Given a TReNA object with XGBoost as the solver, use the
method = "XGBoost" to esimate importances for each transcription factor
as a predictor of the target gene's expression level.
## S4 method for signature 'XGBoostSolver' run(obj)
An object of class XGBoostSolver
The set of XGBoost relative importances between each transcription factor and the target gene.
Other solver methods:
1 2 3 4 5 6
# 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)
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