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 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.
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 |
rhoSquare |
variance of the segment levels. If |
sigmaSquare |
variance of the noise. If |
typeEstRho |
choice of the estimator of |
regr |
choice of the computation of the regression curve. If |
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).
A list containing:
|
the estimated number of segments |
|
the estimated boundaries |
|
the estimated profile with mBPCR |
|
the estimated bayesian regression curve. It is returned only if |
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for each probe, the posterior probablity to be a breakpoint |
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
estProfileWithMBPCR
, plotEstProfile
, writeEstProfile
, estGlobParam
##import the 250K NSP data of chromosome 11 of cell line JEKO-1 data(jekoChr11Array250Knsp) ##first example ## we select a part of chromosome 11 y <- jekoChr11Array250Knsp$log2ratio[6400:6900] p <- jekoChr11Array250Knsp$PhysicalPosition[6400:6900] ##we estimate the profile using the global parameters estimated on the whole genome ##the profile is estimated with mBPCR and with the Bayesian Regression Curve results <- computeMBPCR(y, nu=-3.012772e-10, rhoSquare=0.0479, sigmaSquare=0.0699, regr="BRC") 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$log2ratio[10600:11600] #p <- jekoChr11Array250Knsp$PhysicalPosition[10600:11600] ###we estimate the profile using the global parameters estimated on the whole genome ###the profile is estimated with mBPCR and with the Bayesian Regression Curve Ak #results <- computeMBPCR(y, nu=-3.012772e-10, rhoSquare=0.0479, sigmaSquare=0.0699, regr="BRCAk") #plot(p,y) #points(p, results$estPC, type='l', col='red') #points(p, results$regrCurve, type='l', col='green')