birtePredict {birte} | R Documentation |
Given a biRte model, this function makes posterior inference about gene expression data.
birtePredict(model, test.genes, method=c("Bayes", "MAP"), knock.out=NULL)
model |
output of |
test.genes |
Set of gene IDs. Gene IDs should be contained into the defined regulator-target gene network. Note that expression data is generally not required to be available for these genes. |
method |
Bayes: estimate expectation of posterior predictive distribution. MAP: Use previously fitted ridge regression model ( |
knock.out |
optionally: A character vector of those regulators, which should be removed from the network before making predictions, hence simulating a knock.out |
#conditions x #replicates matrix containing data.frame objects with
gene |
gene, for which predictions are made |
mean |
expected expression |
sd |
SD of predictions (only for method="Bayes") |
Holger Froehlich
# 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) est = birtePredict(result, rownames(sim$dat.mRNA))