Description Usage Arguments Value See Also Examples
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.
1 2 | ## S4 method for signature 'RidgeSolver'
run(obj)
|
obj |
An object of class RidgeSolver |
A data frame containing the coefficients relating the target gene to each transcription factor, plus other fit parameters.
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,SqrtLassoSolver-method
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)
|
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