XGBoostSolver: Create a Solver class using gradient boosting (a regression...

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

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: BicorSolver, 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)

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