solve.XGBoost.RdGiven a TReNA object with XGBoost as the solver, use the cor
function with method = "XGBoost" to esimate importances for each transcription factor
as a predictor of the target gene's expression level.
# S4 method for XGBoostSolver run(obj)
| obj | An object of class XGBoostSolver |
|---|
The set of XGBoost relative importances between each transcription factor and the target gene.
Other solver methods:
run,BayesSpikeSolver-method,
run,EnsembleSolver-method,
run,LassoPVSolver-method,
run,LassoSolver-method,
run,PearsonSolver-method,
run,RandomForestSolver-method,
run,RidgeSolver-method,
run,SpearmanSolver-method,
run,SqrtLassoSolver-method
# 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) XGBoost.solver <- XGBoostSolver(mtx.sub, target.gene, tfs) tbl <- run(XGBoost.solver)