computeMBPCR {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.
computeMBPCR(y, kMax=50, nu=NULL, rhoSquare=NULL, sigmaSquare=NULL, typeEstRho=1, regr=NULL)
y |
array containing the log2ratio of the copy number data |
kMax |
maximum number of segments |
nu |
mean of the segment levels. If nu=NULL , then the algorithm estimates it on the sample. |
rhoSquare |
variance of the segment levels. If rhoSquare=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 with K_2 is computed (BRC with K_2), if regr=2 the Bayesian
Regression Curve Averaging over k is computed (BRCAk). |
A list cointaining: estK
(the estimated number of segments), estBoundaries
(the estimated boundaries),
estPC
(the estimated profile with mBPCR), nu
, rhoSquare
, sigmaSquare
,
postProbT
(i.e. for each probe, the posterior probablity to be a breakpoint) and, eventually, regrCurve
(i.e. the estimated bayesian regression curve).
##import the 250K NSP data of chromosome 11 of cell line JEKO-1 ##for windows path <- 'data\\jekoChr11Array250Knsp.dat' ##for linux ##path <- 'data//jekoChr11Array250Knsp.dat' jekoChr11Array250Knsp <- importCNData(path, NRowSkip=1) ##first example ## we select a part of chromosome 11 y <- jekoChr11Array250Knsp$logratio[10600:11600] p <- jekoChr11Array250Knsp$position[10600:11600] ##we estimate the profile using the global parameters estimated on the whole genome results <- computeMBPCR(y, nu=-3.012772e-10, rhoSquare=0.0479, sigmaSquare=0.0699, regr=2) plot(p,y) points(p, results$estPC, type='l', col='red') points(p, results$regrCurve, type='l', col='green') ##second example ## we select a part of chromosome 11 y <- jekoChr11Array250Knsp$logratio[6400:6900] p <- jekoChr11Array250Knsp$position[6400:6900] ##we estimate the profile using the global parameters estimated on the whole genome results <- computeMBPCR(y, nu=-3.012772e-10, rhoSquare=0.0479, sigmaSquare=0.0699, regr=1) plot(p, y) points(p, results$estPC, type='l', col='red') points(p, results$regrCurve,type='l', col='green')[Package mBPCR version 1.0 Index]