solve.Ridge: Run the Ridge Regression Solver

In PriceLab/TReNA: Fit transcriptional regulatory networks using gene expression, priors, machine learning

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,BicorSolver-method, run,EnsembleSolver-method, run,LassoPVSolver-method, run,LassoSolver-method, run,PearsonSolver-method, run,RandomForestSolver-method, run,SpearmanSolver-method, run,XGBoostSolver-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)

PriceLab/TReNA documentation built on March 21, 2023, 1:57 p.m.