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\title{Practicalities of machine learning: applications to high-throughput genomics
with Bioconductor MLInterfaces 2.0}
\author{VJ Carey}
\maketitle

\tableofcontents

\section{Overview}

The term \textit{machine learning} refers to a family of
computational methods for analyzing multivariate datasets.
Each data point has a vector of \textit{features} in a shared
\textit{feature space},
and may have a \textit{class label} from some fixed finite set.

\textit{Supervised learning} refers to processes
that help articulate rules that map \textit{feature vectors}
to \textit{class labels}.  The class labels are known and
function as supervisory information to guide rule construction.
\textit{Unsupervised learning} refers to processes
that discover structure in collections of feature
vectors.  Typically the structure consists of a grouping
of objects into clusters.

This practical introduction to machine learning will begin
with a survey of a low-dimensional dataset to fix concepts,
and will then address problems coming from expression arrays
and array CGH data.

Some basic points to consider at the start:
\bi
\item Distinguish predictive modeling from inference
on model parameters.  Typical work in epidemiology focuses
on estimation of relative risks, and random samples are not
required.  Typical work with machine learning tools targets
estimation (and minimization) of the misclassification rate.
Representative samples are required for this task.
\item ``Two cultures'': model fitters vs. algorithmic predictors.
If statistical models are correct, parameter estimation based
on the mass of data can
yield optimal discriminators (e.g., LDA).  Algorithmic discriminators 
tend to prefer
to identify boundary cases and downweight the mass of data (e.g., boosting,
svm).
\item Different learning tools have different capabilities.
There is little \textit{a priori} guidance on matching learning algorithms to
aspects of problems.  While it is convenient to sift through a variety
of approaches, one must pay a price for the model search.
\item Data and model/learner visualization are  important, but
visualization in higher dimensional data structures is hard.
Dynamic graphics can help; look at ggobi and Rggobi for this.
\item These notes provide very little mathematical background
on the methods; see for example Ripley (\textit{Pattern recognition and neural networks},
1995), Duda, Hart, Stork (\textit{Pattern classification}),
Hastie, Tibshirani and Friedman (2003, 
\textit{Elements of statistical learning}) for copious
background.
\ei

\section{Getting acquainted with machine learning via the crabs data}

\subsection{Attaching and checking the data}

The following steps bring the crabs data into scope and
illustrate aspects of its structure.

<<intro1>>=
library(MASS)
data(crabs)
dim(crabs)
crabs[1:4,]
table(crabs$sex)
@
<<dolat,fig=TRUE>>=
library(lattice)
print(bwplot(RW~sp|sex, data=crabs))
@

We will regard these data as providing five features
(FL, RW,   CL,   CW,  BD) and a pair of class labels (sex, sp=species).
We may regard this as a four class problem, or as two two class
problems.

\subsection{A simple classifier derived by human reasoning}

Our first problem does not involve any computations.
If you want to write R code to solve the problem, do so,
but use prose first.

\newcounter{mlq}
\setcounter{mlq}{1}
\newcommand{\MLQ}{\arabic{mlq}}
\bi
\item \textit{Question \MLQ.}  On the basis of the lattice
plot given above, comment on the prospects
for predicting species on the basis of RW.  State a
rule for computing the predictions.  Describe how to assess the
performance of your rule.
\ei

\subsection{Prediction via logistic regression}

A simple approach to prediction involves logistic regression.
<<dop>>=
m1 = glm(sp~RW, data=crabs, fam=binomial)
summary(m1)
@

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  Write down the statistical model
corresponding to the R expression above.  How can we derive
a classifier from this model?
\ei

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  Perform the following
computations.  Discuss their interpretation.
What are the estimated error rates of the two
models?  Is the second model better?
\ei

<<domods, results=hide>>=
table(predict(m1,type="response")>.5, crabs$sp)
m2 = update(m1, subset=sex=="F")
table(predict(m2,type="response")>.5, crabs$sp[crabs$sex=="F"])
@

\subsection{The cross-validation concept}

Cross-validation is a technique that is widely used for
reducing bias in estimation of predictive accuracy.
Briefly, the dataset is deterministically partitioned into
a series of training and test sets.  The model is built
for each training set and evaluated on the test set.
The accuracy measures are averaged over this series
of fits.  Leave-one-out cross-validation consists of N
fits, with N training sets of size N-1 and N test sets
of size 1.

%Convenient interfaces for cross-validation with Bioconductor's
%MLInterfaces are only worked
%out for ExpressionSets.  So we will convert the crabs data
%to such a structure in the next few commands.


First let's use \texttt{MLearn} from the 
\textit{MLInterfaces} package to fit a single logistic model.
\texttt{MLearn} requires you to specify a training index set.
We use \texttt{c(1:30, 51:80)} to choose a training set of
size 60, balanced between two species (because we know the
ordering of records).  This procedure also requires you
to specify a probability threshold for classification.
We use a typical default of 0.5.  If the predicted probability
of being "O" exceeds 0.5, we classify to "O", otherwise to "B".
<<doml1>>=
library(MLInterfaces)
fcrabs = crabs[crabs$sex == "F", ]
ml1 = MLearn( sp~RW, fcrabs, glmI.logistic(thresh=.5), c(1:30, 51:80), family=binomial)
ml1
confuMat(ml1)
@

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  What does the report
on \texttt{ml1} tell you about predictions with this model?
Can you reconcile this with the results in model \texttt{m2}?
[Hint -- non-randomness of the selection of the training set
is a problem.]
\ei

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  Modify the
MLearn call to obtain a predictor that is more successful on the 
test set.
% ROC curve?
\ei

Now we will illustrate cross-validation.  First, we scramble
the order of records in the \texttt{ExpressionSet}
so that sequentially formed groups are approximately
random samples.

<<doscra>>=
set.seed(123)
sfcrabs = fcrabs[ sample(1:100, size=100, replace=FALSE),  ]
@

We invoke the MLearn method in two ways -- first specifying a training 
index set, then specifying a five-fold cross-validation.
<<domods>>=
sml1 = MLearn( sp~RW, sfcrabs, glmI.logistic(thresh=.5), c(1:30, 51:80), family=binomial)
confuMat(sml1)
smx1 = MLearn( sp~RW, sfcrabs, glmI.logistic(thresh=.5), xvalSpec("LOG",
   5, function(data, clab, iternum) { which(rep(1:5, each=20) == iternum) }), family=binomial)
confuMat(smx1)
@

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  Define clearly the
difference between models sml1 and smx1 and state the
misclassification rate estimates associated with each
model.
\ei

\subsection{Exploratory multivariate analysis}

\subsubsection{Scatterplots}

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  Interpret the following
code.  Modify it to depict the pairwise configurations
with different colors for crab genders.
\ei

<<dopa,fig=TRUE>>=
pairs(crabs[,-c(1:3)], col=ifelse(crabs$sp=="B", "blue", "orange"))
@

\subsubsection{Principal components; biplot}

Principal components analysis transforms the multivariate
data X into a new coordinate system.  If the original variables
are X1, \ldots, Xp, then the variables in the new representation
are denoted PC1, \ldots, PCp.  These new variables have
the properties that PC1 is the linear combination of the X
having maximal variance, PC2 is the variance-maximizing linear combination of
residuals of X after projecting on PC1, and so on.
If most of the variation in $X_{n \times p}$ can be captured in 
a low dimensional linear subspace of the space spanned
by the columns of $X$, scatterplots of
the first few principal
components will depict this.

Formally, we can compute the PC using the singular value decomposition of
$X$, in which $X = UDV^t$, where $U_{n \times p}$ and $V_{p \times p}$
are orthonormal, and $D$ is a diagonal matrix of $p$ nonnegative
singular values.  The principal components transformation is
$XV = UD$, and if $D$ is structured so that $D_{ii} \geq D_{jj}$ whenever
$i > j$, then column $i$ of $XV$ is PCi.  Note also that $D_{ii} =
\sqrt{n-1}\mbox{SD}$ PCi.

<<dopc>>=
pc1 = prcomp( crabs[,-c(1:3)] )
<<lkpc, fig=TRUE>>=
pairs(pc1$x, col=ifelse(crabs$sp=="B", "blue", "orange"))
@

The biplot shows the data in PC space and also shows the
relative contributions of the original variables in composing
the transformation.

<<dobi,fig=TRUE>>=
biplot(pc1, choices=2:3)
@

\subsubsection{Clustering}

A familiar technique for displaying multivariate data
in high-throughput biology is called the heatmap.
In this display, samples are clustered as columns, and
features as rows.  The clustering technique used
by default is R 'hclust'.
This procedure builds a clustering
tree for the data as follows.
Distances are computed between feature
vectors for all $N$ observations.
The two closest observations are joined and
are regarded as a new object,
so there
are $N-1$ objects (clusters) at this point.  
This process is repeated until 1 cluster is formed;
the clustering tree shows the process by which
clusters are created via agglomeration.

There are several ways to measure distances
between clusters, once these
have been formed.  Single-linkage clustering
takes the distance between two clusters
to be the shortest distance between any two
members of the different clusters;
average linkage averages all the distances
between members; complete-linkage uses hte
maximum distance between any two members of the different
clusters.  Other methods are available in hclust.

The following figure shows cluster trees
for samples and features, using all defaults
for the heatmap function.
A tiled display at the top shows the
species codes for the samples.

<<dohm,fig=TRUE>>=
X = data.matrix(crabs[,-c(1:3)])
heatmap(t(X), ColSideColors=ifelse(crabs$sp=="O", "orange", "blue"))
@

Typically clustering is done in the absence of labels -- it is
an example of unsupervised machine learning.  We can ask
whether the clustering provided is a 'good' one using
the measurement of a quantity called the \textit{silhouette}.
This is defined in R documentation as follows:
\begin{verbatim}
     For each observation i, the _silhouette width_ s(i) is defined as
     follows: 
      Put a(i) = average dissimilarity between i and all other points
     of the cluster to which i belongs (if i is the _only_ observation
     in its cluster, s(i) := 0 without further calculations). For all
     _other_ clusters C, put d(i,C) = average dissimilarity of i to all
     observations of C.  The smallest of these d(i,C) is b(i) := min_C
     d(i,C), and can be seen as the dissimilarity between i and its
     "neighbor" cluster, i.e., the nearest one to which it does _not_
     belong. Finally, 

             s(i) := ( b(i) - a(i) ) / max( a(i), b(i) ).
\end{verbatim}

We can compute the silhouette for any partition of a dataset,
and can use the hierarchical clustering result to define a partition
as follows:

<<docl>>=
cl = hclust(dist(X))
tr = cutree(cl,2)
table(tr)
<<dos,fig=TRUE>>=
library(cluster)
sil = silhouette( tr, dist(X) )
plot(sil)
@

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  In the preceding, we
have used default \texttt{dist}, and default clustering
algorithm for the heatmap.  Investigate the impact of
altering the choice of distance and
clustering method on the clustering performance,
both in relation to capacity to recover groups defined by
species and in relation to the silhouette distribution.
\addtocounter{mlq}{1}
\item \textit{Question \MLQ.}  The PCA shows that
the data configuration in PC2 and PC3
is at least bifurcated.  Apply hierarchical
and K-means clustering to the two-dimensional
data in this subspace, and compare results
with respect to capturing the species $\times$
gender labels, and with respect to silhouette
values.  For example, load the exprs slot of
crES [see just below for the definition
of this structure] with the PCA reexpression of the features,
call the result pcrES, and then:
\begin{verbatim}
> ff = kmeansB(pcrES[2:3,], k=4)
> table(ff@clustIndices, crES$spsex)
\end{verbatim}
\ei

\subsection{Supervised learning}

In this section we will examine procedures
for polychotomous prediction.
We want to be able to use the measurements
to predict both species and sex of the crab.
Again we would like to use the MLInterfaces
infrastructure, so an ExpressionSet
container will be useful.
<<newes>>=
feat2 = t(data.matrix(crabs[, -c(1:3)]))
pd2 = new("AnnotatedDataFrame", crabs[,1:2])
crES = new("ExpressionSet", exprs=feat2, phenoData=pd2)
crES$spsex = paste(crES$sp, crES$sex, sep=":")
table(crES$spsex)
@
We will permute the samples so that simple
selections for training set indices are
random samples.
<<doper>>=
set.seed(1234)
crES = crES[ , sample(1:200, size=200, replace=FALSE)]
@
\subsubsection{RPART}

A classic procedure is recursive partitioning.
<<dotr>>=
library(rpart)
tr1 = MLearn(spsex~., crES, "rpart", 1:140)
tr1
confuMat(tr1)
@

The actual tree is
<<doplTree,fig=TRUE>>=
plot(RObject(tr1))
text(RObject(tr1))
@
This procedure includes a diagnostic tool called the
cost-complexity plot:
<<doccp,fig=TRUE>>=
plotcp(RObject(tr1))
@


\subsubsection{Random forests}

A generalization of recursive partitioning is obtained
by creating a collection of trees by
bootstrap-sampling cases and randomly sampling from
features available for splitting at nodes.

<<dorf>>=
set.seed(124)
library(randomForest)
rf1 = MLearn(spsex~., crES, "randomForest", 1:140 )
rf1
cm = confuMat(rf1)
cm
@

The single split error rate is estimated at 
\Sexpr{round(1-(sum(diag(cm))/sum(cm)),2)*100}\%. 

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  What is the out-of-bag
error rate for rf1?  Obtain a cross-validated
estimate of misclassification error using randomForest
with an xvalSpec().
\ei

\subsubsection{Linear discriminants}

<<dold>>=
ld1 = MLearn(spsex~., crES, "lda", 1:140 )
ld1
confuMat(ld1)
xvld = MLearn( spsex~., crES, ldaI, xvalSpec("LOG", 5, balKfold.xvspec(5)))
confuMat(xvld)
@

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  Use the balKfold
function to generate an index set for
partitions that is balanced
with respect to class distribution.  Check the
balance and repeat the
cross validation.
\ei

\subsubsection{Neural net}

<<dnn>>=
nn1 = MLearn(spsex~., crES, nnetI, 1:140, size=3, decay=.1)
nn1
RObject(nn1)
confuMat(nn1)
<<doxx,results=hide>>=
xvnnBAD = MLearn( spsex~., crES, nnetI, 
    xvalSpec("LOG", 5, function(data, clab, iternum) {
      which( rep(1:5,each=40) == iternum ) }),
      size=3, decay=.1 )
xvnnGOOD = MLearn( spsex~., crES, nnetI, 
    xvalSpec("LOG", 5, balKfold.xvspec(5) ),
      size=3, decay=.1 )
<<lktann>>=
confuMat(xvnnBAD)
confuMat(xvnnGOOD)
@

\subsubsection{SVM}

<<dnn>>=
sv1 = MLearn(spsex~., crES, svmI, 1:140)
sv1
RObject(sv1)
confuMat(sv1)
<<doxxs,results=hide>>=
xvsv = MLearn( spsex~., crES, svmI, xvalSpec("LOG", 5,
    balKfold.xvspec(5)))
<<lktasv>>=
confuMat(xvsv)
@

\section{Learning with expression arrays}

Here we will concentrate on ALL: acute lymphocytic
leukemia, B-cell type.


\subsection{Phenotype reduction}

We will identify expression patterns
that discriminate individuals with BCR/ABL fusion in
B-cell leukemia.

<<setupALL>>=
library(ALL)
data(ALL)
bALL = ALL[, substr(ALL$BT,1,1) == "B"]
fus = bALL[, bALL$mol.biol %in% c("BCR/ABL", "NEG")]
fus$mol.biol = factor(fus$mol.biol)
fus
@

\subsection{Nonspecific filtering}

We can nonspecifically filter to 300 genes (to save computing time) with 
largest measures of robust variation across all samples:

<<getq>>=
mads = apply(exprs(fus),1,mad)
fusk = fus[ mads > sort(mads,decr=TRUE)[300], ]
fcol = ifelse(fusk$mol.biol=="NEG", "green", "red")
@

\subsection{Exploratory work}

For exploratory data analysis, a heatmap is customary.
<<dohALL,fig=TRUE,eval=FALSE>>=
heatmap(exprs(fusk), ColSideColors=fcol)
@
\includegraphics{fuskmap}

Principal components and a biplot may be more revealing.
How many principal components are likely to be important?

<<dopcALL>>=
PCg = prcomp(t(exprs(fusk)))
<<lkscre,fig=TRUE>>=
plot(PCg)
<<lkprALL,fig=TRUE>>=
pairs(PCg$x[,1:5],col=fcol,pch=19)
<<dobiALL,fig=TRUE>>=
biplot(PCg)
@

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  Modify the biplot so that instead
of plotting sample ID, the symbol "O" is plotted for a NEG
sample and "+" is plotted for a BCR/ABL sample.
\ei

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  Consider the following code
\begin{verbatim}
chkT = function (x, eset=fusk) {
t.test(exprs(eset)[x, eset$mol.b == "NEG"], exprs(eset)[x, eset$mol.b == 
    "BCR/ABL"]) }
\end{verbatim}
Use it in conjunction with the biplot to interpret
expression patterns of genes that appear to be important in
defining the PCs.
\ei


\subsection{Classifier construction}

\subsubsection{Demonstrations}

Diagonal LDA has a good reputation.  Let's try it first, followed by
neural net and random forests.  We will not attend to tuning the latter
two, defaults or guesses for key parameters  are used.
<<dld>>=
dld1 = MLearn( mol.biol~., fusk, dldaI, 1:40 )
dld1
confuMat(dld1)
nnALL = MLearn( mol.biol~., fusk, nnetI, 1:40, size=5, decay=.01,
  MaxNWts=2000 )
confuMat(nnALL)
rfALL = MLearn( mol.biol~., fusk, randomForestI, 1:40 )
rfALL
confuMat(rfALL)
@

None of these are extremely impressive, but the problem may just
be very hard.  An interesting new proposal is RDA, regularized
discriminant analysis (package rda, Guo, Hastie, Tibshirani 2007
Biostatistics).  This algorithm recognizes the fact that
covariance matrix estimation in high dimensional data is
very inaccurate, so the estimator is shrunk towards the identity.
We have a rudimentary interface, in which the
key parameters are chosen by native cross-validation (in rda.cv)
and then applied once to get the final object.
<<lkrda,results=hide>>=
rdaALL = MLearn( mol.biol~., fusk, rdacvI, 1:40 )
<<lkrda2>>=
rdaALL
confuMat(rdaALL)
@
A by-product of the algorithm is a set of retained genes that
were found to play a role in discrimination.
This can be established as follows:
<<getgl>>=
library(hgu95av2)
inds = as.numeric(which(RObject(rdaALL)$gene.list[,1,]>0))
saved = featureNames(fusk)[inds]
as.list(hgu95av2GENENAME)[saved[1:4]]
@

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  How can we assess the relative impacts
of regularization (expanding the covariance model beyond that of
DLDA, which was shown above
to do poorly, but without relying
on the full covariance) and implicit feature selection
conducted in RDA?
\ei

\subsubsection{Gene set appraisal}

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  We can assess the predictive
capacity of a set of genes by restricting the ExpressionSet
to that set and using the best classifier appropriate to
the problem.  We can also assess the incremental effect of
combining gene sets, relative to using them separately.

One collection of gene sets that is straightforward to use
and interpret is provided by the keggorth package (see also
GSEABase).  Here's how we can define the ExpressionSets for
genes annotated by KEGG to Environmental (Genetic) Information
Processing:
<<getko>>=
library(keggorth)
data(KOgraph)
adj(KOgraph, nodes(KOgraph)[1])
EIP = getKOprobes("Environmental Information Processing")
GIP = getKOprobes("Genetic Information Processing")
length(intersect(EIP, GIP))
EIPi = setdiff(EIP, GIP)
GIP = setdiff(GIP, EIP)
EIP = EIPi
Efusk = fusk[ featureNames(fusk) %in% EIP, ]
Gfusk = fusk[ featureNames(fusk) %in% GIP, ]
@
Obtain and assess the predictive capacity of the genes annotated
to "Cell Growth and Death".
\ei

\addtocounter{mlq}{1}
\bi
\item \textit{Question \MLQ.}  How many of the genes identified
by RDA as important for discriminating fusion are annotated
to Genetic Information Processing in the KEGG orthology?
\ei

\section{Embedding features selection in cross-validation}

We provide helper functions to conduct several kinds of feature
selection in cross-validation, see \texttt{help(fs.absT)}.
Here we pick the top 30 features (ranked by absolute t statistic)
for each cross-validation partition.

<<dofs>>=
dldFS = MLearn( mol.biol~., fusk, dldaI, xvalSpec("LOG", 5,
   balKfold.xvspec(5), fs.absT(30) ))
dldFS
confuMat(dld1)
confuMat(dldFS)
@


\section{Learning with array CGH}

This section contains two parts, one of which is left as a pair
of open questions.  In the first part, we use rpart as a \textit{device}
to perform segmentation of aCGH log intensity ratios.
In the second part, we consider how to extract features from the
CGH profile to learn about gene cluster activation and phenotype.


%\subsection{Working with Neve2006}
%
%We are going to examine some aCGH copy number data
%as published by Neve et al 2006.
%
%<<getdat>>=
%library(Neve2006)
%data(neveCGHmatch)
%chr = function(x) pData(featureData(x))$Chrom
%kb = function(x) pData(featureData(x))$kb
%nc17 = neveCGHmatch[ chr(neveCGHmatch) == 17, ]
%<<lk1,fig=TRUE>>=
%par(mfrow=c(3,2))
%for (i in 1:6)
% plot( logRatios(nc17)[,i] ~ kb(nc17), main=nc17$geneCluster[i],
%  ylab="log ratio", xlab="on chr 17")
%par(mfrow=c(1,1))
%@
%
%\subsection{Segmentation via rpart}
%
%First we are going to use rpart as a device for estimating
%a piecewise constant model for log ratios.  No optimality is
%claimed for the technique; it can be compared to tilingArray::segment
%and DNAcopy::segment.  However, there are infrastructure gains
%obtained by using a bona fide modeling tool, which has a generic predict().
%
%We develop additional infrastructure here:
%<<build,results=hide>>=
%setOldClass("rpart")
%setClass("rpSeg", representation(
%  obj="rpart", samp="numeric", LR="numeric", KB="numeric",
%  chr="character", rpCall="call", pdLabel="character"))
%setMethod("show", "rpSeg", function(object) {
% cat("rpart segmentation for sample", object@samp, "phenoLabel", 
%    object@pdLabel, "\n")
% cat("range of clone locations on chr", object@chr, "\n")
% print(range(object@KB))
%})
%setMethod("plot", "rpSeg", function(x, y, ...) {
% rng = range(x@KB)
% X = seq(rng[1], rng[2], 500) 
% Y = predict( x@obj, newdata=data.frame(KB=X) )
% plot( x@KB, x@LR, xlab=paste("kB on chr", x@chr), ylab="log ratio",
%   main=paste("sample", x@samp, ";", x@pdLabel))
% points(X, Y, pch="-", cex=2)
%})
%treeSeg = function(es, samp, chr="17", pdv="geneCluster", ...) {
% LR = logRatios(es)[,samp]
% pdl = as.character(es[[pdv]][samp])
% KB = kb(es)
% ob = rpart(LR~KB, ...)
% new("rpSeg", obj=ob, samp=samp, LR=LR, KB=KB,
%   chr=chr, rpCall=ob$call, pdLabel=pdl)
%}
%@
%
%Now test it out:
%<<lkts1>>=
%ts1 = treeSeg(nc17, 1)
%ts1
%@
%<<ss,fig=TRUE>>=
%tsL = list()
%for (i in 1:6) tsL[[i]] = treeSeg(nc17, i)
%par(mfrow=c(3,2))
%for (i in 1:6) plot(tsL[[i]])
%par(mfrow=c(1,1))
%@
% 
%\subsection{Open problems in integrative analysis}
%
%\addtocounter{mlq}{1}
%\bi
%\item \textit{Question \MLQ.}  Add infrastructure to extract
%estimated mean log ratio at selected chromosomal offsets for
%all samples in a CGHset.
%These extracted quantities 
%can be regarded as new predictive features, and
%will need names.  If possible,
%write the infrastructure so that queries can be couched in
%terms of gene symbols and extents.
%\ei
%
%\addtocounter{mlq}{1}
%\bi
%\item \textit{Question \MLQ.}  Is predictability of breast
%cancer phenotype enhanced
%when use is made of genomic aberration data in addition
%to expression data?
%\ei
%
\end{document}

