segmentByNonPairedPSCBS: Segment total copy numbers and allele B fractions using the...

In PSCBS: Analysis of Parent-Specific DNA Copy Numbers

Segment total copy numbers and allele B fractions using the Non-paired PSCBS method

Description

Segment total copy numbers and allele B fractions using the Non-paired PSCBS method [1]. This method does not requires matched normals. This is a low-level segmentation method. It is intended to be applied to one tumor sample at the time.

Usage

## Default S3 method:
segmentByNonPairedPSCBS(CT, betaT, ..., flavor=c("tcn", "tcn&dh", "tcn,dh",
  "sqrt(tcn),dh", "sqrt(tcn)&dh"), tauA=NA, tauB=1 - tauA, verbose=FALSE)

Arguments

CT	A numeric vector of J tumor total copy number (TCN) ratios in [0,+Inf) (due to noise, small negative values are also allowed). The TCN ratios are typically scaled such that copy-neutral diploid loci have a mean of two.
betaT	A numeric vector of J tumor allele B fractions (BAFs) in [0,1] (due to noise, values may be slightly outside as well) or NA for non-polymorphic loci.
...	Additional arguments passed to segmentByPairedPSCBS().
flavor	A character specifying what type of segmentation and calling algorithm to be used.
tauA, tauB	Lower and upper thresholds (tauA < tauB for calling SNPs heterozygous based on the tumor allele B fractions (betaT). If NA, then they are estimates from data.
verbose	See Verbose.

Details

Internally segmentByPairedPSCBS() is used for segmentation. This segmentation method does not support weights.

Value

Returns the segmentation results as a NonPairedPSCBS object.

Reproducibility

The "DNAcopy::segment" implementation of CBS uses approximation through random sampling for some estimates. Because of this, repeated calls using the same signals may result in slightly different results, unless the random seed is set/fixed.

Whole-genome segmentation is preferred

Although it is possible to segment each chromosome independently using Paired PSCBS, we strongly recommend to segment whole-genome (TCN,BAF) data at once. The reason for this is that downstream CN-state calling methods, such as the AB and the LOH callers, performs much better on whole-genome data. In fact, they may fail to provide valid calls if done chromosome by chromosome.

Missing and non-finite values

The total copy number signals as well as any optional positions must not contain missing values, i.e. NAs or NaNs. If there are any, an informative error is thrown. Allele B fractions may contain missing values, because such are interpreted as representing non-polymorphic loci.

None of the input signals may have infinite values, i.e. -Inf or +Inf. If so, an informative error is thrown.

Non-Paired PSCBS with known genotypes

If allele B fractions for the matched normal (betaN) are not available, but genotypes (muN) are, then it is possible to run Paired PSCBS. See segmentByPairedPSCBS() for details.

Author(s)

Henrik Bengtsson

References

[1] A.B. Olshen, H. Bengtsson, P. Neuvial, P.T. Spellman, R.A. Olshen, V.E. Seshan, Parent-specific copy number in paired tumor-normal studies using circular binary segmentation, Bioinformatics, 2011
[2] H. Bengtsson, P. Neuvial and T.P. Speed, TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays, BMC Bioinformatics, 2010

See Also

To segment paired tumor-normal total copy numbers and allele B fractions, see segmentByPairedPSCBS().

To segment total copy numbers, or any other unimodal signals, see segmentByCBS().

Examples

verbose <- R.utils::Arguments$getVerbose(-10*interactive(), timestamp=TRUE)

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


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

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

str(dataS)

R.oo::attachLocally(dataS)

# Non-Paired PSCBS segmentation
fit <- segmentByNonPairedPSCBS(CT, betaT=betaT,
                            chromosome=chromosome, x=x,
                            seed=0xBEEF, verbose=verbose)
print(fit)


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


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Calling segments in allelic balance (AB)
# NOTE: Ideally, this should be done on whole-genome data
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# 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=verbose)
print(deltaAB)

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

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

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

PSCBS documentation built on May 29, 2024, 5:59 a.m.