run,BayesSpikeSolver-method {trena} | R Documentation

## Run the Bayes Spike Solver

### Description

Given a TReNA object with Bayes Spike as the solver, use the `vbsr` function to estimate coefficients for each transcription factor as a predictor of the target gene's expression level.

### Usage

    ## S4 method for signature 'BayesSpikeSolver'
    run(obj)

### Arguments

`obj` | An object of the class BayesSpikeSolver

### Value

A data frame containing the coefficients relating the target gene to each transcription factor, plus other fit parameters

### See Also

`vbsr`, `BayesSpikeSolver`

Other solver methods: `run,EnsembleSolver-method`, `run,LassoPVSolver-method`, `run,LassoSolver-method`, `run,PearsonSolver-method`, `run,RandomForestSolver-method`, `run,RidgeSolver-method`, `run,SpearmanSolver-method`, `run,XGBoostSolver-method`

### Examples

    ## Not run:
    # Load included Alzheimer's data, create a TReNA object with Bayes Spike as solver, and solve
    load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
    target.gene <- "MEF2C"
    tfs <- setdiff(rownames(mtx.sub), target.gene)
    bayes.solver <- BayesSpikeSolver(mtx.sub, target.gene, tfs)
    tbl <- run(bayes.solver)

    # Solve the same Alzheimer's problem, but this time set the number of random starts to 100
    bayes.solver <- BayesSpikeSolver(mtx.sub, target.gene, tfs, nOrderings = 100)
    tbl <- run(bayes.solver)

    ## End(Not run)

[Package trena version 1.12.1 Index]