run,RidgeSolver-method {trena} | R Documentation

## Run the Ridge Regression Solver

### Description

Given 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.

### Usage

    ## S4 method for signature 'RidgeSolver'
    run(obj)

### Arguments

`obj` | An object of class RidgeSolver

### Value

A data frame containing the coefficients relating the target gene to each transcription factor, plus other fit parameters.

### See Also

`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`

### Examples

    # 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)

[Package trena version 1.2.0 Index]