biosigner

This package is for version 3.4 of Bioconductor; for the stable, up-to-date release version, see biosigner.

Signature discovery from omics data

Bioconductor version: 3.4

Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics.

Author: Philippe Rinaudo <phd.rinaudo at gmail.com>, Etienne Thevenot <etienne.thevenot at cea.fr>

Maintainer: Philippe Rinaudo <phd.rinaudo at gmail.com>, Etienne Thevenot <etienne.thevenot at cea.fr>

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("biosigner")

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("biosigner")

Details

Package Archives

Follow Installation instructions to use this package in your R session.