correctCopyNumber: Allelic Copy Number correction for cellularity
In CRImage: CRImage a package to classify cells and calculate tumour cellularity

Description Usage Arguments Details Value Note Author(s) References Examples

This function segments copy number and corrects log-ratios (LRR) and beta allele frequencies (BAF) values for cellularity.

1	correctCopyNumber(arr="Sample1", chr=NULL, p=NULL, z=NULL, min.value=-5)

`arr`	Name of the array.
`chr`	Chromosome to run. If `NULL`, all chromosomes are run.
`p`	Percentage of tumoural cells

`z`	Copy Number Data. Must be a dataframe with the following columns: Name (id of the probe), Chr (chromosome), Pos (position), LRR (log ratios) and BAF (beta allele frequencies).
`min.value`	Value assigned to the probes that have 0 copies after correction.

The data.frame z must contain only SNP probes, that is probes with both LRR and BAF values. It is recommended that all replicated probes are merged so the positions are unique. This function calls DNAcopy to segment the LRR and then correct the segmented profiles for normal contamination according to the method described in the reference below (see for details).

A list with 2 components:

y

a data.frame with as many rows as probes containing the following variables: Chrom (chromosome), Pos (position), Orig.LRR (LRR before correction) Orig.BAF (BAF before correction), Corr.LRR (LRR after cellularity correction) and Corr.BAF (BAF after correction)

seg

a data.frame with the segmented data. Contains the following columns: ID (name of the array), chrom (chromosome), loc.start (start of the region), loc.end (end of the region), num.mark (number of probes in the region), seg.mean (LRR of the region), BAF (BAF of the regions), num.BAF (number of SNP probes in the region), Sa (estimated absolute copy number for the first allele), Sb (estimated absolute copy number for the first allele), LRR.tum (corrected LRR for the region), BAF.tum (corrected BAF for the region).

Includes an adaptation of aCGH mergeLevels function to fix a problem with ansari.test.

Oscar M. Rueda, rueda.om@gmail.com

Yuan, Y et al. Quantitative image analysis of cellular heterogeneity in primary breast tumors enriches genomic assays. In prep.

LRR <- c(rnorm(100, 0, 1), rnorm(10, -2, 1), rnorm(20, 3, 1),
       rnorm(100,0, 1)) 
BAF <- c(rnorm(100, 0.5, 0.1), rnorm(5, 0.2, 0.01), rnorm(5, 0.8, 0.01), rnorm(10, 0.25, 0.1), rnorm(10, 0.75, 0.1),
       rnorm(100,0.5, 0.1)) 

Pos <- sample(x=1:500, size=230, replace=TRUE)
Pos <- cumsum(Pos)
Chrom <- rep(1, length(LRR))
z <- data.frame(Name=1:length(LRR), Chrom=Chrom, Pos=Pos, LRR=LRR, BAF=BAF)
res <- correctCopyNumber(arr="Sample1", chr=1, p=0.75, z=z)