solve.BayesSpike.RdGiven 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.
# S4 method for BayesSpikeSolver run(obj)
| obj | An object of the class BayesSpikeSolver |
|---|
A data frame containing the coefficients relating the target gene to each transcription factor, plus other fit parameters
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,SqrtLassoSolver-method,
run,XGBoostSolver-method
if (FALSE) { # 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) }