elasticNetSolver {trena} | R Documentation |
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.
elasticNetSolver(obj, target.gene, tfs, tf.weights, alpha, lambda, keep.metrics)
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 |
A data frame containing the coefficients relating the target gene to each transcription factor, plus other fit parameters