GReNaDIne: Gene Regulatory Network Data-driven Inference

This Python 3.7 package allows to infer Gene Regulatory Networks through several Data-driven methods. Pre-processing and evaluation methods are also included.

Dependencies

• pip install numpy version 1.16.2
• pip install pandas version 0.24.2
• pip install matplotlib version 3.0.3
• pip install joblib version 0.13.2
• pip install Cython version 0.29.6
• pip install scikit-learn version 0.20.3
• pip install networkx
• pip install seaborn
• pip install pot version 0.5.1 POT is not included in the automatic dependencies install You have to install it if you want to use columns_matrix_OT_norm() normalization function. The other functions of the package do not need POT.
• pip install rpy2 version 3.0.2 rpy2 is not included in the automatic dependencies install You have to install it if you want to use DEseq2() normalization function. The other functions of the package do not need rpy2.

Installation:

pip install GReNaDIne

Docker Image

docker run speignier/grenadine

Documentation:

grenadine.readthedocs.io

Tutorials:

Check the jupyter notebook tutorials located in the tutorial folder

• Infer_dream5_E_coli_GRN_using_GENIE3.ipynb to infer GRNs using the GENIE3 method (random forest regression)
• Infer_dream5_E_coli_GRN_using_SVMs.ipynb to infer GRNs using the SVM method (SVM classification)

Authors:

For bug reports and feedback do not hesitate to contact the authors

• Sergio Peignier: sergio.peignier AT insa-lyon.fr
• Pauline Schmitt: pauline.schmitt AT insa-lyon.fr

Maintainer:

• Sergio Peignier: sergio.peignier AT insa-lyon.fr