print.pREC_A: Method for printing probabilistic common region.

In RJaCGH: Reversible Jump MCMC for the Analysis of CGH Arrays

Description

A print method for pREC_A objects

Usage

## S3 method for class 'pREC_A'
print(x,...)
## S3 method for class 'pREC_A.none'
print(x,...)
## S3 method for class 'pREC_A.Chromosomes'
print(x,...)

Arguments

x	An object of class pREC_A, pREC_A.Chromosomes, pREC_A.RJaCGH.none.
...	Additional arguments passed to print. Currently ignored.

Value

A data.frame is printed with as many rows as regions found and with columns containing chromosome where the region is, position of start and end of the region, number of genes in it and joint probability.

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, modelAveraging, 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=100, 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=100, 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")

RJaCGH documentation built on May 2, 2019, 3:34 p.m.