solve.RandomForest.RdGiven a TReNA object with RandomForest as the solver, use the randomForest function
to estimate coefficients for each transcription factor as a predictor of the target gene's
expression level.
# S4 method for RandomForestSolver run(obj)
| obj | An object of class TReNA with "randomForest" as the solver string |
|---|
A data frame containing the IncNodePurity for each candidate regulator. This coefficient estimates the relationship between the candidates and the target gene.
randomForest, RandomForestSolver
Other solver methods:
run,BayesSpikeSolver-method,
run,EnsembleSolver-method,
run,LassoPVSolver-method,
run,LassoSolver-method,
run,PearsonSolver-method,
run,RidgeSolver-method,
run,SpearmanSolver-method,
run,SqrtLassoSolver-method,
run,XGBoostSolver-method
# Load included Alzheimer's data, create a TReNA object with Random Forest as solver, and solve load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData")) targetGene <- "MEF2C" candidateRegulators <- setdiff(rownames(mtx.sub), targetGene) rf.solver <- RandomForestSolver(mtx.sub, targetGene, candidateRegulators) tbl <- run(rf.solver)