recipe bioconductor-biosigner

Signature discovery from omics data

Homepage

https://bioconductor.org/packages/3.14/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

1.22.0-0,  1.20.0-0,  1.18.2-0,  1.18.0-0,  1.16.0-0,  1.14.0-0,  1.12.0-1,  1.10.0-0,  1.8.0-0, 

1.22.0-0,  1.20.0-0,  1.18.2-0,  1.18.0-0,  1.16.0-0,  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 >=2.54.0,<2.55.0

• bioconductor-multidataset >=1.22.0,<1.23.0

• bioconductor-ropls >=1.26.0,<1.27.0

• r-base >=4.1,<4.2.0a0

• r-e1071

• r-randomforest

Required By

• bioconductor-multisight

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>)

Download stats

Link to this page

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