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\begin{document}

\title{The \Rpackage{eisa} and \Rpackage{biclust} packages}
\author{G\'abor Cs\'ardi}
\maketitle

\tableofcontents

\RaggedRight

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\section{Introduction}

Biclustering is technique that simultaneously clusters the rows and
columns of a matrix~\cite{madeira04}. In other words, the problem is
finding blocks in the reordered input matrix that exhibit correlated
behavior, both across the rows and columns of the block.

Biclustering is used increasingly in the analysis of gene expression
data sets, because it reduces the complexity of the data: instead of
tens of thousands of individual genes, one can focus on a handful of
biclusters, in which the genes behave similarly.

The Iterative Signature Algorithm (ISA)~\cite{sa,isa,isamod} is a
biclustering method, that can efficiently find potentially overlapping
biclusters (modules, according to the ISA terminology) in a
matrix. The ISA is implemented in the \Rpackage{eisa} package. This
package uses standard BioConductor classes and includes a number of
visualization tools as well.

The \Rpackage{biclust} R package~\cite{biclust} is a general
biclustering package, it contains several biclustering methods, and
these can be invoked with a common interface. It provides a set of
visualization tools for the results. 

In this short document, we show examples on how to use the
visualization tools of \Rpackage{eisa} for the biclusters found with
\Rpackage{biclust}, and vice-versa.

\section{From \Rclass{Biclust} to \Rclass{ISAModules}}

For all examples in this document, we will use the acute lymphoblastic
leukemia data set, that is included in the standard BioConductor
\Rpackage{ALL} package. Let's load this data set and the required
packages first.
<<packages>>=
library(biclust)
library(eisa)
library(ALL)
data(ALL)
@ 

Next, we select a subset of the genes in the data set. We do this to speed 
up the computation for our simple examples. We select the genes that are 
annotated to be involved in immune system processes, according to the Gene
Ontology database.

<<filter>>=
library(GO.db)
library(hgu95av2.db)
gotable <- toTable(GOTERM)
myterms <- unique(gotable$go_id[gotable$Term %in% c("immune system process")])
myprobes <- unique(unlist(mget(myterms, hgu95av2GO2ALLPROBES)))
ALL.filtered <- ALL[myprobes,]
@

We have kept only \Sexpr{nrow(ALL.filtered)} probes:
<<filtered>>=
nrow(ALL.filtered)
@ 

For consistent results, we set the random seed.
<<seed>>=
set.seed(0xf00)
@ 

Next, we apply the Plaid Model Biclustering method~\cite{turner03} to the 
reduced data set.
<<bcplaid>>=
Bc <- biclust(exprs(ALL.filtered), BCPlaid(),
              fit.model = ~m + a + b, verbose = FALSE)
@ 
The method finds \Sexpr{Bc@Number} biclusters, and returns a 
\Rclass{Biclust} object:
<<bcplaid result>>=
class(Bc)
Bc
@

Now we will convert the \Rclass{Biclust} object to an \Rclass{ISAModules} 
object, that is used in the \Rpackage{eisa} package. To help
some \Rpackage{eisa} functions, we add some biological annotation to
the \Rclass{Biclust} object.  of the annotation package to 
the parameters stored in the \Rclass{Biclust} object, this is always advised.
The procedure makes use of the probe and sample names that are kept and stored 
in the \Rclass{Biclust} object, this information will be used later, e.g. for 
the enrichment analysis. The conversion itself can be performed with
the usual \Rfunction{as} function. 
<<convert bc to isa>>=
Bc <- annotate(Bc, ALL.filtered)
modules <- as(Bc, "ISAModules")
modules
@

\subsection{Enrichment analysis}

Now we are able apply the usual \Rclass{ISAModules} methods to 
the biclusters. See more about these functions in the documentation of the 
\Rpackage{eisa} package. 

Performing enrichment analysis is easy:
<<biclust enrichment>>=
library(KEGG.db)
KEGG <- ISAKEGG(modules)
sigCategories(KEGG)[[2]]
unlist(mget(sigCategories(KEGG)[[2]], KEGGPATHID2NAME))
@ 

\subsection{Heatmaps}

The \Rfunction{ISA2heatmap} function creates a heatmap for a module. Let us
annotate the heatmap with the leukemia sample type, white means B-cell, black 
means T-cell leukemia. See Fig.~\ref{fig:heatmap}.
<<heatmap,eval=FALSE>>=
col <- ifelse(grepl("^B", ALL.filtered$BT), "white", "black")
modcol <- col[ getSamples(modules, 2)[[1]] ]
ISA2heatmap(modules, 2, ALL.filtered, 
            ColSideColors=modcol)
@

\begin{figure}
\centering
<<heatmap-real,fig=TRUE,echo=FALSE>>=
<<heatmap>>
@
\caption{Heatmap of the second module, found with the Plaid Model
  biclustering algorithm. The black squares denote the T-cell samples;
  all samples in the module are from T-cell leukemia patients.}
\label{fig:heatmap}
\end{figure}

It turns out, that all samples in the second bicluster belong to
patients with T-cell leukemia.

\subsection{Profile plots}

Profile plots visualize the mean expression levels, both for the
genes/samples in the module and in the background (i.e. the background
means all genes and samples \emph{not} in the module). 
See Fig.~\ref{fig:profile}.
<<profilePlot,eval=FALSE>>=
profilePlot(modules, 2, ALL, plot="both")
@
\begin{figure}
\centering
<<profilePlot-real,fig=TRUE,echo=FALSE>>=
<<profilePlot>>
@ 
\caption{Profile plot for the second module. The red lines show the
  average exression of the samples/genes in the module. The green
  lines show the same for the samples/genes not in the module.}
\label{fig:profile}
\end{figure}

\subsection{Gene Ontology tree plots}

The \Rfunction{gograph} and \Rfunction{gographPlot} functions 
create a plot of the part of the Gene Ontology tree that contains 
the enriched categories. See Fig.~\ref{fig:gotree}.
<<psoptions,results=hide,echo=FALSE>>=
ps.options(fonts=c("serif", "mono"))
@
<<GOtreeplot,eval=FALSE>>=
library(GO.db)
GO <- ISAGO(modules)
gog <- gograph(summary(GO$CC)[[3]])
summary(gog)
gographPlot(gog)
@ 
\begin{figure}
\centering
<<GOtreeplot-real,fig=TRUE,width=10,height=4,echo=FALSE>>=
<<GOtreeplot>>
@
\caption{Part of the Gene Ontology tree, Cellular Components ontology.
  The plot includes all terms with significant enrichment for the
  second module, and their parent terms, up to the most general term.
}
\label{fig:gotree}
\end{figure}

\subsection{HTML summary of the biclusters}

The \Rfunction{ISAHTML} function creates a HTML overview of all modules.
<<html>>=
CHR <- ISACHR(modules)
htmldir <- tempdir()
ISAHTML(eset=ALL.filtered, modules=modules, target.dir=htmldir, 
        GO=GO, KEGG=KEGG, CHR=CHR, condPlot=FALSE)
@
<<html-show,eval=FALSE>>=
if (interactive()) {
  browseURL(URLencode(paste("file://", htmldir, "/index.html", sep="")))
}
@ 

\subsection{Group-mean plots}

The \Rfunction{ISAmnplot} funtion plots group means of expression
levels againts each other, for all genes in the module. Here we plot
the mean expression of the B-cell samples against the T-cell samples, 
for the second module. See Fig.~\ref{fig:mnplot}.
<<mnplot,eval=FALSE>>=
group <- ifelse(grepl("^B", ALL.filtered$BT), "B-cell", "T-cell")
ISAmnplot(modules, 2, ALL.filtered, norm="raw", group=group)
@ 
\begin{figure}
\centering
<<mnplot-real,fig=TRUE,echo=FALSE>>=
<<mnplot>>
@ 
\caption{Group means against each other, for B-cell and T-cell
  samples, for all genes in the second bicluster.}
\label{fig:mnplot}
\end{figure}

\section{From \Rclass{ISAModules} to \Rclass{Biclust}}

It is also possible to convert an \Rclass{ISAModules} object to a 
\Rclass{Biclust} object, but this involves some information loss. The reason
for this is, that ISA biclusters are not binary, but the genes and 
the samples both have scores between minus one and one; whereas 
\Rclass{Biclust} biclusters are required to be binary. 

We make use of the small sample set of modules that is included in the
\Rpackage{eisa} package. These were generated for the ALL data set.
<<load isa data>>=
data(ALLModules)
ALLModules
@ 

The conversion from \Rclass{ISAModules} to \Rclass{Biclust} can be done
the usual way, using the \Rfunction{as} function:
<<isa to biclust>>=
BcMods <- as(ALLModules, "Biclust")
BcMods
@

\subsection{Coherence of biclusters}

The usual methods of the \Rclass{Biclust} class can be applied to 
\texttt{BcMods} now. E.g. we can calculate the coherence of the 
biclusters:
<<coherence>>=
data <- exprs(ALL[featureNames(ALLModules),])
constantVariance(data, BcMods, 1)
additiveVariance(data, BcMods, 1)
multiplicativeVariance(data, BcMods, 1)
signVariance(data, BcMods, 1)
@

As another example, we calculate these coherence measures for all 
modules and compare them to the ISA robustness measure.
<<coherence vs robustness>>=
cV <- sapply(1:BcMods@Number, function(x) constantVariance(data, BcMods, x))
aV <- sapply(1:BcMods@Number, function(x) additiveVariance(data, BcMods, x))
mV <- sapply(1:BcMods@Number, function(x) multiplicativeVariance(data, BcMods, x))
sV <- sapply(1:BcMods@Number, function(x) signVariance(data, BcMods, x))
rob <- ISARobustness(ALL, ALLModules)
@

Let's create a pairs-plot to visualize the relationship of these measures
for our data set, the result is in Fig.~\ref{fig:coherence}.
<<pairs,eval=FALSE>>=
panel.low <- function(x, y) {
  usr <- par("usr")
  m <- c((usr[2]+usr[1])/2, (usr[4]+usr[3])/2)
  text(m[1], m[2], adj=c(1/2,1/2), cex=1.5,
       paste(sep="\n", "Correlation:", round(cor(x,y),2)))
}
pairs( cbind(cV, aV, mV, sV, rob), lower.panel=panel.low )
@
\begin{figure}
\centering
<<pairs-real,fig=TRUE,echo=FALSE>>=
<<pairs>>
@ 
\caption{Relationship of the various bicluster coherence measueres and
  the ISA robustness measure. They show high correlation.}
\label{fig:coherence}
\end{figure}

\section{More information}

For more information about the ISA, please see the references
below. The ISA homepage at
\url{http://www.unil.ch/cbg/homepage/software.html} has example data
sets, and all ISA related tutorials and papers.

\section{Session information}

The version number of R and packages loaded for generating this
vignette were:

<<sessioninfo,results=tex,echo=FALSE>>=
toLatex(sessionInfo())
@ 

\bibliographystyle{apalike}
\bibliography{EISA}
\nocite{*}

\end{document}