RJaCGH (version 2.0.4)

relabelStates: Relabelling of hidden states to biological states of alteration.

Description

For every model, each hidden state is assigned to a state of copy number alteration ('normal', 'loss1', 'loss2', 'gain1', 'gain2'...)

Usage

relabelStates(obj, normal.reference = 0, window = NULL, singleState = FALSE,
array=NULL, Chrom=NULL)
"relabelStates"(obj, normal.reference = 0, window = NULL, singleState = FALSE,
array=NULL, Chrom=NULL)

Arguments

obj

An object returned from RJaCGH() of class 'RJaCGH', 'RJaCGH.Chrom', 'RJaCGH.Genome'.

normal.reference

The value considered as the mean of the normal state. See details. By default is 0.

window

Multiplier of the standard deviation of the data to dtermine the width of the normal state. See details. Default (window = NULL) is 1.

singleState

If TRUE, each state is assigned a probability of 1 of being Gained or Lost or Normal, and 0 of being anyting else. This mimics the behavior of previous versions of RJaCGH. See details.

array

Vector of arrays to be relabeled. If NULL, all of them.

Chrom

Vector of chromosome to be relabeled (only if a different model has been fitted to each of them). If NULL, all of them are labeled.

Value

An object of the same class as obj with hidden states relabelled.

Details

A relabelling of hidden states is performed to match biological states. We first define an upper and lower limit as: normal.reference +/- window. Next, we compute as the probability that a given state is gained the area beyond the upper limit, and the area up to the lower limit as the probability that a given state is lost. The areas are obtained from a Normal with posterior estimates of mean and standard deviation for each hidden state. The probability of being in a normal state is defined as 1 - prob.gained - prob.loss. Labels are assigned based on the state with highest probability. If we set singleState = TRUE, all the probability is assigned to a single condition, the one with largest 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

Examples

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.chrom <- RJaCGH(y=y, Pos=Pos, Chrom=Chrom, model="Chrom",
                    burnin=10, TOT=1000, k.max = 4,
                    jump.parameters=jp)
plot(fit.chrom)
fit.chrom.2 <- relabelStates(fit.chrom, normal.reference=3)
plot(fit.chrom.2)