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Last data update: 2014.03.03

R: Estimate and print the copy number profile of some...

estProfileWithMBPCR

R Documentation

Estimate and print the copy number profile of some chromosomes of a sample

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

Usage

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

Arguments

`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).

Details

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.

Value

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.

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

Examples

##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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(mBPCR)
Loading required package: oligoClasses
Welcome to oligoClasses version 1.34.0
Loading required package: SNPchip
Welcome to SNPchip version 2.18.0
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/mBPCR/estProfileWithMBPCR.Rd_%03d_medium.png", width=480, height=480)
> ### Name: estProfileWithMBPCR
> ### Title: Estimate and print the copy number profile of some chromosomes
> ###   of a sample
> ### Aliases: estProfileWithMBPCR
> ### Keywords: regression smooth
> 
> ### ** Examples
> 
> ##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)
Estimation of global  parameters
Estimation of the profile of chromosome 5
Computation of log(A^0)
Computation of left and right recursions
Determination of PC Regression
> ##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)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>