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.
With an activated Bioconda channel (see 2. Set up channels), install with:
conda install bioconductor-biosigner
and update with:
conda update bioconductor-biosigner
A Docker container is available at https://quay.io/repository/biocontainers/bioconductor-biosigner.