birtePredict {birte} | R Documentation

## Prediction of gene expression via biRte.

### Description

Given a biRte model, this function makes posterior inference about gene expression data.

### Usage

    birtePredict(model, test.genes, method=c("Bayes", "MAP"), knock.out=NULL)

### Arguments

`model` | output of `birteRun`
`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 (`birteFitRidge`), which has to be stored into a slot "fit.ridge".
`knock.out` | optionally: A character vector of those regulators, which should be removed from the network before making predictions, hence simulating a knock.out

### Value

#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")

### Author(s)

Holger Froehlich

### Examples

    # 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))

[Package birte version 1.18.0 Index]