Given a most likely configuration of active regulators identified by biRte, this method fits a conventional ridge regression model to explain gene expression. This function is required, if one would like to use MAP based prediction of gene expression instead of Bayesian predictions (see
birtePredict). To fit the ridge regression model the R-package
ridge is employed, which provides an efficient tuning of the regularization hyperparameter.
birteFitRidge(model, mRNA.train, ref.cond=1)
vector of gene expression values
condition to consider
In order to make predictions with the fitted ridge regression model (
birtePredict) store it into a slot "fit.ridge" of the object returned by
an object of class "cv.glmnet" (see
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# artificial data data(humanNetworkSimul) sim = simulateData(affinities2) limmamRNA = limmaAnalysis(sim$dat.mRNA, design=NULL, "treated - control") # burnin and sampling size is much too small in reality result = birteLimma(dat.mRNA=sim$dat.mRNA, data.regulators=NULL, limmamRNA=limmamRNA, affinities=affinities2, niter=100, nburnin=100, thin=2) fit.ridge = birteFitRidge(result, sim$dat.mRNA[,1])
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