pREC_S {RJaCGH} | R Documentation |
An algorithm to find regions of gain/lost copy number shared by a given proportion of arrays over a probability threshold.
pREC_S(obj, p, freq.array, alteration = "Gain") ## S3 method for class 'RJaCGH.array': pREC_S(obj, p, freq.array, alteration = "Gain")
obj |
An object of class 'RJaCGH.array'. |
p |
Threshold for the minimum joint probability of the region on every array. |
freq.array |
Minimum number of arrays that share every region. |
alteration |
Either 'Gain' or 'Loss'. |
This algorithm, as pREC_A
computes
probabilistic common regions
but instead of finding regions that have
a joint probability of alteration over all arrays, pREC_S
searches for regions that have a probability of alteration
higher than a threshold in at least a minimum number of
arrays.
So, pREC_S
finds subsets of arrays that share subsets
of alterations.
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.
An object of class pREC_S.RJaCGH.array
,
pREC_S.RJaCGH.array.Chrom
or
pREC_S.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. |
members |
Arrays that share the region. |
If there are chromosome information,
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 pMCR2
. Objects created with
function pMCR2
in older versions of RJaCGH can be converted into
the new one in order to print them, or plot them.
Two steps must be performed:
1.-Change the class apropriately; if for example obj
has
class 'pMCR2.RJaCGH.array' make
class(obj) <- 'pREC_S.RJaCGH.array'
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_S.RJaCGH.array
plot.pREC_S.RJaCGH.array
getSequence
prob.seq
pREC_A
## 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) z <- c(rnorm(110, 0, 1), rnorm(20, 3, 1), rnorm(100,0, 1)) zz <- c(rnorm(90, 0, 1), rnorm(40, 3, 1), rnorm(100,0, 1)) fit.array.genome <- RJaCGH(y=cbind(y,z,zz), Pos=Pos, Chrom=Chrom, model="genome", burnin=1000, TOT=1000, jump.parameters=jp, k.max = 4) pREC_S(fit.array.genome, p=0.4, freq.array=2, alteration="Gain") pREC_S(fit.array.genome, p=0.4, freq.array=2, alteration="Loss") ## End(Not run)