recipe bioconductor-biosigner

Signature discovery from omics data

Homepage

https://bioconductor.org/packages/3.10/bioc/html/biosigner.html

License

CeCILL

Recipe

/bioconductor-biosigner/meta.yaml

Links

biotools: biosigner, doi: 10.3389/fmolb.2016.00026

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.

package bioconductor-biosigner

Versions

1.14.0-0, 1.12.0-1, 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 >=2.46.0,<2.47.0

• bioconductor-multidataset >=1.14.0,<1.15.0

• bioconductor-ropls >=1.18.0,<1.19.0

• r-base >=3.6,<3.7.0a0

• 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

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