inst/testScripts/complete/dataSets/GSE12702/31.doASCRMAv2,PairedPSCBS,Nsp.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)


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Setup
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dataSet <- "GSE12702"
chipType <- "Mapping250K_Nsp"

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)


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Group samples by name and type
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# 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 (single) tumor-normal pairs
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
dfTList <- lapply(dsList, FUN=function(dsList) { dsList$T[[1]] })
dsT <- newInstance(dsList[[1]]$T, dfTList)
dfNList <- lapply(dsList, FUN=function(dsList) { dsList$N[[1]] })
dsN <- newInstance(dsList[[1]]$T, dfNList)


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# 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)
scs <- process(caller, verbose=verbose)
print(scs)


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

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