RJaCGH (version 1.1.1)

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

Description

A print method for pMCR objects

Usage

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

Arguments

x

An object of class pMCR.RJaCGH, pMCR.RJaCGH.Chrom, pMCR.RJaCGH.genome, pMCR.RJaCGH.array, pMCR.RJaCGH.array.Chrom or pMCR.RJaCGH.array.genome.

...

Additional arguments passed to 'print'. Currently ignored.

Value

• A matrix 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

Oscar M. Rueda and Ramon Diaz Uriarte. A flexible, accurate and extensible statistical method for detecting genomic copy-number changes. http://biostats.bepress.com/cobra/ps/art9/ {http://biostats.bepress.com/cobra/ps/art9/}.

See Also

RJaCGH, states, model.averaging, pMCR

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)
pMCR(fit.genome, p=0.8, alteration="Gain")
pMCR(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)
pMCR(fit.array.genome, p=0.4, alteration="Gain")
pMCR(fit.array.genome, p=0.4, alteration="Loss")