pREC_A {RJaCGH} | R Documentation |
This method compute regions of gain/lost copy number with a joint probability of alteration greater than a given threshold.
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)
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. |
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.
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.
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)
Oscar M. Rueda and Ramon Diaz Uriarte
Rueda OM, Diaz-Uriarte R. Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH. PLoS Comput Biol. 2007;3(6):e122
RJaCGH
,
states
, model.averaging
,
print.pREC_A
getSequence
prob.seq
pREC_S
## 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)