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        <h1 class="documentFirstHeading">ML-lab.R</h1>
        
        
    
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        <div class="plain">
            <p>###################################################
### chunk number 1: loadlib
###################################################
library("Biobase")
library("ALL")</p>
<p>data(ALL, package="ALL")</p>
<p>ALLBs = ALL[,grep("^B", as.character(ALL$BT))]</p>
<p>ALLBCRNEG = ALLBs[, ALLBs$mol == "BCR/ABL" | ALLBs$mol =="NEG"]</p>
<p>ALLBCRNEG$mol.biol = factor(ALLBCRNEG$mol.biol)</p>
<p>numBN = length(ALLBCRNEG$mol.biol)</p>
<p>ALLBCRALL1 = ALLBs[, ALLBs$mol == "BCR/ABL" | ALLBs$mol == "ALL1/AF4"]</p>
<p>ALLBCRALL1$mol.biol = factor(ALLBCRALL1$mol.biol)
numBA = length(ALLBCRALL1$mol.biol)</p>
<p>###################################################
### chunk number 2: IQRfilter
###################################################
lowQ = rowQ(ALLBCRNEG, floor(0.25 <em> numBN))
upQ = rowQ(ALLBCRNEG, ceiling(0.75 </em> numBN))
iqrs = upQ - lowQ
##you can also do:
##iqrs = esApply(ALLBCRNEG, 1, IQR)</p>
<p>giqr = iqrs > quantile(iqrs, probs=.75)
sum(giqr)</p>
<p>BNsub = ALLBCRNEG<a href="ML-lab.R#refgiqr,">[giqr,]</a></p>
<p>###################################################
### chunk number 3: distEx
###################################################
dSub <- BNsub<a href="#ref1:60,">[1:60,]</a>
eucD <- dist(t(exprs(dSub)))
eucD@Size
eucM <- as.matrix(eucD)</p>
<p>###################################################
### chunk number 4: distHM
###################################################
library("RColorBrewer")
hmcol <- colorRampPalette(brewer.pal(10, "RdBu"))(256)
heatmap(eucM, sym=TRUE, col=hmcol,
    distfun=function(x) as.dist(x))</p>
<p>###################################################
### chunk number 5: helperfuns
###################################################
closestN = function(distM, label) {
   loc = match(label, row.names(distM))
   names(which.min(distM<a href="ML-lab.R#reflabel,-loc">[label,-loc]</a>))
}</p>
<p>closestN(eucM, "03002")</p>
<p>###################################################
### chunk number 6: rowttests
###################################################
library("genefilter")
tt1 = rowttests(BNsub, "mol.biol")
numToSel <- 50</p>
<p>###################################################
### chunk number 7: topNumToSel
###################################################
tt1ord = order(abs(tt1$statistic), decreasing=TRUE)</p>
<p>top50 = tt1ord<a href="ML-lab.R#ref1:numToSel">[1:numToSel]</a></p>
<p>BNsub1 = BNsub<a href="ML-lab.R#reftop50,">[top50,]</a></p>
<p>###################################################
### chunk number 8: rowIQRsFun
###################################################
rowIQRs = function(eSet) {
  if (is(eSet, "exprSet"))
    numSamp = length(sampleNames(eSet))
  else
    numSamp = ncol(eSet)
  lowQ = rowQ(eSet, floor(0.25 <em> numSamp))
  upQ = rowQ(eSet, ceiling(0.75 </em> numSamp))
  upQ - lowQ
}</p>
<p>###################################################
### chunk number 9: standardize
###################################################
standardize = function(x) (x - rowMedians(x)) / rowIQRs(x)</p>
<p>exprs(BNsub1) = standardize(exprs(BNsub1))</p>
<p>###################################################
### chunk number 10: heatmap
###################################################
library("RColorBrewer")
hmcol <- colorRampPalette(brewer.pal(10, "RdBu"))(256)
spcol <- ifelse(BNsub1$mol.biol=="BCR/ABL", "goldenrod", "skyblue")
heatmap(exprs(BNsub1), col=hmcol, ColSideColors=spcol)</p>
<p>###################################################
### chunk number 11: knn1
###################################################</p>
<p>library("class")</p>
<p>a1 = knn.cv(t(exprs(BNsub1)), BNsub1$mol.biol)</p>
<p>ctab1 = table(a1, BNsub1$mol.biol)</p>
<p>errrate = (ctab1["BCR/ABL", "NEG"] + ctab1["NEG", "BCR/ABL"])/sum(ctab1)</p>
<p>###################################################
### chunk number 12: selectk
###################################################
alist = NULL
for(i in 1:4)
  alist[<a href="ML-lab.R#refi">[i]</a>] = knn.cv(t(exprs(BNsub1)), BNsub1$mol.biol, k=i)</p>
<p>sapply(alist, function(x) { 
    ct1 = table(x, BNsub1$mol.biol)
    (ct1["BCR/ABL", "NEG"] + ct1["NEG", "BCR/ABL"]) / sum(ct1)
})</p>
<p>###################################################
### chunk number 13: MLInterfaces
###################################################
library("MLInterfaces") </p>
<p>###################################################
### chunk number 14: knn
###################################################
knnResult <- MLearn("mol.biol", BNsub1, "knn", 1:50)
knnResult</p>
<p>###################################################
### chunk number 15: knn-confusion
###################################################
confuMat(knnResult)</p>
<p>###################################################
### chunk number 16: xval
###################################################
knnXval <- xval(BNsub1, "mol.biol", knnB, xvalMethod="LOO")</p>
<p>###################################################
### chunk number 17: xval-table
###################################################
table(given=BNsub1$mol.biol,predicted=knnXval)</p>
<p>###################################################
### chunk number 18: xval-feature-selection-function
###################################################
t.fun<-function(data, fac) {
    (abs(rowttests(data,data[<a href="#reffac">[fac]</a>], tstatOnly=FALSE)$statistic))
}</p>
<p>###################################################
### chunk number 19: realXval
###################################################
BNx = BNsub
exprs(BNx) = standardize(exprs(BNx))</p>
<p>lk3f <- xval(BNx, "mol.biol", knnB, xvalMethod="LOO", 0:0,
             fsFun=t.fun, fsNum=50)
table(given=BNx$mol.biol, predicted=lk3f$out)</p>
<p>###################################################
### chunk number 20: 
###################################################
sessionInfo()</p>

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