SqrtLassoSolver: Create a Solver class object using the Square Root LASSO...

In PriceLab/trena-until-01mar2018: Fit transcriptional regulatory networks using gene expression, priors, machine learning

Description

Create a Solver class object using the Square Root LASSO solver

Usage

SqrtLassoSolver(mtx.assay = matrix(), targetGene, candidateRegulators,
  regulatorWeights = rep(1, length(candidateRegulators)),
  lambda = numeric(0), nCores = 4, quiet = TRUE)

Arguments

mtx.assay	An assay matrix of gene expression data
targetGene	A designated target gene that should be part of the mtx.assay data
candidateRegulators	The designated set of transcription factors that could be associated with the target gene
regulatorWeights	A set of weights on the transcription factors (default = rep(1, length(tfs)))
lambda	A tuning parameter that determines the severity of the penalty function imposed on the elastic net regression. If unspecified, lambda will be determined via permutation testing (default = numeric(0)).
nCores	An integer specifying the number of computational cores to devote to this square root LASSO solver. This solver is generally quite slow and is greatly sped up when using multiple cores (default = 4)
quiet	A logical denoting whether or not the solver should print output

Value

A Solver class object with Square Root LASSO as the solver

See Also

solve.SqrtLasso, getAssayData

Other Solver class objects: BayesSpikeSolver, EnsembleSolver, HumanDHSFilter-class, LassoPVSolver, LassoSolver, PearsonSolver, RandomForestSolver, RidgeSolver, Solver-class, SpearmanSolver

Examples

load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
target.gene <- "MEF2C"
tfs <- setdiff(rownames(mtx.sub), target.gene)
sqrt.solver <- SqrtLassoSolver(mtx.sub, target.gene, tfs)

PriceLab/trena-until-01mar2018 documentation built on May 25, 2019, 1:22 p.m.