solve.XGBoost: Run the XGBoost Solver

In PriceLab/TReNA: Fit transcriptional regulatory networks using gene expression, priors, machine learning

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,BicorSolver-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)

PriceLab/TReNA documentation built on March 21, 2023, 1:57 p.m.