solve.BayesSpike: Run the Bayes Spike Solver

In TReNA: 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. This method should be called using the solve method on an appropriate TReNA object.

Usage

## S4 method for signature 'BayesSpikeSolver'
run(obj, target.gene, tfs, tf.weights = rep(1,
  length(tfs)), extraArgs = list())

Arguments

obj	An object of the class Solver with "bayesSpike" as the solver string
target.gene	A designated target gene that should be part of the mtx.assay data
tfs	The designated set of transcription factors that could be associated with the target gene.
tf.weights	A set of weights on the transcription factors (default = rep(1, length(tfs)))
extraArgs	Modifiers to the Bayes Spike solver; this includes n_orderings, the number of random starts used by the solver

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, solve,TReNA-method

Examples

# 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"))
trena <- TReNA(mtx.assay = mtx.sub, solver = "bayesSpike")
target.gene <- "MEF2C"
tfs <- setdiff(rownames(mtx.sub), target.gene)
tbl <- solve(trena, target.gene, tfs)

# Solve the same Alzheimer's problem, but this time set the number of random starts to 100
tbl <- solve(trena, target.gene, tfs, extraArgs = list("n_orderings" = 100))

TReNA documentation built on Nov. 17, 2017, 12:35 p.m.