pREC_S {RJaCGH}R Documentation

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'.

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.

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.

Note

This is a preliminary method, so it can be slow. This class supersedes the class pMCR2. Objects created with function pMCR2 in older versions of RJaCGH can be converted into the new one in order to print them, or plot them. Two steps must be performed: 1.-Change the class apropriately; if for example obj has class 'pMCR2.RJaCGH.array' make class(obj) <- 'pREC_S.RJaCGH.array' 2.-If obj has information about chromosomes, run names(obj) <- 1:length(obj)

Author(s)

Oscar M. Rueda and Ramon Diaz Uriarte

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

## Not run: 
## 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")
## End(Not run)

[Package RJaCGH version 1.2.5 Index]