rePreProcSample: Re-(pre)-process a sample

In mskcc/facets: Cellular Fraction and Copy Numbers from Tumor Sequencing

Description

Re-process jointseg data frame from procSample output to generate a segmentation tree for further analysis.

Usage

  rePreProcSample(jseg, cval=25, deltaCN=0, gbuild=c("hg19", "hg38",
       "hg18", "mm9", "mm10"), hetscale=TRUE, unmatched=FALSE)

Arguments

jseg	data frame with 8 required columns: chrom, maploc, rCountT, vafT, rCountN, vafN, het and keep. Rest are ignored.
cval	critical value for segmentation
deltaCN	minimum detectable difference in CN from diploid state
gbuild	genome build used for the alignment of the genome. Default value is human genome build hg19. Other possibilities are hg38 & hg18 for human and mm9 & mm10 for mouse. Chromosomes used for analysis are 1-22, X for humans and 1-19 for mouse. Option udef in preProcSample not implemented.
hetscale	logical variable to indicate if logOR should get more weight in the test statistics for segmentation and clustering. Usually only 10% of snps are hets and hetscale gives the logOR contribution to T-square as 0.25/proportion of hets.
unmatched	indicator of whether the normal sample is unmatched. When this is TRUE hets are called using tumor reads only and logOR calculations are different. Use het.thresh = 0.1 or lower when TRUE.

Details

The output from procSample can be processed to generate the R object needed for further analysis. This function will create what is necessary for procSample can be applied. Usage is xx <- rePreProcSample(zout$jointseg[,1:8]) oo <- procSample(xx, ...)

Value

A list consisting of three elements:

pmat	Read counts and other elements of all the loci
seg.tree	a list of matrices one for each chromosome. the matrix gives the tree structure of the splits. each row corresponds to a segment with the parent row as the first element the start-1 and end index of each segment and the maximal T^2 statistic. the first row is the whole chromosome and its parent row is by definition 0.
jointseg	The data that were segmented. Only the loci that were sampled within a snp.nbhd are present. segment results given.
hscl	scaling factor for logOR data.

mskcc/facets documentation built on Oct. 15, 2021, 3:12 p.m.