RJaCGH (version 1.2.5)

pREC_S: Subgroups of arrays that share common alterations

Description

An algorithm to find regions of gain/lost copy number shared by a given proportion of arrays over a probability threshold.

Usage

pREC_S(obj, p, freq.array, alteration = "Gain")
## S3 method for class 'RJaCGH.array':
pREC_S(obj, p, freq.array, alteration = "Gain")

Arguments

obj

An object of class 'RJaCGH.array'.

p

Threshold for the minimum joint probability of the region on every array.

freq.array

Minimum number of arrays that share every region.

alteration

Either 'Gain' or 'Loss'.

Value

• An object of class pREC_S.RJaCGH.array, pREC_S.RJaCGH.array.Chrom or pREC_S.RJaCGH.array.genome, as corresponding. They are lists with a sublist for every region encountered and elements:
• start
  Start position of the region.
• indexStart
  index position of the start of the region.
• indexEnd
  index position of the end of the region.
• end
  End position of the region.
• members
  Arrays that share the region.
• If there are chromosome information, this information will be enclosed in a list for each chromosome.

Details

This algorithm, as pREC_A computes probabilistic common regions but instead of finding regions that have a joint probability of alteration over all arrays, pREC_S searches for regions that have a probability of alteration higher than a threshold in at least a minimum number of arrays. So, pREC_S finds subsets of arrays that share subsets of alterations. Please note that if the method returns several sets or regions, the probability of alteration of all of them doesn't have to be over the probability threshold; in other words p is computed for every region, not for all the sequence of regions.

References

Rueda OM, Diaz-Uriarte R. Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH. PLoS Comput Biol. 2007;3(6):e122

See Also

RJaCGH, states, model.averaging, print.pREC_S.RJaCGH.array plot.pREC_S.RJaCGH.array getSequence prob.seq pREC_A

Examples

## MCR for a single array:
y <- c(rnorm(100, 0, 1), rnorm(10, -3, 1), rnorm(20, 3, 1),
       rnorm(100,0, 1)) 
Pos <- sample(x=1:500, size=230, replace=TRUE)
Pos <- cumsum(Pos)
Chrom <- rep(1:23, rep(10, 23))

jp <- list(sigma.tau.mu=rep(0.05, 4), sigma.tau.sigma.2=rep(0.03, 4),
           sigma.tau.beta=rep(0.07, 4), tau.split.mu=0.1, tau.split.beta=0.1)

fit.genome <- RJaCGH(y=y, Pos=Pos, Chrom=Chrom, model="genome",
burnin=1000, TOT=1000, jump.parameters=jp, k.max = 4)

z <- c(rnorm(110, 0, 1), rnorm(20, 3, 1),
       rnorm(100,0, 1)) 
zz <- c(rnorm(90, 0, 1), rnorm(40, 3, 1),
       rnorm(100,0, 1)) 

fit.array.genome <- RJaCGH(y=cbind(y,z,zz),
Pos=Pos, Chrom=Chrom, model="genome",
burnin=1000, TOT=1000, jump.parameters=jp, k.max = 4)
pREC_S(fit.array.genome, p=0.4, freq.array=2,
alteration="Gain")
pREC_S(fit.array.genome, p=0.4, freq.array=2, alteration="Loss")