estProfileWithMBPCR: Estimate and print the copy number profile of some...
In mBPCR: Bayesian Piecewise Constant Regression for DNA copy number estimation

Description Usage Arguments Details Value References See Also Examples

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{ρ}_1^2 or \hat{ρ}^2) and by default \hat{ρ}_1^2 is used.

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  estProfileWithMBPCR(snpName, chr, position, logratio, chrToBeAnalyzed, maxProbeNumber, 
                      rhoSquare=NULL, kMax=50, nu=NULL, sigmaSquare=NULL, typeEstRho=1, 
                      regr=NULL, hg='hg18')

`snpName`	array containing the name of each probe
`chr`	array containing the name of the chromosome to which each of the probes belongs. The possible values of the elements of `chr` are: the integers from 1 to 22, 'X' and 'Y'.
`position`	array containing the physical position of each probe
`logratio`	array containing the log2ratio of the raw copy number data
`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.
`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{ρ}_1^2, while if `typeEstRho=0`, it estimates `rhoSquare` with \hat{ρ}^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).
`hg`	genome build used for retrieving the base positions of the centromeres in case the chromosomes need to be divided into two parts for the estimation (see explanation of `maxProbeNumber`). Current available options are: `'hg18'`, `'hg19'` and `'hg38'`.

By default, the function estimates the copy number profile with mBPCR and estimating rhoSquare on the sample, using \hat{ρ}_1^2. It is also possible to use \hat{ρ}^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).

See function writeEstProfile, to have the results in nicer tables or to write them on files.

A list containing:

`estPC`	an array containing the estimated profile with mBPCR
`estBoundaries`	the list of estimated breakpoints for each of the analyzed chomosomes
`postProbT`	the list of the posterior probablity to be a breakpoint for each estimated breakpoint of the analyzed chomosomes
`regrCurve`	an array containing the estimated bayesian regression curve

estPC and regrCurve have the same length of logratio, hence their components, corresponding to the not analyzed chromosomes, are equal to NA.

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

plotEstProfile, writeEstProfile, computeMBPCR

##import the 10K data of cell line REC  
data(rec10k)
##estimation of the profile of chromosome 5
results <- estProfileWithMBPCR(rec10k$SNPname, rec10k$Chromosome, rec10k$PhysicalPosition, 
rec10k$log2ratio, chrToBeAnalyzed=5, maxProbeNumber=2000)
##plot the estimated profile of chromosome 5
y <- rec10k$log2ratio[rec10k$Chromosome == 5]
p <- rec10k$PhysicalPosition[rec10k$Chromosome == 5]
plot(p, y)
points(p, results$estPC[rec10k$Chromosome == 5], type='l', col='red')

###for the estimation of the profile of all chromosomes
#results <- estProfileWithMBPCR(rec10k$SNPname, rec10k$Chromosome, rec10k$PhysicalPosition, 
#rec10k$log2ratio, chrToBeAnalyzed=c(1:22,'X'), maxProbeNumber=2000)