elasticNetSolver: Run the Elastic Net Solvers

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

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.