solve.Ridge.RdGiven a TReNA object with Ridge Regression as the solver,
use the glmnet function to estimate coefficients
for each transcription factor as a predictor of the target gene's expression level.
# S4 method for RidgeSolver run(obj)
| obj | An object of class RidgeSolver |
|---|
A data frame containing the coefficients relating the target gene to each transcription factor, plus other fit parameters.
glmnet, , RidgeSolver
Other solver methods:
run,BayesSpikeSolver-method,
run,EnsembleSolver-method,
run,LassoPVSolver-method,
run,LassoSolver-method,
run,PearsonSolver-method,
run,RandomForestSolver-method,
run,SpearmanSolver-method,
run,SqrtLassoSolver-method,
run,XGBoostSolver-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) ridge.solver <- RidgeSolver(mtx.sub, target.gene, tfs) tbl <- run(ridge.solver)