trena-package {trena} | R Documentation

## Inferring Transcriptional Regulation with TReNA

### Description

'trena' provides a framework for using gene expression data to infer relationships between a target gene and a set of transcription factors. It does so using a several classes and their associated methods, briefly documented below

### Details

#' Solver Class Objects

The `Solver` class is a base class used within 'trena'. A particular `Solver` object also contains the name of the selected solver and dispatches the correct feature selection method when `run` is called on the object. It is inherited by all the following subclasses, representing the different feature selection methods: `BayesSpikeSolver`, `EnsembleSolver`, `LassoPVSolver`, `LassoSolver`, `PearsonSolver`, `RandomForestSolver`, `RidgeSolver`, `SpearmanSolver`, `SqrtLassoSolver`.

CandidateFilter Class Objects

The `CandidateFilter` class is a base class that is generally used to filter the transcription factors in the expression matrix to obtain a set of candidate regulators. Filtering method depends on the filter type chosen; there are currently the following subclasses: `FootprintFilter`, `HumanDHSFilter`, `GeneOntologyFilter`, and `VarianceFilter`. The filters are applied using the `getCandidates` method on a given `CandidateFilter` object.

FootprintFinder Class Objects

The `FootprintFinder` class is designed to allow extraction of gene footprinting information from existing PostgreSQL or SQLite databases. In standard use of the 'trena' package, it is used solely by the `getCandidates` method for a `FootprintFilter` object. However, a `FootprintFinder` object has many more available methods that allow it to extract information more flexibly.

[Package trena version 1.2.0 Index]