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<h1 class="documentFirstHeading">GSEA.R</h1>
<div class="documentDescription"></div>
<div class="plain">
<p>###################################################
### chunk number 1: Setup
###################################################
library("Biobase")
library("annotate")
library("Category")
library("hgu95av2")
library("genefilter")</p>
<p>###################################################
### chunk number 2: Example-subset
###################################################
library("ALL")
data(ALL)
Bcell <- grep("^B", as.character(ALL$BT))
bcrAblOrNegIdx <- which(as.character(ALL$mol.biol) %in% c("NEG", "BCR/ABL"))
eset <- ALL[, intersect(Bcell, bcrAblOrNegIdx)]
eset$mol.biol <- factor(eset$mol.biol)
numBN <- length(eset$mol.biol)</p>
<p>###################################################
### chunk number 3: noEntrez
###################################################
entrezIds <- mget(geneNames(eset), envir=hgu95av2LOCUSID)
haveEntrezId <- names(entrezIds)[sapply(entrezIds, function(x) !is.na(x))]
numNoEntrezId <- length(geneNames(eset)) - length(haveEntrezId)
eset <- eset[haveEntrezId, ]</p>
<p>###################################################
### chunk number 4: simplefiltering
###################################################
## Non-specific filtering based on IQR
lowQ = rowQ(eset, floor(0.25 <em> numBN))
upQ = rowQ(eset, ceiling(0.75 </em> numBN))
iqrs = upQ - lowQ</p>
<p>selected <- iqrs > 0.5</p>
<p>nsFiltered <- eset[selected, ]</p>
<p>###################################################
### chunk number 5: reduceto1to1
###################################################
## Reduce to unique probe <--> gene mapping by keeping largest IQR
## This gives us "unique genes" in the non-specific filtered gene
## set which simplifies further calculations.
nsFilteredIqr <- iqrs<a href="#refselected">[selected]</a>
uniqGenes <- findLargest(geneNames(nsFiltered), nsFilteredIqr, "hgu95av2")
nsFiltered <- nsFiltered[uniqGenes, ]</p>
<p>## basic stats on our non-specific filter result
numSelected <- length(geneNames(nsFiltered))
numBcrAbl <- sum(nsFiltered$mol.biol == "BCR/ABL")
numNeg <- sum(nsFiltered$mol.biol == "NEG")</p>
<p>###################################################
### chunk number 6: noKEGG
###################################################
## Remove genes with no PATH mapping
havePATH <- sapply(mget(geneNames(nsFiltered), hgu95av2PATH),
function(x) if (length(x) == 1 && is.na(x)) FALSE else TRUE)
numNoPATH<- sum(!havePATH)
nsF <- nsFiltered[havePATH, ]</p>
<p>###################################################
### chunk number 7: compAmat
###################################################<p> Am = PWAmat("hgu95av2")
egN = unlist(mget(geneNames(nsF), hgu95av2LOCUSID))</p>
<p> sub1 = match(egN, row.names(Am))</p>
<p> Am = Am<a href="GSEA.R#refsub1,">[sub1,]</a>
dim(Am)
table(colSums(Am))</p>
</p>
<p>###################################################
### chunk number 8: compttests
###################################################</p>
<p>rtt = rowttests(nsF, "mol.biol")
rttStat = rtt$statistic</p>
<p>###################################################
### chunk number 9: reducetoInt
###################################################
Amat = t(Am)
rs = rowSums(Amat)
Amat2 = Amat[rs>10,]
rs2 = rs[rs>10]
nCats = length(rs2)</p>
<p>###################################################
### chunk number 10: pctests
###################################################</p>
<p>tA = as.vector(Amat2 %*% rttStat)
tAadj = tA/sqrt(rs2)</p>
<p>names(tA) = names(tAadj) = row.names(Amat2)</p>
<p>###################################################
### chunk number 11: qqplot
###################################################
qqnorm(tAadj)</p>
<p>###################################################
### chunk number 12: findSmPW
###################################################</p>
<p>smPW = tAadj[tAadj < -5]
pwName = KEGGPATHID2NAME[[names(smPW)]]
pwName</p>
<p>###################################################
### chunk number 13: mnplot
###################################################
KEGGmnplot(names(smPW), nsF, "hgu95av2", nsF$"mol.biol")</p>
<p>###################################################
### chunk number 14: heatmap
###################################################
KEGG2heatmap(names(smPW), nsF, "hgu95av2")</p>
<p>###################################################
### chunk number 15: ttperm
###################################################</p>
<p>NPERM = 100
ttp = ttperm(exprs(nsF), nsF$mol.biol, NPERM)</p>
<p>permDm = do.call("cbind", lapply(ttp$perms, function(x) x$statistic))</p>
<p>permD = Amat2 %*% permDm</p>
<p>pvals = matrix(NA, nr=nCats, ncol=2)
dimnames(pvals) = list(row.names(Amat2), c("Lower", "Upper"))</p>
<p>for(i in 1:nCats) {
pvals<a href="GSEA.R#refi,1">[i,1]</a> = sum(permD<a href="GSEA.R#refi,">[i,]</a> < tA<a href="GSEA.R#refi">[i]</a>)/NPERM
pvals<a href="GSEA.R#refi,2">[i,2]</a> = sum(permD<a href="GSEA.R#refi,">[i,]</a> > tA<a href="GSEA.R#refi">[i]</a>)/NPERM
}</p>
<p>ord1 = order(pvals<a href="GSEA.R#ref,1">[,1]</a>)
lowC = (row.names(pvals)<a href="GSEA.R#reford1">[ord1]</a>)[pvals<a href="GSEA.R#reford1,1">[ord1,1]</a>< 0.05]</p>
<h2>highC = row.names(pvals)[pvals<a href="GSEA.R#ref,2">[,2]</a> < 0.05]</h2>
<p> getPathNames(lowC)</p>
<p> getPathNames(highC)</p>
<p> lnhC = length(highC)</p>
<p>###################################################
### chunk number 16: findAmap
###################################################</p>
<p>AmChr = MAPAmat("hgu95av2", minCount=5)</p>
<p>###################################################
### chunk number 17: sub2ourData
###################################################<p> subC = row.names(AmChr) %in% egN</p>
<p> AmChr = AmChr<a href="GSEA.R#refsubC,">[subC,]</a>
dim(AmChr)
table(colSums(AmChr))</p>
</p>
<p>###################################################
### chunk number 18:
###################################################
sessionInfo()</p>
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<a href="../../../../../News/2009-10-28-Release-2.5"
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title="BioC 2.5 Released">2009-10-26</a>
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BioC 2.5, consisting of 352 packages and designed to work with R 2.10.z, was released today.
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R, the open source platform used by Bioconductor, featured in a series of articles in the New York Times.
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