elasticNetSolver: Run the Elastic Net Solvers

elasticNetSolverR Documentation

Run the Elastic Net Solvers

Description

Given a TReNA object with either LASSO or 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

elasticNetSolver(
  obj,
  target.gene,
  tfs,
  tf.weights,
  alpha,
  lambda,
  keep.metrics
)

Arguments

obj

An object of class Solver

target.gene

A designated target gene that should be part of the mtx.assay data

tfs

The designated set of transcription factors that could be associated with the target gene.

tf.weights

A set of weights on the transcription factors (default = rep(1, length(tfs)))

alpha

The LASSO/Ridge tuning parameter

lambda

The penalty tuning parameter for elastic net

keep.metrics

A binary variable indicating whether or not to keep metrics

Value

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

See Also

glmnet


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