pREC_A {RJaCGH}R Documentation

Probabilistic Common Regions for copy number alteration.

Description

This method compute regions of gain/lost copy number with a joint probability of alteration greater than a given threshold.

Usage

pREC_A(obj, p, alteration = "Gain", array.weights = NULL)
## S3 method for class 'RJaCGH':
pREC_A(obj, p, alteration = "Gain", array.weights = NULL)
## S3 method for class 'RJaCGH.Chrom':
pREC_A(obj, p, alteration = "Gain", array.weights = NULL)
## S3 method for class 'RJaCGH.genome':
pREC_A(obj, p, alteration = "Gain", array.weights
= NULL)
## S3 method for class 'RJaCGH.array':
pREC_A(obj, p, alteration = "Gain", array.weights = NULL)

Arguments

obj An object of class 'RJaCGH', 'RJaCGH.Chrom', 'RJaCGH.genome' or 'RJaCGH.array'.
p Threshold for the minimum joint probability of alteration of the region.
alteration Either 'Gain' or 'Lost'
array.weights When 'obj' contains several arrays, the user can give a weight to each of them according to their reliability or precision.

Details

RJaCGH can compute common regions taking into account the probability of every probe to have an altered copy number. The result is a set of probes whose joint probability (not the product of their marginal probabilities, as returned by states or model.averaging) is at least as p or greater.

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.

pREC_A calls the function getSequence that creates temporal files in the working directory containing the sequence of hidden states for every MCMC sample, so there should be writing permisssions in that directory. Then it calls repeatedly prob.seq to compute the joint probability of sets of probes over the MCMC samples.

Value

An object of class pREC_A.RJaCGH, pREC_A.RJaCGH.Chrom, pREC_A.RJaCGH.genome, pREC_A.RJaCGH.array, pREC_A.RJaCGH.array.Chrom or pREC_A.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.
genes Number of genes in the region.
prob Joint probability of gain/loss of the region.


If there are chromosome information (that is, the object inputed is of class RJaCGH.Chrom, RJaCGH.genome or RJaCGH.array with each array of any of these classes), then 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 pMCR. Objects created with function pMCR in older versions of RJaCGH can be converted into the new one in order to print them. Two steps must be performed: 1.-Change the class apropriately; if for example obj has class 'pMCR.RJaCGH.Chrom' make class(obj) <- 'pREC_A.RJaCGH.Chrom' 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_A getSequence prob.seq pREC_S

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)
pREC_A(fit.genome, p=0.8, alteration="Gain")
pREC_A(fit.genome, p=0.8, alteration="Loss")

##MCR for two arrays:
z <- c(rnorm(110, 0, 1), rnorm(20, 3, 1),
       rnorm(100,0, 1)) 
fit.array.genome <- RJaCGH(y=cbind(y,z), Pos=Pos, Chrom=Chrom, model="genome",
burnin=1000, TOT=1000, jump.parameters=jp, k.max = 4)
pREC_A(fit.array.genome, p=0.4, alteration="Gain")
pREC_A(fit.array.genome, p=0.4, alteration="Loss")
## End(Not run)

[Package RJaCGH version 1.2.5 Index]