solve.RandomForest: Run the Random Forest 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 RandomForest as the solver, use the randomForest function
to estimate coefficients for each transcription factor as a predictor of the target gene's
expression level.
Usage
1
2
## S4 method for signature 'RandomForestSolver'
run(obj)
Arguments
obj
An object of class TReNA with "randomForest" as the solver string
Value
A data frame containing the IncNodePurity for each candidate regulator. This coefficient
estimates the relationship between the candidates and the target gene.
See Also
randomForest, RandomForestSolver
Other solver methods:
run,BayesSpikeSolver-method,
run,EnsembleSolver-method,
run,LassoPVSolver-method,
run,LassoSolver-method,
run,PearsonSolver-method,
run,RidgeSolver-method,
run,SpearmanSolver-method,
run,XGBoostSolver-method
Examples
1
2
3
4
5
6
# Load included Alzheimer's data, create a TReNA object with Random Forest as solver, and solve
load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
targetGene <- "MEF2C"
candidateRegulators <- setdiff(rownames(mtx.sub), targetGene)
rf.solver <- RandomForestSolver(mtx.sub, targetGene, candidateRegulators)
tbl <- run(rf.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 RandomForest as the solver, use the randomForest function
to estimate coefficients for each transcription factor as a predictor of the target gene's
expression level.
Usage
1 2 | ## S4 method for signature 'RandomForestSolver'
run(obj)
|
Arguments
obj |
An object of class TReNA with "randomForest" as the solver string |
Value
A data frame containing the IncNodePurity for each candidate regulator. This coefficient estimates the relationship between the candidates and the target gene.
See Also
randomForest, RandomForestSolver
Other solver methods:
run,BayesSpikeSolver-method,
run,EnsembleSolver-method,
run,LassoPVSolver-method,
run,LassoSolver-method,
run,PearsonSolver-method,
run,RidgeSolver-method,
run,SpearmanSolver-method,
run,XGBoostSolver-method
Examples
1 2 3 4 5 6 | # Load included Alzheimer's data, create a TReNA object with Random Forest as solver, and solve
load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
targetGene <- "MEF2C"
candidateRegulators <- setdiff(rownames(mtx.sub), targetGene)
rf.solver <- RandomForestSolver(mtx.sub, targetGene, candidateRegulators)
tbl <- run(rf.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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