run,BayesSpikeSolver-method {trena} | R Documentation |
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.
## S4 method for signature '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
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
## 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)