BayesSpikeSolver: Create a Solver class object using the Bayes Spike Solver

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

Description

Create a Solver class object using the Bayes Spike Solver

Usage

BayesSpikeSolver(mtx.assay = matrix(), targetGene, candidateRegulators,
  nOrderings = 10, quiet = TRUE)

Arguments

mtx.assay	An assay matrix of gene expression data
targetGene	A designated target gene that should be part of the mtx.assay data
candidateRegulators	The designated set of transcription factors that could be associated with the target gene
nOrderings	An integer denoting the number of random starts to use for the Bayes Spike method (default = 10)
quiet	A logical denoting whether or not the solver should print output

Value

A Solver class object with Bayes Spike as the solver

See Also

solve.BayesSpike, getAssayData

Other Solver class objects: EnsembleSolver, HumanDHSFilter-class, LassoPVSolver, LassoSolver, PearsonSolver, RandomForestSolver, RidgeSolver, Solver-class, SpearmanSolver, SqrtLassoSolver

Examples

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)

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