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RJaCGH (version 1.2.5)

print.pREC_A.RJaCGH: Method for printing probabilistic minimal common region.

Description

A print method for pREC_A objects

Usage

## S3 method for class 'pREC_A.RJaCGH':
print(x,...)
## S3 method for class 'pREC_A.RJaCGH.Chrom':
print(x,...)
## S3 method for class 'pREC_A.RJaCGH.genome':
print(x,...)
## S3 method for class 'pREC_A.RJaCGH.array':
print(x,...)
## S3 method for class 'pREC_A.RJaCGH.array.Chrom':
print(x,...)
## S3 method for class 'pREC_A.RJaCGH.array.genome':
print(x,...)

Arguments

x
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.
...
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.

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, pREC_A

Examples

Run this code
## 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")

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