segmentationCBS: CBS segmentation

In lima1/PureCN: Copy number calling and SNV classification using targeted short read sequencing

CBS segmentation

Description

The default segmentation function. This function is called via the fun.segmentation argument of runAbsoluteCN. The arguments are passed via args.segmentation.

Usage

segmentationCBS(
  normal,
  tumor,
  log.ratio,
  seg,
  plot.cnv,
  sampleid,
  weight.flag.pvalue = 0.01,
  alpha = 0.005,
  undo.SD = NULL,
  vcf = NULL,
  tumor.id.in.vcf = 1,
  normal.id.in.vcf = NULL,
  max.segments = NULL,
  min.logr.sdev = 0.15,
  prune.hclust.h = NULL,
  prune.hclust.method = "ward.D",
  chr.hash = NULL,
  additional.cmd.args = "",
  centromeres = NULL
)

Arguments

normal	Coverage data for normal sample.
tumor	Coverage data for tumor sample.
log.ratio	Copy number log-ratios, one for each target in the coverage files.
seg	If segmentation was provided by the user, this data structure will contain this segmentation. Useful for minimal segmentation functions. Otherwise PureCN will re-segment the data. This segmentation function ignores this user provided segmentation.
plot.cnv	Segmentation plots.
sampleid	Sample id, used in output files.
weight.flag.pvalue	Flag values with one-sided p-value smaller than this cutoff.
alpha	Alpha value for CBS, see documentation for the segment function.
undo.SD	undo.SD for CBS, see documentation of the segment function. If NULL, try to find a sensible default.
vcf	Optional CollapsedVCF object with germline allelic ratios.
tumor.id.in.vcf	Id of tumor in case multiple samples are stored in VCF.
normal.id.in.vcf	Id of normal in in VCF. Currently not used.
max.segments	If not NULL, try a higher undo.SD parameter if number of segments exceeds the threshold.
min.logr.sdev	Minimum log-ratio standard deviation used in the model. Useful to make fitting more robust to outliers in very clean data.
prune.hclust.h	Height in the hclust pruning step. Increasing this value will merge segments more aggressively. If NULL, try to find a sensible default.
prune.hclust.method	Cluster method used in the hclust pruning step. See documentation for the hclust function.
chr.hash	Mapping of non-numerical chromsome names to numerical names (e.g. chr1 to 1, chr2 to 2, etc.). If NULL, assume chromsomes are properly ordered.
additional.cmd.args	character(1). Ignored.
centromeres	A GRanges object with centromere positions. Currently not supported in this function.

Value

data.frame containing the segmentation.

Author(s)

Markus Riester

References

Olshen, A. B., Venkatraman, E. S., Lucito, R., Wigler, M. (2004). Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5: 557-572.

Venkatraman, E. S., Olshen, A. B. (2007). A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23: 657-63.

See Also

runAbsoluteCN

Examples

normal.coverage.file <- system.file("extdata", "example_normal_tiny.txt",
    package = "PureCN")
tumor.coverage.file <- system.file("extdata", "example_tumor_tiny.txt",
    package = "PureCN")
vcf.file <- system.file("extdata", "example.vcf.gz",
    package = "PureCN")
interval.file <- system.file("extdata", "example_intervals_tiny.txt",
    package = "PureCN")

# The max.candidate.solutions, max.ploidy and test.purity parameters are set to
# non-default values to speed-up this example.  This is not a good idea for real
# samples.
ret <-runAbsoluteCN(normal.coverage.file = normal.coverage.file,
    tumor.coverage.file = tumor.coverage.file, vcf.file = vcf.file,
    genome = "hg19", sampleid = "Sample1", interval.file = interval.file,
    max.candidate.solutions = 1, max.ploidy = 4,
    test.purity = seq(0.3, 0.7, by = 0.05),
    fun.segmentation = segmentationCBS,
    args.segmentation = list(alpha = 0.001))

lima1/PureCN documentation built on Nov. 22, 2024, 6:07 a.m.