segmentByNonPairedPSCBS {PSCBS} | R Documentation |
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 |
betaT |
A |
... |
Additional arguments passed to |
flavor |
A |
tauA , tauB |
Lower and upper thresholds ( |
verbose |
See |
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. NA
s or NaN
s.
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)