%
% NOTE -- ONLY EDIT THE .Rnw FILE!!!  The .tex file is
% likely to be overwritten.
%
% \VignetteIndexEntry{Visualizing Data Using GOstats}
%\VignetteDepends{Biobase, genefilter, graph, GO, hgu95av2, xtable, multtest}
%\VignetteSuggests{Rgraphviz}
%\VignetteKeywords{EDA, graphs, ontology, visualization}
%\VignettePackage{GOstats}
\documentclass[12pt]{article}

\usepackage{times}
\usepackage{hyperref}

\usepackage[authoryear,round]{natbib}
\usepackage{times}
\usepackage{comment}

\textwidth=6.2in
\textheight=8.5in
%\parskip=.3cm
\oddsidemargin=.1in
\evensidemargin=.1in
\headheight=-.3in

\newcommand{\scscst}{\scriptscriptstyle}
\newcommand{\scst}{\scriptstyle}

\newcommand{\Rfunction}[1]{{\texttt{#1}}}
\newcommand{\Robject}[1]{{\texttt{#1}}}
\newcommand{\Rpackage}[1]{{\textit{#1}}}
\newcommand{\Rclass}[1]{{\textit{#1}}}


\bibliographystyle{plainnat}

\title{Visualizing and Distances Using GO}
\author{R. Gentleman}

\begin{document}

\maketitle

\section*{Introduction}

The basic characteristics of the GO \citep{GO} data are described in
\cite{GentGO2004a} and the interested reader is referred there for
more details. In this paper we assume that readers are familiar with
the basic DAG structure of GO and with the mappings of genes to GO
terms that are provide by GOA \citep{GOA}. We examine some of the
different visualization problems and data analytic problems that can
be addressed using these data. To make some of the concepts concrete
and to provide extensive, but related examples we will make use of a
microarray data set \citep{Chiaretti2004}.


We begin with brief descriptions and reminders of some of the
important concepts, as well as of our example data. Once that has been
done we will then consider several different bioinformatic tasks that
are of interest and show how these tasks can be carried out using
software from the Bioconductor Project.
%%FIXME: once we have centered on tasks we can come back and fill in
%%details

<<Setup, echo=FALSE, results=hide>>=
library("Biobase")
library("annotate")
library("GOstats")
library("xtable")
library("multtest")
require("Rgraphviz", quietly=TRUE)
library("hgu95av2")
library("genefilter")
@

The results reported in this document are based on version
\Sexpr{packageDescription("hgu95av2")[["Version"]]} of the
\Rpackage{hgu95av2} data package and on
\Sexpr{packageDescription("GO")[["Version"]]} of the \Rpackage{GO}
package. If you have different versions your results will probably be
different.

\subsection{Graphs}

The relationships between different GO terms, within a specific
ontology are represented in the form of a directed acyclic graph.
The leaves of this graph represent the most specific terms and their
are edges from a specific term (child) to all less specific terms
(each is a parent). The \textit{induced} GO graph is the graph that
obtains from taking a set of GO terms and finding all parents of those
terms, and so on until the root node has been obtained. Given any set
of genes one may use the mappings provided by GOA \citep{GOA} to  find
the most specific set of GO terms associated with those genes and
hence, from those, to the induced GO graph. Thus we can speak of the
induced GO graph either from the perspective of selected genes or from
the perspective of selected terms.

We note that there is a form of algebra possible on these induced GO
graphs. If $G_1$ and $G_2$ are two different GO graphs from the same
ontology then we can form their union, $G_1 \cup G_2$ which is the
union of the nodes in the two graphs together with the union of the
edge sets. We note that this is a bit different than the usual
mathematical definition of the union of two graphs. Other set
operations such as intersection and complement are also easy to define
and implement and they will play a role in our subsequent discussions.

\subsection{Some problems}

One should be aware that our state of biological knowledge is
constantly changing and that the terms, their relationships and the
genes mapped to them are constantly changing and being updated. The
meta-data used to prepare this report will likely be out of date in
less than one year. Hence, one must try to ensure that the mappings
being used are up to date (Bioconductor meta-data packages are built
quarterly).

Another practical issue that needs some attention is consideration of
the incompleteness of the data. Not all genes (or all transcripts) are
assayed and not all genes are well enough understood to accurately
annotate them at the existing GO nodes; to say nothing of the issues
regarding the evolving nature of GO discussed above. For each problem
the data analyst will need to consider what, if any, biases these
issues might raise.

In all comparisons and gene list developments reported in the
remainder of this paper we restrict attention to those genes which
satisfy two conditions first they were included on the chip being used
and second they have a GO annotation for the ontology of interest.

\subsection{An Example}
\label{sec:example}

To demonstrate some of the tools that are included in the
\Rpackage{GOstats} package we consider expression data from 79 samples
from patients with acute lymphoblastic leukemia (ALL) that were
investigated using HGU95AV2 Affymetrix GeneChip arrays
\citep{Chiaretti2004}.  The data were normalized using quantile
normalization and expression estimates were computed using
RMA~\citep{Irizarry2003}. We will consider the subset of these
patients that have B-cell ALL and within those we will further
restrict our interest to comparing three groups. Those with BCR/ABL
(this is a translocation between chromosomes 9 and 22), those with
ALL1/AF4 (this is a translocation between chromosomes 4 and 11) and
those with no detected cytogenetic abnormalities (labeled NEG).  There
are 37 patients with BCR/ABL, 10 with ALL1/AF4 and 42 from the NEG
group.

We will examine genes that are differentially expressed between these
different phenotypes and then make use of GO and other meta-data
resources to help understand the genes that were selected. The actual
method of determining differentially expressed genes is in some sense
irrelevant to the subsequent analysis using GO and readers can easily
substitute their own favorite methods. We refer readers to
\cite{WileyEncy} for a general discussion of some of the issues
involved in gene filtering.


<<Example, results=hide, echo=FALSE>>=
## subset of interest: 37+42 samples
data(ALL)
eset <- ALL[, intersect(grep("^B", as.character(ALL$BT)),
          which(as.character(ALL$mol) %in% c("BCR/ABL","NEG", "ALL1/AF4")))]

MB = factor(eset$mol.bio)

## intensities above 100 in at least 7 of the samples
f1 <- kOverA(7, log2(100))
f2 = function(x) {gps = split(2^x, MB); mns=sapply(gps, mean);
          if(max(mns) - min(mns) < 100) FALSE else TRUE}
ff <- filterfun(f1, f2)
selected <- genefilter(eset, ff)
sum(selected)
esetSub <- eset[selected,]

@

Our approach is fairly simplistic. First, we require that genes be
expressed in a reasonable set of the samples. Since our smallest
group, those with ALL1/AF4, has only 10 samples we will require that a
gene be expressed in at least 7 samples to be deemed interesting. For
our purposes we consider a gene to be expressed if its value is
greater than 100. We also introduce a second selection criteria that
is based on differences between the group means. We require that the
difference between the smallest group mean and the largest be larger
than 100 units. Applying both of these transformations yielded
\Sexpr{sum(selected)} probes that were differentially expressed.

<<mtFstats, results=hide, echo=FALSE>>=

 pvCutOff = 0.05

 Fstats = mt.maxT(exprs(esetSub), as.numeric(MB)-1,
                   B=1000, test="f")

 eSet = esetSub[Fstats$index[Fstats$adjp<pvCutOff],]

 gN = geneNames(eSet)
 lls = unlist(mget(gN, hgu95av2LOCUSID, ifnotfound=NA))

 eS = eSet[!duplicated(lls),]
 gN = geneNames(eS)
 lls = unlist(mget(gN, hgu95av2LOCUSID, ifnotfound=NA))
 lls = as.character(lls)
 syms = unlist(mget(gN, hgu95av2SYMBOL, ifnotfound=NA))

@

Next restricting attention to just these \Sexpr{sum(selected)}
probes we use the \Rfunction{mt.maxT} function from the
\Rpackage{multtest} package to compute adjusted $p$-values for F-test
comparisons.  We found \Sexpr{sum(Fstats$adjp < pvCutOff)} of these to
have adjusted $p$-values less than \Sexpr{round(pvCutOff, 2)}. We will
deem these to be the differentially expressed genes and will now turn
our attention to using GO to gain a better understanding of the
associated genes.  Before going further though, we note that these
\Sexpr{sum(Fstats$adjp < pvCutOff)} probes map to
\Sexpr{sum(!duplicated(lls))} distinct LocusLink identifiers and since
our GO mappings are based on LocusLink we further reduce our attention
to these. We kept the first (in index order) of each of the duplicated
probes for any analyses or computations that require gene expression
data and note that others may want to use different conventions.

\section{Visualization}

Using the genes selected in Section~\ref{sec:example} we can
construct the induced GO graph for any one of the three different
Ontologies. In Figures~\ref{fig:mf}, \ref{fig:bp} and \ref{fig:cc} we
present these three different graphs. Of course one would like to add
some of the experimental data and information to these plots.

<<inducedGO, echo=FALSE, results=hide>>=

 gMF <- makeGOGraph(lls, "MF")
 gBP = makeGOGraph(lls, "BP")
 gCC = makeGOGraph(lls, "CC")


 nMF = list()
 lbs = rep("", length(nodes(gMF)))
 names(lbs) = nodes(gMF)
 nMF$label = lbs

 nBP = list()
 lbs = rep("", length(nodes(gBP)))
 names(lbs) = nodes(gBP)
 nBP$label = lbs

 nCC = list()
 lbs = rep("", length(nodes(gCC)))
 names(lbs) = nodes(gCC)
 nCC$label = lbs

if( require("Rgraphviz") ) {
  gMFlo = agopen(gMF, "gMF")
  gBPlo = agopen(gBP, "gBP")
  gCClo = agopen(gCC, "gCC")
}

@

\begin{figure}[htbp]
\begin{center}

<<GOMF, fig=TRUE, echo=FALSE>>=
  if (require("Rgraphviz"))
     plot(gMFlo, nodeAttrs=nMF, main="Molecular Function")
@
\caption{The induced MF GO graph for the selected genes. \label{fig:mf}}
\end{center}
\end{figure}

\begin{figure}[htbp]
\begin{center}

<<GOBP, fig=TRUE, echo=FALSE>>=
  if (require("Rgraphviz"))
    plot(gBPlo, nodeAttrs=nBP, main="Biological Process")
@
\caption{The induced BP GO graph for the selected genes. \label{fig:bp}}
\end{center}
\end{figure}

\begin{figure}[htbp]
\begin{center}

<<GOCC, fig=TRUE, echo=FALSE>>=
  if (require("Rgraphviz"))
    plot(gCClo, nodeAttrs=nCC, main="Cellular Component")
@
\caption{The induced CC GO graph for the selected genes. \label{fig:cc}}
\end{center}
\end{figure}

The nodes in these different graphs can be colored according to
different criteria. In \cite{Storch} they considered using color for
the nodes. The studied differences in gene expression
between heart and liver in mice.  Their Figure 2 presents data that
were assembled in roughly the following manner. Genes of interest were
mapped to terms in the Biological Process arm of GO \cite{GO}.  They
then colored the nodes in this tree red if the associated genes were
expressed in heart, green if they were expressed in liver and yellow
if they were expressed in both. The figure itself is quite helpful and
clearly displays the data informatively.

This is an interesting idea and we amplify it a bit here. In our
example we have three groups and we might want to consider coloring
the nodes differently. For each node in the induced graph there is one
or more genes from our list of interesting genes annotated at that
node (due to the way we constructed the graph). We could color the
nodes according to the phenotype (ALL1/AF4, BCR/ABL, or NEG) that had
the highest mean (if there is more than one gene then we might use the
multiplicity of highest means). Or, possibly of more interest would be
to categorize the genes (again we could use which phenotype had the
highest mean) and render the nodes using pie charts.

The computations are quite straightforward.
First we find the maximum mean for each gene. We see that BCR/ABL has
the most maxima, followed by the ALL1/AF4 samples and finally the NEGs.

<<findhigestmeans>>=
mns = apply(exprs(eS), 1, function(x) sapply(split(x, MB), mean))
whismax = apply(mns, 2, function(x) match(max(x), x))
maxNames = names(mns[,1])[whismax]
names(maxNames) = names(whismax)
table(maxNames)
@

We next need to associate each GO term with the set of these genes and
classify them according to which phenotype has the highest mean.
Since the CC ontology has the fewest nodes in the induced graph we
will use it for the examples here -- the reader may want to adapt the
code to deal with the other two ontologies.

<<genesToGO>>=

 CCnodes = nodes(gCC)
 nodes2affy = mget(CCnodes, hgu95av2GO2ALLPROBES)

 cts = sapply(nodes2affy, function(x) {
       wh = names(whismax) %in% x
       table(maxNames[wh])
   })

 all1cts = sapply(cts, function(x) x["ALL1/AF4"])
 all1cts = ifelse(is.na(all1cts), 0, all1cts)
 ##put nice names on
 names(all1cts) = names(cts)

 bcrcts = sapply(cts, function(x) x["BCR/ABL"])
 bcrcts = ifelse(is.na(bcrcts), 0, bcrcts)
 names(bcrcts) = names(cts)

 negcts = sapply(cts, function(x) x["NEG"])
 negcts = ifelse(is.na(negcts), 0, negcts)
 names(negcts) = names(cts)

 ctmat = cbind(all1cts, bcrcts, negcts)
@

Now that we have assembled the requisite counts we are now ready to
layout and render the graph. The code in the following code chunk does
this. We first do a layout to get the node positions and then write a
function that will draw each node as a piechart and color the
components the requested colors.


<<layoutandrender>>=

if( require(Rgraphviz) ) {
opar = par(xpd = NA)
plotPieChart <- function(curPlot, counts, main) {
    renderNode <- function(x) {
        force(x)
        y <- x*100+1
        function(node, ur, attrs=list(), radConv=1) {
            nodeCenter <- getNodeCenter(node)
            pieGlyph(y, xpos=getX(nodeCenter),
                     ypos=getY(nodeCenter),
                     radius=getNodeRW(node),
                     col=c("blue", "green", "red"))
        }
    }
    drawing <- vector(mode="list", length=nrow(counts))
    for (i in 1:length(drawing)) {
        drawing[[i]] <- renderNode(counts[i,])
    }
    if( missing(main) )
       main="Example Pie Chart Plot"

    plot(curPlot, drawNode=drawing, main=main)

    legend(300, 65, legend=c("ALL1/AF4", "BCR/ABL", "NEG"),
          fill=c("blue", "green", "red"))
}
plotPieChart(gCClo, ctmat)
par(opar)
}
@

We can see a number of interesting features in this plot. For example,
long sequences of single colored nodes - as set of blue nodes on the
right and another set of red nodes on the left. We might want to
investigate these and see what cellular components they are related
to.

We can make use of the \Rfunction{imageMap} function from the
\Rpackage{prada} package to provide tool tips for our graph.

<<imageMap>>=

 getNodeBB = function(ingraph) {
     xypos = getNodeXY(ingraph)
     hts = getNodeHeight(ingraph)
     wdsR = getNodeRW(ingraph)
     wdsL = getNodeLW(ingraph)
     llx = xypos$x - wdsL
     lly = xypos$y - (hts/2)
     urx = xypos$x + wdsR
     ury = xypos$y + (hts/2)
     cbind(llx, lly, urx, ury)
 }

 CCbb = getNodeBB(gCClo)

 ##now we need to adjust the graphBB to the plot region
 bbG = boundBox(gCClo)
 llx = getX(botLeft(bbG))
 lly = getY(botLeft(bbG))
 urx = getX(upRight(bbG))
 ury = getY(upRight(bbG))


##FIXME: work in progress here - we need to do a bit of mapping
##to get the right user coords on the jpg file...
if (interactive()) {
 jpeg("mygraph.jpg", quality=100, width=urx-llx, height=ury-lly)
  opar = par(plt=c(0,1,0,1), xpd=NA)
  plotPieChart(gCClo, ctmat)
  par(opar)
 dev.off()
}
 if( require(prada) ) {
     con = openHTMLpage("example", "An Image Map")

     imageMap(con, "mygraph.jpg", CCbb, tooltips=getNodeNames(gCClo),
              url=rep("", length(nodes(gCC))))

     closeHTMLpage(con)
 }


@
\section{GO induced Distances}

Another idea that has seen some exploration is the notion of defining
distances between two genes in terms of the similarity of their GO
annotations. We explore some of the suggestions and provide some
guidance for those who would like to explore these ideas.

It seems reasonable to treat the three ontologies separately. Even
within an ontology a single gene might be annotated at several GO
terms. Some mechanisms for dealing with this issue would be helpful.
For each gene we could define the graph to be that which is induced by
all GO terms to which the gene is mapped, within the ontology of
interest. Then use the between graph distances as already described.
One could define distance as some combination of the annotation
specific distances. For example, if gene 1 is annotated at 2 terms and
gene 2 is annotated at three terms there are 6 distances to be
computed and some functional of these 6 distances will be reported.



There are two basic strategies that have been widely used to define
distances, or similarities, between genes based on GO annotation. One,
is to make use of the graph structure that is associated with each
pair of genes. If the two graphs are very similar then the two genes
are presumed to also be quite similar. The second general strategy is
to use the information content in GO as a basis for assigning
similarity. Readers should consult \cite{Lordetal} for a general
review of the area and for some suggestions. Code contributions that
implement some of their methods would be most welcome.

Two methods based on graph similarity have been implemented in
\Rpackage{GOstats} they are \Rfunction{simUI} and
\Rfunction{simLP}. The first uses the number of nodes that the two
graphs have in common divided by the number of nodes in the union of
the two node sets. Hence these similarities are bounded between 0 and 1
with genes that are more similar having values near one.

For \Rfunction{simLP} the similarity measure is the depth of the
longest shared path from the root node. Two genes that are both quite
specific and similar should have long shared paths, while those that
have less in common should have relatively short shared paths.

Of course neither is perfect and there are many cases where they seem
to fail to uncover similarity and others where they suggest a
similarity that does not exist. Further investigation and comparison
with other methods is certainly warranted.

To compare different genes with respect to their GO annotation you may
use \Rfunction{simLL} which first computes the induced GO graph for each of
its two, user supplied, LocusLink IDs and then calls either
\Rfunction{simLP} or \Rfunction{simUI}. Users can restrict the
mappings from LocusLink IDs to GO IDs based on evidence codes.

Some further work is needed to correctly address two outstanding
issues. First, in some cases a LocusLink identifier is mapped to
several GO terms and in the current implementation these are merely
combined into one large graph. However, there may be some benefits in
considering the comparison of each induced graph separately. For
example, suppose that we have a gene, $G_1$ that performs a specific
molecular function and we want to know which other genes are
reasonably similar to it. We might not care if those other genes also
do something else and so in this case we would probably use each
mapping separately.

The second issue that needs to be addressed at some time is to include
functionality that would allow the user to construct a GO graph based
only on \textit{is-a} relationships or only on \textit{partof}
relationships.

In the next code chunk we take the first 10 genes selected and
construct a set of similarity measures between all pairs of genes with
respect to the BP ontology.

<<multip>>=

 igenes = unlist(mget(gN[1:10], hgu95av2LOCUSID))

 igenes = igenes[!is.na(igenes)]
 igenes = as.character(igenes)


 GOs = mget(as.character(igenes), GOLOCUSID2GO, ifnotfound=NA)
 gBP = sapply(GOs, getOntology, "BP")
 ##how many terms

 sum(sapply(gBP, length))

 ##drop those with no BP annotations
 hasBP = sapply(gBP, function(x) length(x) > 0)

 gBP = gBP[hasBP]

 ggs = lapply(igenes[hasBP], makeGOGraph, "BP")

## you could also do ggx = lapply(gBP, GOGraph, GOBPPARENTS)
## and should get the same thing

 simatM1 = matrix(1, nr=sum(hasBP), nc=sum(hasBP))
 for(i in 1:sum(hasBP))
    for( j in 1:sum(hasBP) )
        if( i== j ) next else
          simatM1[i,j] = simUI(ggs[[i]], ggs[[j]])

 library(RBGL)

 simatM2 = matrix(1, nr=sum(hasBP), nc=sum(hasBP))
 for(i in 1:sum(hasBP))
    for( j in 1:sum(hasBP) )
        if( i== j ) next else {
          simatM2[i,j] = simLP(ggs[[i]], ggs[[j]])
      }

@

You can plot these two measures against each other and compare
them. We hope that the two measures are somewhat positively
correlated.



\bibliography{GOstats}

\end{document}

