solve.XGBoost: Run the XGBoost Solver
In trena: Fit transcriptional regulatory networks using gene expression, priors, machine learning
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
1
2
## 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
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
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)
trena documentation built on Nov. 15, 2020, 2:07 a.m.
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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
1 2 | ## 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
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
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)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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