors
   which belong to the same cluster are put together.

   This sorting is in general not able to
   visualize all biclusters as blocks if they overlap.


   The kmeans clusters are given by \texttt{biclust} with
   components \texttt{biclustx} (the clustered observations)
   and \texttt{biclusty} (the clustered samples).

  Implementation in \R.


\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]



resEx <- fabia(X,3,0.3,20,1.0,1.0)

rEx <- extract_plot(resEx,ti="FABIA",Y=Y)



#---------------
# DEMO1
#---------------

dat <- make_fabi_data_blocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]


resToy <- fabia(X,13,0.1,200,1.0,1.0)

rToy <- extract_plot(resToy,ti="FABIA",Y=Y)

#---------------
# DEMO2
#---------------

data(Breast_A)

X <- as.matrix(XBreast)


resBreast <- fabia(X,5,0.1,200,1.0,1.0)

rBreast <- extract_plot(resBreast,ti="FABIA Breast cancer(Veer)")

#sorting of predefined labels
CBreast%*%rBreast$pmZ

\end{Sinput}


\subsection{plotBicluster}

  Plots a bicluster.

\begin{enumerate}
\item Usage:
 \texttt{plotBicluster(r,p,opp=FALSE,zlim=NULL,title=NULL,which=c(1, 2))}




\item Arguments:
  \begin{itemize}
  \item{r:} the result of extract\_bic.
  \item{p:} the bicluster to plot. 
  \item{opp:} plot opposite bicluster, default = FALSE.
  \item{zlim:} vector containing a low and high value to use for the color scale.
  \item{title:} title of the plot.
  \item{which:} which plots are shown: 1=data matrix with bicluster on upper left, 2=plot of the bicluster; default c(1, 2).

\end{itemize}

\end{enumerate}

    One bicluster is visualized by two plots. The variable
  "which" indicates which plots should be shown.

  Plot1 (which=1):
  The data matrix is sorted such that the bicluster appear at the
  upper left corner.

  The bicluster is marked by a rectangle.

  Plot2 (which=2):
  Only the bicluster is presented.

   Implementation in \R.
\begin{Sinput}

#---------------
# TEST
#---------------

dat <- make_fabi_data_blocks(n = 100,l= 50,p = 3,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]


resEx <- fabia(X,3,0.1,20,1.0,1.0)

rEx <- extract_bic(resEx)

plotBicluster(rEx,p=1)


\dontrun{
#---------------
# DEMO1
#---------------

dat <- make_fabi_data_blocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]


resToy <- fabia(X,13,0.1,200,1.0,1.0)



rToy <- extract_bic(resToy)

plotBicluster(rToy,p=1)

#---------------
# DEMO2
#---------------

data(Breast_A)

X <- as.matrix(XBreast)

resBreast <- fabia(X,5,0.1,200,1.0,1.0)

rBreast <- extract_bic(resBreast)

plotBicluster(rBreast,p=1)


\end{Sinput}



\section{Data Generation Methods}


\subsection{make\_fabi\_data}

Generation of bicluster data.

\begin{enumerate}

\item  Usage: \verb+make_fabi_data(n,l,p,f1,f2,of1,of2,sd_noise,sd_z_noise,mean_z,+\\
\hspace*{6em}\verb+          sd_z,sd_l_noise,mean_l,sd_l)+

\item Arguments:
 \begin{itemize}
  \item{n.} number of observations.
  \item{l:} number of samples.
  \item{p:} number of biclusters.
  \item{f1:} $l/f1$ max. additional samples are active in a bicluster.
  \item{f2:} $n/f2$ max. additional observations that form a pattern in a bicluster.
  \item{of1:} minimal active samples in a bicluster.
  \item{of2:} menial observations that form a pattern in a bicluster.
  \item{sd\_noise:} Gaussian zero mean noise std on data matrix.
  \item{sd\_z\_noise:} Gaussian zero mean noise std for deactivated hidden factors.
  \item{mean\_z:} Gaussian mean for activated factors.
  \item{sd\_z:} Gaussian std for activated factors.
  \item{sd\_l\_noise:} Gaussian zero mean noise std if no observation patterns are present.
  \item{mean\_l:} Gaussian mean for observation patterns.
  \item{sd\_l:} Gaussian std for observation patterns.
 \end{itemize}

\item Return values:
 \begin{itemize}
  \item{X:} the noisy data $\X$ from $\mathbb{R}^{n \times l}$.
  \item{Y:} the noise free data $\Y$ from $\mathbb{R}^{n \times l}$.
  \item{ZC:} list where $i$th element gives samples belonging to $i$th
    bicluster.
  \item{LC:} list where $i$th element gives observations belonging to $i$th
    bicluster.
 \end{itemize}

\end{enumerate}

  Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ + \ \Up \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z \ + \ \Up \ .
  \end{align*}

  Here $\X \in \Rb^{n \times l}$ and $\Up \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.




   Sequentially $\la_i$ are generated using
   \texttt{n}, \texttt{f2}, \texttt{of2}, \texttt{sd\_l\_noise}, \texttt{mean\_l},
   \texttt{sd\_l}.
   \texttt{of2} gives the minimal observations participating in a
   bicluster to which between 0 and $n/f2$ observations are added,
   where the number is uniformly chosen. \texttt{sd\_l\_noise} gives the
   noise of observations not participating in the
   bicluster. \texttt{mean\_l} and \texttt{sd\_l} determines the Gaussian from
   which the values are drawn for the observations that participate in
   the bicluster. The sign of the mean is randomly chosen for each
   component.

   Sequentially $\z_i$ are generated using
   \texttt{l}, \texttt{f1}, \texttt{of1}, \texttt{sd\_z\_noise}, \texttt{mean\_z},
   \texttt{sd\_z}.
   \texttt{of1} gives the minimal samples participating in a
   bicluster to which between 0 and $l/f1$ samples are added,
   where the number is uniformly chosen. \texttt{sd\_z\_noise} gives the
   noise of samples not participating in the
   bicluster. \texttt{mean\_z} and \texttt{sd\_z} determines the Gaussian from
   which the values are drawn for the samples that participate in
   the bicluster.

   $\Up$ is the overall Gaussian zero mean
   noise generated by \texttt{sd\_noise}.



  Implementation in \R.




\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# DEMO
#---------------

dat <- make_fabi_data(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]

myImagePlot(Y)
x11()
myImagePlot(X)

\end{Sinput}


\subsection{make\_fabi\_data\_pos}

Generation of bicluster data.

\begin{enumerate}

\item  Usage: \verb+make_fabi_data_pos(n,l,p,f1,f2,of1,of2,sd_noise,sd_z_noise,+\\
\hspace*{6em}\verb+              mean_z,sd_z,sd_l_noise,mean_l,sd_l)+

\item Arguments:
 \begin{itemize}
  \item{n.} number of observations.
  \item{l:} number of samples.
  \item{p:} number of biclusters.
  \item{f1:} $l/f1$ max. additional samples are active in a bicluster.
  \item{f2:} $n/f2$ max. additional observations that form a pattern in a bicluster.
  \item{of1:} minimal active samples in a bicluster.
  \item{of2:} menial observations that form a pattern in a bicluster.
  \item{sd\_noise:} Gaussian zero mean noise std on data matrix.
  \item{sd\_z\_noise:} Gaussian zero mean noise std for deactivated hidden factors.
  \item{mean\_z:} Gaussian mean for activated factors.
  \item{sd\_z:} Gaussian std for activated factors.
  \item{sd\_l\_noise:} Gaussian zero mean noise std if no observation patterns are present.
  \item{mean\_l:} Gaussian mean for observation patterns.
  \item{sd\_l:} Gaussian std for observation patterns.
 \end{itemize}

\item Return values:
 \begin{itemize}
  \item{X:} the noisy data $\X$ from $\mathbb{R}^{n \times l}$.
  \item{Y:} the noise free data $\Y$ from $\mathbb{R}^{n \times l}$.
  \item{ZC:} list where $i$th element gives samples belonging to $i$th
    bicluster.
  \item{LC:} list where $i$th element gives observations belonging to $i$th
    bicluster.
 \end{itemize}

\end{enumerate}

  Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ + \ \Up \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z \ + \ \Up \ .
  \end{align*}

  Here $\X \in \Rb^{n \times l}$ and $\Up \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.



   Sequentially $\la_i$ are generated using
   \texttt{n}, \texttt{f2}, \texttt{of2}, \texttt{sd\_l\_noise}, \texttt{mean\_l},
   \texttt{sd\_l}.
   \texttt{of2} gives the minimal observations participating in a
   bicluster to which between 0 and $n/f2$ observations are added,
   where the number is uniformly chosen. \texttt{sd\_l\_noise} gives the
   noise of observations not participating in the
   bicluster. \texttt{mean\_l} and \texttt{sd\_l} determines the Gaussian from
   which the values are drawn for the observations that participate in
   the bicluster.  "POS": The sign of the mean is fixed.


   Sequentially $\z_i$ are generated using
   \texttt{l}, \texttt{f1}, \texttt{of1}, \texttt{sd\_z\_noise}, \texttt{mean\_z},
   \texttt{sd\_z}.
   \texttt{of1} gives the minimal samples participating in a
   bicluster to which between 0 and $l/f1$ samples are added,
   where the number is uniformly chosen. \texttt{sd\_z\_noise} gives the
   noise of samples not participating in the
   bicluster. \texttt{mean\_z} and \texttt{sd\_z} determines the Gaussian from
   which the values are drawn for the samples that participate in
   the bicluster.

   $\Up$ is the overall Gaussian zero mean
   noise generated by \texttt{sd\_noise}.



  Implementation in \R.




\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# DEMO
#---------------

dat <- make_fabi_data_pos(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]

myImagePlot(Y)
x11()
myImagePlot(X)

\end{Sinput}


\subsection{make\_fabi\_data\_blocks}

Generation of bicluster data with bicluster blocks.


\begin{enumerate}

 \item Usage: \verb+make_fabi_data_blocks(n,l,p,f1,f2,of1,of2,sd_noise,sd_z_noise,+\\
\hspace*{6em}\verb+                mean_z,sd_z,sd_l_noise,mean_l,sd_l)+


\item Arguments:
 \begin{itemize}
  \item{n:} number of observations.
  \item{l:} number of samples.
  \item{p:} number of biclusters.
  \item{f1:} $l/f1$ max. additional samples are active in a bicluster.
  \item{f2:} $n/f2$ max. additional observations that form a pattern in a bicluster.
  \item{of1:} minimal active samples in a bicluster.
  \item{of2:} minimal observations that form a pattern in a bicluster.
  \item{sd\_noise:} Gaussian zero mean noise std on data matrix.
  \item{sd\_z\_noise:} Gaussian zero mean noise std for deactivated hidden factors.
  \item{mean\_z:} Gaussian mean for activated factors.
  \item{sd\_z:} Gaussian std for activated factors.
  \item{sd\_l\_noise:} Gaussian zero mean noise std if no observation patterns are present.
  \item{mean\_l:} Gaussian mean for observation patterns.
  \item{sd\_l:} Gaussian std for observation patterns.
 \end{itemize}

\item Return Values:
 \begin{itemize}
  \item{X:} the noisy data $\X$ from $\mathbb{R}^{n \times l}$.
  \item{Y:} the noise free data $\Y$ from $\mathbb{R}^{n \times l}$.
  \item{ZC:} list where $i$th element gives samples belonging to $i$th
    bicluster.
  \item{LC:} list where $i$th element gives observations belonging to $i$th
    bicluster.
 \end{itemize}

\end{enumerate}

  Bicluster data is generated for visualization because the biclusters
  are now in block format. That means observations and samples that
  belong to a bicluster are consecutive. This allows visual inspection
  because the use can identify blocks and whether they have been found
  or reconstructed.

  Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ + \ \Up \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z \ + \ \Up \ .
  \end{align*}

  Here $\X \in \Rb^{n \times l}$ and $\Up \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.



   Sequentially $\la_i$ are generated using
   \texttt{n}, \texttt{f2}, \texttt{of2}, \texttt{sd\_l\_noise}, \texttt{mean\_l},
   \texttt{sd\_l}.
   \texttt{of2} gives the minimal observations participating in a
   bicluster to which between 0 and $n/f2$ observations are added,
   where the number is uniformly chosen. \texttt{sd\_l\_noise} gives the
   noise of observations not participating in the
   bicluster. \texttt{mean\_l} and \texttt{sd\_l} determines the Gaussian from
   which the values are drawn for the observations that participate in
   the bicluster. The sign of the mean is randomly chosen for each
   component.

   Sequentially $\z_i$ are generated using
   \texttt{l}, \texttt{f1}, \texttt{of1}, \texttt{sd\_z\_noise}, \texttt{mean\_z},
   \texttt{sd\_z}.
   \texttt{of1} gives the minimal samples participating in a
   bicluster to which between 0 and $l/f1$ samples are added,
   where the number is uniformly chosen. \texttt{sd\_z\_noise} gives the
   noise of samples not participating in the
   bicluster. \texttt{mean\_z} and \texttt{sd\_z} determines the Gaussian from
   which the values are drawn for the samples that participate in
   the bicluster.

   $\Up$ is the overall Gaussian zero mean
   noise generated by \texttt{sd\_noise}.



  Implementation in \R.




\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# DEMO
#---------------

dat <- make_fabi_data_blocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

Y <- dat[[1]]
X <- dat[[2]]

myImagePlot(Y)
x11()
myImagePlot(X)



\end{Sinput}


\subsection{make\_fabi\_data\_blocks\_pos}

Generation of bicluster data with bicluster blocks.


\begin{enumerate}

 \item Usage: \verb+make_fabi_data_blocks_pos(n,l,p,f1,f2,of1,of2,sd_noise,+\\
 \hspace*{6em}\verb+                    sd_z_noise,mean_z,sd_z,sd_l_noise,mean_l,sd_l)+


\item Arguments:
 \begin{itemize}
  \item{n:} number of observations.
  \item{l:} number of samples.
  \item{p:} number of biclusters.
  \item{f1:} $l/f1$ max. additional samples are active in a bicluster.
  \item{f2:} $n/f2$ max. additional observations that form a pattern in a bicluster.
  \item{of1:} minimal active samples in a bicluster.
  \item{of2:} minimal observations that form a pattern in a bicluster.
  \item{sd\_noise:} Gaussian zero mean noise std on data matrix.
  \item{sd\_z\_noise:} Gaussian zero mean noise std for deactivated hidden factors.
  \item{mean\_z:} Gaussian mean for activated factors.
  \item{sd\_z:} Gaussian std for activated factors.
  \item{sd\_l\_noise:} Gaussian zero mean noise std if no observation patterns are present.
  \item{mean\_l:} Gaussian mean for observation patterns.
  \item{sd\_l:} Gaussian std for observation patterns.
 \end{itemize}

\item Return Values:
 \begin{itemize}
  \item{X:} the noisy data $\X$ from $\mathbb{R}^{n \times l}$.
  \item{Y:} the noise free data $\Y$ from $\mathbb{R}^{n \times l}$.
  \item{ZC:} list where $i$th element gives samples belonging to $i$th
    bicluster.
  \item{LC:} list where $i$th element gives observations belonging to $i$th
    bicluster.
 \end{itemize}

\end{enumerate}

  Bicluster data is generated for visualization because the biclusters
  are now in block format. That means observations and samples that
  belong to a bicluster are consecutive. This allows visual inspection
  because the use can identify blocks and whether they have been found
  or reconstructed.

  Essentially the model is the sum of
  outer products of vectors:
  \begin{align*}
    \X \ = \ \sum_{i=1}^{p} \la_i \ \z_i^T \ + \ \Up \ ,
  \end{align*}
  where the number of summands  $p$
  is the number of biclusters.
  The matrix factorization is
  \begin{align*}
    \X \ = \ \La \ \Z \ + \ \Up \ .
  \end{align*}

 Here $\X \in \Rb^{n \times l}$ and $\Up \in \Rb^{n \times l}$,
  $\la_i  \in \Rb^n$, $\z_i  \in \Rb^l$, $\La \in \Rb^{n \times p}$,
  $\Z \in \Rb^{p \times l}$.




   Sequentially $\la_i$ are generated using
   \texttt{n}, \texttt{f2}, \texttt{of2}, \texttt{sd\_l\_noise}, \texttt{mean\_l},
   \texttt{sd\_l}.
   \texttt{of2} gives the minimal observations participating in a
   bicluster to which between 0 and $n/f2$ observations are added,
   where the number is uniformly chosen. \texttt{sd\_l\_noise} gives the
   noise of observations not participating in the
   bicluster. \texttt{mean\_l} and \texttt{sd\_l} determines the Gaussian from
   which the values are drawn for the observations that participate in
   the bicluster. "POS": The sign of the mean is fixed.


   Sequentially $\z_i$ are generated using
   \texttt{l}, \texttt{f1}, \texttt{of1}, \texttt{sd\_z\_noise}, \texttt{mean\_z},
   \texttt{sd\_z}.
   \texttt{of1} gives the minimal samples participating in a
   bicluster to which between 0 and $l/f1$ samples are added,
   where the number is uniformly chosen. \texttt{sd\_z\_noise} gives the
   noise of samples not participating in the
   bicluster. \texttt{mean\_z} and \texttt{sd\_z} determines the Gaussian from
   which the values are drawn for the samples that participate in
   the bicluster.

   $\Up$ is the overall Gaussian zero mean
   noise generated by \texttt{sd\_noise}.



  Implementation in \R.




\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# DEMO
#---------------

dat <- make_fabi_data_blocks_pos(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

Y <- dat[[1]]
X <- dat[[2]]

myImagePlot(Y)
x11()
myImagePlot(X)



\end{Sinput}

\section{Utilities}


\subsection{fabiaVersion}

Display version info for package and for FABIA.

\begin{enumerate}
\item Usage:
\texttt{fabiaVersion()}
\end{enumerate}




\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}
fabiaVersion()
\end{Sinput}





\subsection{myImagePlot}

Plotting of a matrix.

\begin{enumerate}
\item Usage:
 \texttt{myImagePlot(x,xLabels=NULL, yLabels=NULL, zlim=NULL, title=NULL)}



\item Arguments:
  \begin{itemize}
 \item{x:} the matrix.
  \item{xLabels:} vector of strings to label the rows (default "rownames(x)").
  \item{yLabels:} vector of strings to label the columns (default "colnames(x)").
  \item{zlim:} vector containing a low and high value to use for the color scale.
  \item{title:} title of the plot.
 \end{itemize}

\end{enumerate}

  Plotting a table of numbers as an image using \R.

  The color scale is based on the highest and lowest values in the
  matrix.

  Program has been obtained by
 \url{http://www.phaget4.org/R/myImagePlot.R}




\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# DEMO
#---------------

dat <- make_fabi_data_blocks(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5,
  of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0,
  sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

X <- dat[[1]]
Y <- dat[[2]]

myImagePlot(X)


\end{Sinput}


\subsection{nprojfunc}

Projection of a vector to a sparse non-negative vector with given
sparseness and given $l_2$-norm.

\begin{enumerate}

\item Usage:
\texttt{nprojfunc(s, k1, k2)}

\item Arguments:
 \begin{itemize}
  \item{s:} data vector.
  \item{k1:} sparseness, $l_1$ norm constraint.
  \item{k2:} $l_2$ norm constraint.
 \end{itemize}

\item Return Values:
 \begin{itemize}
  \item{v:} non-negative sparse projected vector.
 \end{itemize}

\end{enumerate}

  The projection minimize the Euclidean distance
  to the original vector  given an
  $l_1$-norm and an $l_2$-norm and enforcing non-negativity.

  The projection is a convex quadratic problem which is solved
  iteratively where at each iteration at least one component is set to
  zero.


  In the applications,
  instead of the  $l_1$-norm a sparseness measurement is used
  which relates the $l_1$-norm to the $l_2$-norm:

\begin{align*}\mathrm{sparseness}(\vb) \ = \ \frac{\sqrt{l} \ - \
    \sum_{j=1}^{l} \left|v_{j} \right|\ / \  \sum_{j=1}^{l}
    v_{j}^2}{\sqrt{l} \ - \ 1} \end{align*}

  The code is implemented in \R.



\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# DEMO
#---------------

size <- 30
sparseness <- 0.7

s <- as.vector(rnorm(size))
sp <- sqrt(1.0*size)-(sqrt(1.0*size)-1.0)*sparseness

ss <- nprojfunc(s,k1=sp,k2=1)

s
ss
\end{Sinput}



\subsection{projfunc}

Projection of a vector to a sparse vector with given
sparseness and given $l_2$-norm.

\begin{enumerate}

\item Usage:
\texttt{projfunc(s, k1, k2)}

\item Arguments:
 \begin{itemize}
  \item{s:} data vector.
  \item{k1:} sparseness, $l_1$ norm constraint.
  \item{k2:} $l_2$ norm constraint.
 \end{itemize}

\item Return Values:
 \begin{itemize}
  \item{v:} sparse projected vector.
 \end{itemize}

\end{enumerate}


The projection is done according to \citep{Hoyer:04}: given an
  $l_1$-norm and an $l_2$-norm minimize the Euclidean distance
  to the original vector.
  The projection is a convex quadratic problem which is solved
  iteratively where at each iteration at least one component is set to
  zero.

  In the applications,
  instead of the  $l_1$-norm a sparseness measurement is used
  which relates the $l_1$-norm to the $l_2$-norm:

\begin{align*}\mathrm{sparseness}(\vb) \ = \ \frac{\sqrt{l} \ - \
    \sum_{j=1}^{l} \left|v_{j} \right|\ / \  \sum_{j=1}^{l}
    v_{j}^2}{\sqrt{l} \ - \ 1} \end{align*}

  The code is implemented in \R.





\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# DEMO
#---------------

size <- 30
sparseness <- 0.7

s <- as.vector(rnorm(size))
sp <- sqrt(1.0*size)-(sqrt(1.0*size)-1.0)*sparseness

ss <- projfunc(s,k1=sp,k2=1)

s
ss
\end{Sinput}

\subsection{estimateMode}

Estimation of the modes of the rows of a matrix.

\begin{enumerate}

\item Usage:
\texttt{estimateMode(X,maxiter=50,tol=0.001,alpha=0.1,a1=4.0,G1=FALSE)}

\item Arguments:
 \begin{itemize}
  \item{X:} matrix of which the modes of the rows are estimated.
  \item{maxiter:} maximal number of iterations; default = 50.
  \item{tol:} tolerance for stopping; default = 0.001.
  \item{alpha:} learning rate; default = 0.1.
  \item{a1:} parameter of the width of the given distribution; default = 4.
  \item{G1:} kind of distribution, TRUE: $\frac{1}{a_1} \ln (\cosh (a1
    x))$,
    FALSE: $- \frac{1}{a_1} \exp(- \frac{a_1}{2} x^2)$; default = FALSE.
 \end{itemize}

\item Return Values:
 \begin{itemize}
  \item{u:} the vector of estimated modes.
  \item{xu:} $\X-\u\1^T$ the mode centered data.
 \end{itemize}

\end{enumerate}

  The mode is estimated by contrast functions G1 $\frac{1}{a_1} \ln (\cosh (a1
    x))$ or G2  $- \frac{1}{a_1} \exp(- \frac{a_1}{2} x^2)$.
  The estimation is performed by gradient descent initialized by the
  median.


  Implementation in \R.

\vspace{0.5cm}

\noindent
EXAMPLE:

\begin{Sinput}

#---------------
# DEMO
#---------------

dat <- make_fabi_data_blocks_pos(n = 100,l= 50,p = 10,f1 = 5,f2 = 5,
       of1 = 5,of2 = 10,sd_noise = 2.0,sd_z_noise = 0.2,mean_z = 2.0,
       sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0)

     X <- dat[[1]]

modes <- estimateMode(X)

modes$u - apply(X, 1, median)

\end{Sinput}


\subsection{eqscplotLoc}

Local Version of \texttt{eqscplot} from the package MASS.


  Plots with Geometrically Equal Scales: Version of a scatterplot
  with scales chosen to be equal on both axes, that is 1cm represents
  the same units on each.

\begin{enumerate}

\item Usage:
\texttt{eqscplotLoc(x, y, ratio = 1, tol = 0.04, uin, ...)}

\item Arguments:
 \begin{itemize}

  \item{x:} vector of x values, or a 2-column matrix, or a list with
   components ``x'' and ``y''.

  \item{y:} vector of y values.

  \item{ratio:} desired ratio of units on the axes.  Units on the y axis are
   drawn at ``ratio'' times the size of units on the x axis.
   Ignored if ``uin'' is specified and of length 2.

 \item{tol:} proportion of white space at the margins of plot.

 \item{uin:} desired values for the units-per-inch parameter. If of length
   1, the desired units per inch on the x axis.

 \item{...:} further arguments for ``plot'' and graphical parameters.
          Note that ``par(xaxs="i", yaxs="i")'' is enforced, and
          ``xlim'' and ``ylim'' will be adjusted accordingly.

 \end{itemize}

\item Return Values:
 \begin{itemize}
 \item invisibly, the values of ``uin'' used for the plot.
 \end{itemize}

\end{enumerate}


 Plots with Geometrically Equal Scales
 eqscplot from the package MASS (Version: 7.3-3, 2009-10-15
 Maintainer: Brian Ripley <ripley@stats.ox.ac.uk>).

 To make the package stand alone, this function
 is included as source.

     Limits for the x and y axes are chosen so that they include the
     data.  One of the sets of limits is then stretched from the
     midpoint to make the units in the ratio given by ``ratio''.
     Finally both are stretched by ``1 + tol'' to move points away
     from the axes, and the points plotted.

  Implementation in \R.


\vspace{0.5cm}

\noindent
REFERENCE:
\begin{itemize}
 \item
Venables, W. N. and Ripley, B. D., (2002), ``Modern Applied
     Statistics with S'', Fourth edition,  Springer.
\end{itemize}



\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.



\bibliographystyle{natbib}
\bibliography{fabia}

\end{document}
