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