solve.Pearson: Run the Pearson Solver

In PriceLab/TReNA: Fit transcriptional regulatory networks using gene expression, priors, machine learning

Run the Pearson Solver

Description

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

Usage

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

Arguments

obj	An object of class PearsonSolver

Value

The set of Pearson Correlation Coefficients between each transcription factor and the target gene.

See Also

cor, PearsonSolver

Other solver methods: run,BicorSolver-method, run,EnsembleSolver-method, run,LassoPVSolver-method, run,LassoSolver-method, run,RandomForestSolver-method, run,RidgeSolver-method, run,SpearmanSolver-method, run,XGBoostSolver-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"))
target.gene <- "MEF2C"
tfs <- setdiff(rownames(mtx.sub), target.gene)
pearson.solver <- PearsonSolver(mtx.sub, target.gene, tfs)
tbl <- run(pearson.solver)

PriceLab/TReNA documentation built on March 21, 2023, 1:57 p.m.