estProfileWithMBPCR {mBPCR} | R Documentation |
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 rhoSquare
(i.e. either \hat{rho}_1^2 or \hat{rho}^2) and by default \hat{rho}_1^2 is used.
The function gives also the possibility to print the results.
estProfileWithMBPCR(path='', sampleName='', snpName, chr, position, logratio, chrToBeAnalyzed, maxProbeNumber, rhoSquare=NULL, kMax=50, nu=NULL, sigmaSquare=NULL, typeEstRho=1, regr=NULL)
path |
pathway of the folder where the user wants to print the results of the estimation (it must end with '\\' in windows, or '//' in linux).
By default the results are printed in the working directory, while if path=NULL the results are not printed. |
sampleName |
name of the sample analyzed. If the name of the sample if provided, it is used to named the printed files. |
snpName |
array containing the name of each probe |
chr |
array containing the name of the chromosome to which each of the probes belongs |
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: an integer from 1 to 22 and 'X'. |
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{rho}_1^2, if typeEstRho=0 estimates it 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=1 the Bayesian Regression Curve is computed (BRC with K_2), if regr=2 the Bayesian
Regression Curve Averaging over k is computed (BRCAk). |
A list cointaining: estPC
(i.e. an array containing the estimated profile with mBPCR), estBoundaries
(i.e. the list of estimated breakpoints for each of the analyzed chomosomes),
postProbT
(i.e. the list of the posterior probablity to be a breakpoint for each estimated breakpoint of the analyzed chomosomes) and, eventually, regrCurve
(i.e. 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
.
##import the 10K data of cell line REC ##for windows path <- 'data\\rec10k.dat' ##for linux ##path <- 'data//rec10k.dat' rec10k <- importCNData(path, NRowSkip=1) ##estimation of the profile of all chromosomes results=estProfileWithMBPCR(path='', sampleName='rec10k', rec10k$snpName, rec10k$chr, rec10k$position, rec10k$logratio, chrToBeAnalyzed=c(1:22,'X'), maxProbeNumber=2000) ##plot the estimated profile of chromosome 3 y <- rec10k$logratio[rec10k$chr == 3] p <- rec10k$position[rec10k$chr == 3] plot(p, y) points(p, results$estPC[rec10k$chr == 3], type='l', col='red')[Package mBPCR version 1.0 Index]