mBPCR (version 1.26.0)

estProfileWithMBPCRforOligoSnpSet: Estimate and print the copy number profile of some chromosomes of samples in an oligoSnpSet object

Description

Function to estimate the copy number profile with a piecewise constant function using mBPCR. Eventually, it is possible to estimate the profile with a smoothing curve, using either the Bayesian Regression Curve with $K_2$ (BRC with $K_2$) or the Bayesian Regression Curve Averaging over k (BRCAk). It is also possible to choose the estimator of the variance of the levels rhoSquare (i.e. either $\hat{\rho}_1^2$ or $\hat{\rho}^2$) and by default $\hat{\rho}_1^2$ is used.

Usage

estProfileWithMBPCRforOligoSnpSet(sampleData, sampleToBeAnalyzed, chrToBeAnalyzed, maxProbeNumber, ifLogRatio=1, rhoSquare=NULL, kMax=50, nu=NULL, sigmaSquare=NULL, typeEstRho=1, regr=NULL)

Arguments

sampleData

object of type oligoSnpSet. The following fields must not be empty: assayData(sampleData)$copyNumber (it contains the raw copy number values with scale log2 multiplied by 100 and transformed as integers), featureNames(featureData(sampleData)) (it contains the names of the SNPs), featureData(sampleData)$chromosome (it contains the names of the chromosomes to which each of the SNPs belongs), featureData(sampleData)$position (it contains the physical positions of the SNPs).

sampleToBeAnalyzed

vector containing the number of the columns corresponding to the samples the user wants to analyze.

chrToBeAnalyzed

array containing the name of the chromosomes that the user wants to analyze. The possible values of the chromosomes are: the integers from 1 to 22, 'X' and 'Y'.

maxProbeNumber

maximum number of probes that a chromosome (or arm of a chromosome) can have to be analyzed. The procedure of profile estimation needs the computation of an array of length $(length(chromosome)+1)*(length(chromosome)+2)/2$. To be sure to have set this parameter correctly, try to create the array A <- array(1, dim=(maxProbeNumber+1)*(maxProbeNumber+2)/2), before starting with the estimation procedure.

ifLogRatio

denotes whether the original log2 data were centered at zero (i.e. they were in log2ratio scale) or not. By default, they are considered as derived by log2ratio data (ifLogRatio=1), otherwise (ifLogRatio=0) they are transformed in order to be derived from log2ratio data.

rhoSquare

variance of the segment levels. If rhoSquare=NULL, then the algorithm estimates it on the sample.

kMax

maximum number of segments

nu

mean of the segment levels. If nu=NULL, then the algorithm estimates it on the sample.

sigmaSquare

variance of the noise. If sigmaSquare=NULL, then the algorithm estimates it on the sample.

typeEstRho

choice of the estimator of rhoSquare. If typeEstRho=1, then the algorithm estimates rhoSquare with $\hat{\rho}_1^2$, while if typeEstRho=0, it estimates rhoSquare with $\hat{\rho}^2$.

regr

choice of the computation of the regression curve. If regr=NULL, then the regression curve is not computed, if regr="BRC" the Bayesian Regression Curve is computed (BRC with $K_2$), if regr="BRCAk" the Bayesian Regression Curve Averaging over k is computed (BRCAk).

Value

A list containing:

estPC

an oligoSnpSet equal to sampleData apart from the field assayData(estPC)$copyNumber, which contains the estimated profile with mBPCR in scale log2ratio multiplied by 100

regrCurve

an oligoSnpSet equal to sampleData apart from the field assayData(regrCurve)$copyNumber, which contains the estimated bayesian regression curve in scale log2ratio multiplied by 100. This object is returned only if regr!=NULL.

The matrices assayData(estPC)$copyNumber and assayData(regrCurve)$copyNumber have the same dimension of assayData(sampleData)$copyNumber, hence their elements, corresponding to the not analyzed chromosomes and samples, are equal to NA.

Details

By default, the function estimates the copy number profile with mBPCR and estimating rhoSquare on the sample, using $\hat{\rho}_1^2$. It is also possible to use $\hat{\rho}^2$ as estimator of rhoSquare, by setting typeEstRho=0, or to directly set the value of the parameter. The function gives also the possibility to estimate the profile with a Bayesian regression curve: if regr="BRC" the Bayesian Regression Curve with $K_2$ is computed (BRC with $K_2$), if regr="BRCAk" the Bayesian Regression Curve Averaging over k is computed (BRCAk).

References

Rancoita, P. M. V., Hutter, M., Bertoni, F., Kwee, I. (2009). Bayesian DNA copy number analysis. BMC Bioinformatics 10: 10. http://www.idsia.ch/~paola/mBPCR

See Also

estProfileWithMBPCR, computeMBPCR

Examples

###import an example of oligoSnpSet data
#data(oligoSetExample, package="oligoClasses")
##estimation of chromosome 2 in sample 1 
#r <-estProfileWithMBPCRforOligoSnpSet(oligoSet, sampleToBeAnalyzed=1, chrToBeAnalyzed=2, maxProbeNumber=1000, ifLogRatio=0, rhoSquare=0.0889637)
##plot of the estimated chromosomes
#library(SNPchip)
#cc <- r$estPC
#cc1 <- cc[chromosome(cc) == "2",1]
#par(las=1)
#plot(position(cc1), copyNumber(cc1)/100, ylim=c(-0.23, 0.1), ylab="copy number", xaxt="n")
#plotIdiogram(2, build=genomeBuild(oligoSet), ylim=c(-0.225, -0.19), new=FALSE)