inst/testScripts/complete/dataSets/GSE13372/31.doASCRMAv2,PairedPSCBS.R

In aroma.affymetrix: Analysis of Large Affymetrix Microarray Data Sets

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# AS-CRMAv2 and Paired PSCBS
##########################################################################
future::plan("multisession")
library("aroma.affymetrix")
library("aroma.cn");  # PairedPscbsModel
verbose <- Arguments$getVerbose(-8, timestamp=TRUE)


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# Setup
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dataSet <- "GSE13372"
chipType <- "GenomeWideSNP_6,Full"

csR <- AffymetrixCelSet$byName(dataSet, chipType=chipType)


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# AS-CRMAv2
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dsNList <- doASCRMAv2(csR, verbose=verbose)
print(dsNList)

dsN <- exportAromaUnitPscnBinarySet(dsNList)
print(dsN)


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# Group samples by name and type
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# AD HOC: For now, just hardwire the path.
path <- file.path("testScripts/complete/dataSets", dataSet)
db <- TabularTextFile(sprintf("%s,samples.txt", dataSet), path=path)
setColumnNamesTranslator(db, function(names, ...) {
  names <- gsub("id", "fixed", names)
  names <- gsub("fullname", "replacement", names)
  names
})
df <- readDataFrame(db, colClasses=c("*"="character"))
setFullNamesTranslator(dsN, df)

# Identify unique sample names
sampleNames <- unique(getNames(dsN))

dsList <- lapply(sampleNames, FUN=function(sampleName) {
  ds <- dsN[sampleName]
  lapply(c(T="T", N="N"), FUN=function(type) {
    ds[sapply(ds, hasTag, type)]
  })
})
names(dsList) <- sampleNames


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Extract tumor-normal pairs
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
R <- 4; # Number of replicates
dsList2 <- list()
for (key in names(dsList)) {
  dsListKK <- dsList[[key]]
  dsT <- dsListKK$T
  dsN <- dsListKK$N

  # (a) Replicated tumor vs same normal
  dsTa <- dsT[1:min(length(dsT),R)]
  dsNa <- dsN[rep(1L, times=length(dsTa))]

  # (b) Same tumor vs replicated normals
  dsNb <- dsN[1:min(length(dsN),R)]
  dsTb <- dsT[rep(1L, times=length(dsNb))]

  # (c) Merge
  dsT <- append(dsTa, dsTb)
  dsN <- append(dsNa, dsNb)

  # (d) Drop duplicated tumor-normal pairs
  nT <- getFullNames(dsT)
  nN <- getFullNames(dsN)
  nP <- paste(nT, nN, sep="_vs_")
  dups <- duplicated(nP)
  dsT <- dsT[!dups]
  dsN <- dsN[!dups]
  stopifnot(length(dsT) == length(dsN))

  dsList2[[key]] <- list(T=dsT, N=dsN)
} # for kk ...)

dsT <- Reduce(append, lapply(dsList2, FUN=function(x) x$T))
dsN <- Reduce(append, lapply(dsList2, FUN=function(x) x$N))


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# Segment tumor-normal pairs
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sm <- PairedPscbsModel(dsT=dsT, dsN=dsN, gapMinLength=2e6)
print(sm)

res <- fit(sm, verbose=verbose)
print(res)

sms <- getOutputDataSet(sm)
print(sms)


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Call segments to be in ROH, AB and LOH.
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
## caller <- PairedPscbsCaller(sms)
## print(caller)
## sms <- process(caller, verbose=verbose)
## print(sms)


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# Generate report (just to check)
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setOption("PSCBS::reports/pscnSegmentationTransitions", TRUE)

# Generate reports for tumor-normal pairs
for (ii in 1:min(length(sms),5)) {
  ff <- sms[[ii]]
  fit <- loadObject(df)
  sampleName <- getFullName(df)
  pathname <- report(fit, sampleName=sampleName, studyName=getFullName(dsT), verbose=verbose)
  print(pathname)
} # for (ii ...)