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Lab3 (RNW)
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<h1 class="documentFirstHeading">Lab3 (RNW)</h1>
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%
<br />% NOTE -- ONLY EDIT THE .Rnw FILE!!! The .tex file is
<br />% likely to be overwritten.
<br />%
<br />% \VignetteIndexEntry{Seattle Lab 3}
<br />%\VignetteDepends{Biobase. golubEsets, ellipse,lattice, mva, MASS }
<br />%\VignetteKeywords{Microarray}
<br />\documentclass[12pt]{article}
<br />
<br />\usepackage{amsmath,pstricks}
<br />\usepackage[authoryear,round]{natbib}
<br />\usepackage{hyperref}
<br />
<br />
<br />\textwidth=6.2in
<br />\textheight=8.5in
<br />%\parskip=.3cm
<br />\oddsidemargin=.1in
<br />\evensidemargin=.1in
<br />\headheight=-.3in
<br />
<br />\newcommand{\scscst}{\scriptscriptstyle}
<br />\newcommand{\scst}{\scriptstyle}
<br />
<br />\bibliographystyle{plainnat}
<br />
<br />\begin{document}
<br />
<br />\section*{Clustering Using R and Bioconductor}
<br />
<br />We continue our extended example involving the data set from \citet{Golub99}.
<br />In this lab we will consider
<br />computing a number of different distances and
<br />clustering techniques applied to these data.
<br />
<br />We will look at distance calculations, followed by multidimensional scaling
<br />and then examine a number of different clustering methods.
<br />
<br /><<loadlibs>>=
<br /> library(Seattle)
<br /> library(lattice)
<br />@
<br />
<br />We will set up the data from scratch once again.
<br />
<br /><<setup>>=
<br />X<-exprs(golubTrain)
<br />X[X<100]<-100
<br />X[X>16000]<-16000
<br />
<br />mmfilt <- function(r=5, d=500, na.rm=TRUE) {
<br />function(x) {
<br /> minval <- min(x, na.rm=na.rm)
<br /> maxval <- max(x, na.rm=na.rm)
<br /> (maxval/minval > r) && (maxval-minval > d)
<br /> }
<br />}
<br />
<br />mmfun <- mmfilt()
<br />
<br />ffun <- filterfun(mmfun)
<br />sub <- genefilter(X, ffun )
<br />sum(sub) ## Should get 3051
<br />
<br />X <- X[sub,]
<br />X <- log2(X)
<br />golubTrainSub<-golubTrain[sub,]
<br />golubTrainSub@exprs <- X
<br />
<br />Y <- golubTrainSub$ALL.AML
<br />
<br />Y<-paste(golubTrain$ALL.AML,golubTrain$T.B.cell)
<br />Y<-sub("NA","",Y)
<br />
<br />@
<br />
<br />Now that the data are set up we are ready to start looking at the data.
<br />
<br />\section*{Distances}
<br />
<br />%%FIXME: get the ellipse package
<br />
<br />We first compute the correlation distance between all the genes in
<br />the selected subset from the training set.
<br />
<br />%%FIXME: other distances?
<br />
<br /><<cordist>>=
<br />r<-cor(X)
<br />dimnames(r)<-list(as.vector(Y),as.vector(Y))
<br />d<-1-r
<br />
<br />@
<br />
<br />
<br />We rely on the \textit{ellipse} package for plotting some of these
<br />values.
<br /><<plotcorr>>=
<br />
<br />plotcorr(r,
<br />main="Leukemia data: Correlation matrix for 38 mRNA samples\n All 3,051 genes")
<br />plotcorr(r,numbers=TRUE,
<br />main="Leukemia data: Correlation matrix for 38 mRNA samples\n All 3,051 genes")
<br />levelplot(r,col.region=heat.colors(50),
<br />main="Leukemia data: Correlation matrix for 38 mRNA samples\n All 3,051 genes")
<br />
<br />@
<br />
<br />We can next look at multidimensional scaling using these data.
<br />Multidimensional scaling is a data reduction method that is appropriate for
<br />distance data. It is much like principal components (but it is not the
<br />same -- except for Euclidean distances).
<br />
<br />An interesting question is whether the data in the distance matrix can be
<br />reduced to a smaller dimensional space.
<br />Note, if we are measuring distances between samples on the basis of
<br />$N=3051$ probes then we are essentially looking at points in 3,051 dimensional
<br />space.
<br />
<br /><<mds>>=
<br />library(mva)
<br />
<br />mds<- cmdscale(d, k=2, eig=TRUE)
<br />plot(mds$points, type="n", xlab="", ylab="", main="MDS for ALL AML data, correlation matrix, G=3,051 genes, k=2")
<br />text(mds$points[,1],mds$points[,2],Y, col=codes(factor(Y))+1, cex=0.8)
<br />
<br />mds<- cmdscale(d, k=3, eig=TRUE)
<br />pairs(mds$points, main="MDS for ALL AML data, correlation matrix, G=3,051 genes, k=3", pch=c("B","T","M")[codes(factor(Y))], col = codes(factor(Y))+1)
<br />
<br />
<br />
<br />@
<br />
<br />To assess how many components there are a \textit{scree} plot similar to that
<br />used for principal components can be created.
<br />
<br />This plot of the eigenvalues suggests that much of the information is
<br />contained in the first component. One might consider using either three
<br />or four components as well.
<br />
<br /><<scree>>=
<br />
<br /> mdsScree <- cmdscale(d, k=8, eig=TRUE)
<br />
<br /> plot(mdsScree$eig, pch=18, col="blue")
<br />
<br />@
<br />
<br />
<br />\end{document}
<br />
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