recipe bioconductor-biosigner

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.

Homepage

https://bioconductor.org/packages/3.9/bioc/html/biosigner.html

License

CeCILL

Recipe

/bioconductor-biosigner/meta.yaml

Links

biotools: biosigner, doi: 10.3389/fmolb.2016.00026

package bioconductor-biosigner

Versions

1.10.0-0, 1.8.0-0, 1.6.0-0, 1.4.0-0, 1.1.10-0, 1.0.6-0

Depends

• bioconductor-biobase >=2.42.0,<2.43.0

• bioconductor-ropls >=1.14.0,<1.15.0

• r-base >=3.5.1,<3.5.2.0a0

• r-e1071

• r-randomforest

Required By

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-biosigner

and update with:

conda update bioconductor-biosigner

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-biosigner:<tag>

(see bioconductor-biosigner/tags for valid values for <tag>)

Link to this page

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-biosigner/README.html)