Pathway analysis in metabolomics: Pitfalls and best practice for the use of Over-representation Analysis

Cecilia Wieder ¹, Clément Frainay ³, Nathalie Poupin ³, Pablo Rodríguez-Mier ³, Florence Vinson ³, Juliette Cooke ³, Rachel PJ Lai ², Jacob G Bundy ¹, Fabien Jourdan ³, Timothy Ebbels ¹

¹ Department of Metabolism, Digestion, and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK

² Department of Infectious Disease, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK

³ INRA, Toulouse University, INP, UMR 1331, Toxalim, Research Centre in Food Toxicology, 180 chemin de Tournefeuille, Toulouse, France

This repository contains the code to run the simulations presented in the study. The Python code to generate the results is contained within the Jupyter notebook src/reproducible_simulations.ipynb. Users may adapt the code in the notebook to perform the simulations on their own data. All code has been tested using Python 3.8 on MacOS (v11.2.3) with standard hardware (16GB RAM).

Getting started: local installation

Clone the repository

git clone https://github.com/cwieder/metabolomics-ORA.git

Install the required packages

cd metabolomics-ORA/src
pip3 install -r requirements.txt

Cloning the repository and installing the dependencies should take less than 10 minutes on a standard desktop computer.

Usage

Launch the reproducible_simulations.ipynb Jupyter notebook and run the code cells

cd metabolomics-ORA/src
jupyter-notebook reproducible_simulations.ipynb

Run remotely using Google Colaboratory

As an alternative to local installation, the Jupyter notebook can now be run as an analogous Google Colab version. This does not require any local installation of code, packages, or dependencies, and all simulations are run in a browser window (Chrome, Firefox, and Safari are supported). All code is run on one of Google's virtual machines and therefore does not require access to the user's hardware.

To get started, open the Colab notebook and run the cells.

License

MIT

Citation

Wieder C, Frainay C, Poupin N, Rodríguez-Mier P, Vinson F, et al. (2021) Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis. PLOS Computational Biology 17(9): e1009105. https://doi.org/10.1371/journal.pcbi.1009105