pMCR {RJaCGH} | R Documentation |
This method compute regions of gain/lost copy number with a joint probability of alteration greater than a given threshold.
pMCR(obj, p, alteration = "Gain", array.weights = NULL) ## S3 method for class 'RJaCGH': pMCR(obj, p, alteration = "Gain", array.weights = NULL) ## S3 method for class 'RJaCGH.Chrom': pMCR(obj, p, alteration = "Gain", array.weights = NULL) ## S3 method for class 'RJaCGH.genome': pMCR(obj, p, alteration = "Gain", array.weights = NULL) ## S3 method for class 'RJaCGH.array': pMCR(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 minimal common regions taking into account the probability of every gene to have an altered copy number. The result is a set of genes 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.
An object of class texttt{pMCR.RJaCGH}, texttt{pMCR.RJaCGH.Chrom} or texttt{RJaCGH.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 texttt{RJaCGH.Chrom}, texttt{RJaCGH.genome} or
texttt{RJaCGH.array} with each array of any of these classes), then
those information will be enclosed in a list for each chromosome.
Oscar M. Rueda and Ramon Diaz Uriarte
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/}.
RJaCGH
,
states
, model.averaging
,
print.pMCR.RJaCGH
## 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) 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=1000, 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")