library("PSCBS")


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# Load SNP microarray data
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data <- PSCBS::exampleData("paired.chr01")
str(data)

# Non-paired / tumor-only data
data <- data[,c("chromosome", "x", "CT", "betaT")]
str(data)


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# Paired PSCBS segmentation
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# Drop single-locus outliers
dataS <- dropSegmentationOutliers(data)

# Speed up example by segmenting fewer loci
dataS <- dataS[seq(from=1, to=nrow(data), by=20),]

# Fake a second chromosome
dataT <- dataS
dataT$chromosome <- 2L
dataS <- rbind(dataS, dataT)
rm(dataT)
str(dataS)

# Non-Paired PSCBS segmentation
fit <- segmentByNonPairedPSCBS(dataS, avgDH="median", seed=0xBEEF, verbose=-10)
print(fit)


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# Bootstrap segment level estimates
# (used by the AB caller, which, if skipped here,
#  will do it automatically)
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fit <- bootstrapTCNandDHByRegion(fit, B=100, verbose=-10)
print(fit)


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# Calling segments in allelic balance (AB)
# NOTE: Ideally, this should be done on whole-genome data
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# Explicitly estimate the threshold in DH for calling AB
# (which be done by default by the caller, if skipped here)
deltaAB <- estimateDeltaAB(fit, flavor="qq(DH)", verbose=-10)
print(deltaAB)

fit <- callAB(fit, delta=deltaAB, verbose=-10)
print(fit)


# Even if not explicitly specified, the estimated
# threshold parameter is returned by the caller
stopifnot(fit$params$deltaAB == deltaAB)


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Calling segments in loss-of-heterozygosity (LOH)
# NOTE: Ideally, this should be done on whole-genome data
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Explicitly estimate the threshold in C1 for calling LOH
# (which be done by default by the caller, if skipped here)
deltaLOH <- estimateDeltaLOH(fit, flavor="minC1|nonAB", verbose=-10)
print(deltaLOH)

fit <- callLOH(fit, delta=deltaLOH, verbose=-10)
print(fit)
plotTracks(fit)

# Even if not explicitly specified, the estimated
# threshold parameter is returned by the caller
stopifnot(fit$params$deltaLOH == deltaLOH)