solve.XGBoost: Run the XGBoost Solver

Description Usage Arguments Value See Also Examples

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

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## 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

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# 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)

trena documentation built on Nov. 15, 2020, 2:07 a.m.