run,XGBoostSolver-method {trena} | R Documentation

## Run the XGBoost Solver

### Description

Given a TReNA object with XGBoost as the solver, use the `cor` function with `method = "XGBoost"` to esimate importances for each transcription factor as a predictor of the target gene's expression level.

### Usage

    ## S4 method for signature 'XGBoostSolver'
    run(obj)

### Arguments

`obj` | An object of class XGBoostSolver

### Value

The set of XGBoost relative importances between each transcription factor and the target gene.

### See Also

`cor`, `XGBoostSolver`

Other solver methods: `run,BayesSpikeSolver-method`, `run,EnsembleSolver-method`, `run,LassoPVSolver-method`, `run,LassoSolver-method`, `run,PearsonSolver-method`, `run,RandomForestSolver-method`, `run,RidgeSolver-method`, `run,SpearmanSolver-method`

### Examples

    # Load included Alzheimer's data, create a TReNA object with Bayes Spike as solver, and solve
    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)
    tbl <- run(XGBoost.solver)

[Package trena version 1.10.2 Index]