SqrtLassoSolver {trena} | R Documentation

## Create a Solver class object using the Square Root LASSO solver

### 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)

[Package trena version 1.2.0 Index]