recipe bioconductor-biosigner

Signature discovery from omics data







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 online infrastructure for computational metabolomics.

package bioconductor-biosigner

(downloads) docker_bioconductor-biosigner



depends bioconductor-biobase:


depends bioconductor-multiassayexperiment:


depends bioconductor-multidataset:


depends bioconductor-ropls:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-e1071:

depends r-randomforest:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-biosigner

and update with::

   mamba update bioconductor-biosigner

To create a new environment, run:

mamba create --name myenvname bioconductor-biosigner

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

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

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