https://github.com/cwieder/metabolomics-ora
Code for simulations in "Pathway analysis in metabolomics: Pitfalls and best practice for the use of Over-representation Analysis" 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.
Science Score: 13.0%
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Found 6 DOI reference(s) in README -
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Low similarity (10.7%) to scientific vocabulary
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Repository
Code for simulations in "Pathway analysis in metabolomics: Pitfalls and best practice for the use of Over-representation Analysis" 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.
Basic Info
Statistics
- Stars: 9
- Watchers: 3
- Forks: 3
- Open Issues: 0
- Releases: 0
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Metadata Files
README.md
Pathway analysis in metabolomics: Pitfalls and best practice for the use of Over-representation Analysis
Cecilia Wieder 1, Clment Frainay 3, Nathalie Poupin 3, Pablo Rodrguez-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, Rodrguez-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
Owner
- Login: cwieder
- Kind: user
- Location: London, UK
- Company: Imperial College London
- Repositories: 15
- Profile: https://github.com/cwieder