Description Usage Arguments Details Author(s) References See Also Examples
This function splits (bivariate) copy number signals into parent-specific (PS) segments using recursive binary segmentation
1 2 |
data |
Data frame containing the following columns:
These data are assumed to be ordered by genome position. |
method |
|
stat |
A vector containing the names or indices of the columns of |
dropOutliers |
If TRUE, outliers are droped by using DNAcopy package |
rankTransform |
If TRUE, data are replaced by their ranks before segmentation |
... |
Further arguments to be passed to |
profile |
Trace time and memory usage ? |
verbose |
A |
Before segmentation, the decrease in heterozygosity
d=2|b-1/2|
defined in Bengtsson et al, 2010 is calculated
from the input data. d
is only defined for heterozygous
SNPs, that is, SNPs for which data$genotype==1/2
. d
may be seen as a "mirrored" version of allelic ratios (b
):
it converts them to a piecewise-constant signals by taking
advantage of the bimodality of b
for heterozygous SNPs.
The rationale for this transformation is that allelic ratios
(b
) are only informative for heterozygous SNPs (see
e.g. Staaf et al, 2008).
Before segmentation, the outliers in the copy number signal are droped according the method explained by Venkatraman, E. S. and Olshen, A. B., 2007.
The resulting data are then segmented using the
jointSeg
function, which combines an initial
segmentation according to argument method
and pruning of
candidate change points by dynamic programming (skipped when the
initial segmentation *is* dynamic programming).
If argument stat
is not provided, then dynamic
programming is run on the two dimensional statistic
"(c,d)"
.
If argument stat
is provided, then dynamic
programming is run on stat
; in this case we implicitly
assume that stat
is a piecewise-constant signal.
Morgane Pierre-Jean and Pierre Neuvial
Bengtsson, H., Neuvial, P., & Speed, T. P. (2010). TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays. BMC bioinformatics, 11(1), 245.
Staaf, J., Lindgren, D., Vallon-Christersson, J., Isaksson, A., Goransson, H., Juliusson, G., ... & Ringn\'er, M. (2008). Segmentation-based detection of allelic imbalance and loss-of-heterozygosity in cancer cells using whole genome SNP arrays. Genome Biol, 9(9), R136.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ## load known real copy number regions
affyDat <- loadCnRegionData(dataSet="GSE29172", tumorFraction=0.5)
## generate a synthetic CN profile
K <- 10
len <- 1e4
sim <- getCopyNumberDataByResampling(len, K, regData=affyDat)
datS <- sim$profile
## run binary segmentation (+ dynamic programming) resRBS <-
resRBS <- PSSeg(data=datS, method="RBS", stat=c("c", "d"), K=2*K, profile=TRUE)
resRBS$prof
getTpFp(resRBS$bestBkp, sim$bkp, tol=5)
plotSeg(datS, breakpoints=list(sim$bkp, resRBS$bestBkp))
|
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