XGBoostSolver {trena} | R Documentation

## Create a Solver class using gradient boosting (a regression technique) and the XGBoost library

### Description

Create a Solver class using gradient boosting (a regression technique) and the XGBoost library

### Usage

    XGBoostSolver(
      mtx.assay = matrix(),
      targetGene,
      candidateRegulators,
      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
`quiet` | A logical denoting whether or not the solver should print output

### Value

A Solver class object with XGBoost Importances (Gain) as the solver

### See Also

`solve.XGBoost`, `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)
    XGBoost.solver <- XGBoostSolver(mtx.sub, target.gene, tfs)

[Package trena version 1.10.2 Index]