Nothing
library("aroma.affymetrix")
log <- Arguments$getVerbose(-4, timestamp=TRUE)
dataSet <- "HapMap,CEU,testset"
chipTypes <- c("Mapping50K_Hind240", "Mapping50K_Xba240")
# Expected sample names
sampleNames <- c("NA06985", "NA06991", "NA06993",
"NA06994", "NA07000", "NA07019")
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Tests for setting up CEL sets and locating the CDF file
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
csRList <- list()
for (chipType in chipTypes) {
cs <- AffymetrixCelSet$byName(dataSet, chipType=chipType)
print(cs)
stopifnot(identical(getNames(cs), sampleNames))
csRList[[chipType]] <- cs
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Allelic cross-talk calibration tests
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
csList <- csRList
csCList <- list()
for (chipType in names(csList)) {
cs <- csList[[chipType]]
acc <- AllelicCrosstalkCalibration(cs)
print(acc)
csC <- process(acc, verbose=log)
print(csC)
stopifnot(identical(getNames(csC), getNames(cs)))
csCList[[chipType]] <- csC
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Probe-level modelling test (for CN analysis)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
csList <- csCList
cesCnList <- list()
for (chipType in names(csList)) {
cs <- csList[[chipType]]
plm <- RmaCnPlm(cs, mergeStrands=TRUE, combineAlleles=TRUE, shift=300)
print(plm)
fit(plm, verbose=log)
ces <- getChipEffectSet(plm)
print(ces)
stopifnot(identical(getNames(ces), getNames(cs)))
cesCnList[[chipType]] <- ces
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Fragment-length normalization test
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cesNList <- list()
for (chipType in names(csList)) {
ces <- cesCnList[[chipType]]
fln <- FragmentLengthNormalization(ces)
print(fln)
cesN <- process(fln, verbose=log)
print(cesN)
stopifnot(identical(getNames(cesN), getNames(ces)))
cesNList[[chipType]] <- cesN
}
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Robust estimate of standard deviation of raw CNs
# (The default is to use a first-order difference variance estimator)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Estimate it for some autosomal chromosomes and ChrX
cns <- CbsModel(cesNList)
res <- estimateSds(cns, chromosomes=c(1:6, 23), verbose=log)
chrX <- which(rownames(res) == "23")
layout(matrix(1:2, nrow=2))
par(mar=c(4,4,0.5,2)+0.1)
xlim <- c(1,(ncol(res)+2))
ylim <- c(0, 1.05*max(res, na.rm=TRUE))
ylab <- expression(hat(sigma)==s[Delta](log2(theta/theta[R])))
cols <- seq_len(nrow(res))
ltys <- rep(1, times=nrow(res))
cols[chrX] <- "blue"
ltys[chrX] <- 4
plot(NA, xlim=xlim, ylim=ylim, xlab="", ylab=ylab)
for (kk in seq_len(nrow(res))) {
chromosome <- rownames(res)[kk]
points(res[kk,], col=cols[kk], pch=19)
lines(res[kk,], col=cols[kk], lty=ltys[kk], lwd=2)
}
legend("topright", col=cols, lty=ltys, pch=19, lwd=2,
legend=sprintf("Chr%s", rownames(res)), bty="n")
df <- as.data.frame(res[-chrX,,drop=FALSE])
colnames(df) <- seq_len(nrow(df))
boxplot(df, ylim=ylim, ylab=ylab, xlab="Array")
points(res[chrX,], col="blue", pch=19, cex=1.5)
legend("bottomright", col=c("black", cols[chrX]), pch=19, lwd=2,
legend=c("Autosomal", "ChrX"), horiz=TRUE, bty="n")
devDone()
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