solve.Ridge: Run the Ridge Regression 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 Ridge Regression as the solver,
use the glmnet 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 'RidgeSolver'
run(obj)
Arguments
obj
An object of class RidgeSolver
Value
A data frame containing the coefficients relating the target gene to each
transcription factor, plus other fit parameters.
See Also
Other solver methods:
run,BayesSpikeSolver-method,
run,EnsembleSolver-method,
run,LassoPVSolver-method,
run,LassoSolver-method,
run,PearsonSolver-method,
run,RandomForestSolver-method,
run,SpearmanSolver-method,
run,XGBoostSolver-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)
ridge.solver <- RidgeSolver(mtx.sub, target.gene, tfs)
tbl <- run(ridge.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 Ridge Regression as the solver,
use the glmnet 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 'RidgeSolver'
run(obj)
|
Arguments
obj |
An object of class RidgeSolver |
Value
A data frame containing the coefficients relating the target gene to each transcription factor, plus other fit parameters.
See Also
Other solver methods:
run,BayesSpikeSolver-method,
run,EnsembleSolver-method,
run,LassoPVSolver-method,
run,LassoSolver-method,
run,PearsonSolver-method,
run,RandomForestSolver-method,
run,SpearmanSolver-method,
run,XGBoostSolver-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)
ridge.solver <- RidgeSolver(mtx.sub, target.gene, tfs)
tbl <- run(ridge.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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