solve.SqrtLasso: Run the Square Root LASSO Solver

In PriceLab/trena-until-01mar2018: Fit transcriptional regulatory networks using gene expression, priors, machine learning

Description

Given SqrtLassoSolver object, use the slim function to estimate coefficients for each transcription factor as a predictor of the target gene's expression level.

Usage

## S4 method for signature 'SqrtLassoSolver'
run(obj)

Arguments

obj	An object of class Solver with "sqrtlasso" as the solver string

Value

A data frame containing the coefficients relating the target gene to each transcription factor, plus other fit parameters.

See Also

slim, SqrtLassoSolver

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

# Load included Alzheimer's data, create a TReNA object with Square Root LASSO as solver,
# and run using a few predictors

## Not run: 
load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
target.gene <- "MEF2C"

# Designate just 5 predictors and run the solver
tfs <- setdiff(rownames(mtx.sub), target.gene)[1:5]
sqrt.solver <- SqrtLassoSolver(mtx.sub, target.gene, tfs)
tbl <- run(sqrt.solver)

## End(Not run)

PriceLab/trena-until-01mar2018 documentation built on May 25, 2019, 1:22 p.m.