EnsembleSolver {trena} | R Documentation

## Create a Solver class object using an ensemble of solvers

### Description

Create a Solver class object using an ensemble of solvers

### Usage

    EnsembleSolver(
      mtx.assay = matrix(),
      targetGene,
      candidateRegulators,
      solverNames = c("lasso", "lassopv", "pearson", "bicor", "randomForest", "ridge",
        "spearman", "xgboost"),
      geneCutoff = 0.1,
      alpha.lasso = 0.9,
      alpha.ridge = 0,
      lambda.lasso = numeric(0),
      lambda.ridge = numeric(0),
      lambda.sqrt = numeric(0),
      nCores.sqrt = 4,
      nOrderings.bayes = 10,
      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
`solverNames` | A character vector of strings denoting
`geneCutoff` | A fraction (0-1) of the supplied candidate regulators to be included in the fetaures output by the solver (default = 0.1)
`alpha.lasso` | A fraction (0-1) denoting the LASSO-Ridge balance of the 'glmnet' solver used by the LASSO method (default = 0.9)
`alpha.ridge` | A fraction (0-1) denoting the LASSO-Ridge balance of the 'glmnet' solver used by the Ridge method (default = 0)
`lambda.lasso` | The penalty parameter for LASSO, used to determine how strictly to penalize the regression coefficients. If none is supplied, this will be determined via permutation testing (default = NULL).
`lambda.ridge` | The penalty parameter for Ridge, used to determine how strictly to penalize the regression coefficients. If none is supplied, this will be determined via permutation testing (default = NULL).
`lambda.sqrt` | The penalty parameter for square root LASSO, used to determine how strictly to penalize the regression coefficients. If none is supplied, this will be determined via permutation testing (default = NULL).
`nCores.sqrt` | An integer denoting the number of computational cores to devote to the square root LASSO solver, which is the slowest of the solvers (default = 4)
`nOrderings.bayes` | An integer denoting the number of random starts to use for the Bayes Spike method (default = 10)
`quiet` | A logical denoting whether or not the solver should print output

### Value

A Solver class object with Ensemble as the solver

### See Also

`solve.Ensemble`, `getAssayData`

Other Solver class objects: `BayesSpikeSolver`, `BicorSolver`, `HumanDHSFilter-class`, `LassoPVSolver`, `LassoSolver`, `PearsonSolver`, `RandomForestSolver`, `RidgeSolver`, `Solver-class`, `SpearmanSolver`, `XGBoostSolver`

### Examples

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

[Package trena version 1.16.0 Index]