solve.BayesSpike: Run the Bayes Spike Solver

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

Description

Given a TReNA object with Bayes Spike as the solver, use the vbsr function to estimate coefficients for each transcription factor as a predictor of the target gene's expression level.

Usage

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

Arguments

obj	An object of the class BayesSpikeSolver

Value

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

See Also

vbsr, BayesSpikeSolver

Other solver methods: run,EnsembleSolver-method, run,LassoPVSolver-method, run,LassoSolver-method, run,PearsonSolver-method, run,RandomForestSolver-method, run,RidgeSolver-method, run,SpearmanSolver-method, run,SqrtLassoSolver-method

Examples

## Not run: 
# 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)
bayes.solver <- BayesSpikeSolver(mtx.sub, target.gene, tfs)
tbl <- run(bayes.solver)

# Solve the same Alzheimer's problem, but this time set the number of random starts to 100
bayes.solver <- BayesSpikeSolver(mtx.sub, target.gene, tfs, nOrderings = 100)
tbl <- run(bayes.solver)

## End(Not run)

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