pydeseq2 0.1.1

A python implementation of DESeq2.

PyDESeq2

Table of Contents

• PyDESeq2
  • Table of Contents
  • Overview
  • Installation
    • Requirements
  • Getting started
    • Documentation
    • TCGA Data
  • Contributing
    • 1 - Download the repository
    • 2 - Create a conda environment
  • Citing this work
  • References
  • License

Overview

This package is a python implementation of the DESeq2 method [1] for differential expression analysis (DEA) with bulk RNA-seq data, originally in R. It aims to facilitate DEA experiments for python users.

As PyDESeq2 is a re-implementation of DESeq2 from scratch, you may experience some differences in terms of retrieved values or available features.

Currently, available features broadly correspond to the default settings of DESeq2 (v1.34.0) for single-factor analysis, but we plan to implement more in the near future. In case there is a feature you would particularly like to be implemented, feel free to open an issue.

Installation

PyDESeq2 can be installed from PyPI:

pip install pydeseq2

We recommend installing within a conda environment:

conda env create -n pydeseq2
conda activate pydeseq2
pip install pydeseq2

If you're interested in contributing or want access to the development version, please see the contributing section.

Requirements

The list of package version requirements is available in setup.py.

For reference, the code was tested with python 3.8 and the following package versions:

- numpy 1.23.0
- pandas 1.4.3
- scikit-learn 1.1.1
- scipy 1.8.1
- statsmodels 0.13.2

Please don't hesitate to open an issue in case you encounter any issue due to possible deprecations.

Getting started

The notebooks directory contains minimal examples on how to use PyDESeq2, in the form of jupyter notebooks.

You can also try them from your browser (on synthetic data only):

Documentation

The documentation is hosted here on ReadTheDocs. If you want to have the latest version of the documentation, you can build it from source. Please go to the dedicated README.md for information on how to do so.

TCGA Data

The quick start notebooks either use synthetic data (provided in this repo) or data from The Cancer Genome Atlas.

For more information on how to obtain and organize TCGA data, see datasets.

Contributing

1 - Download the repository

git clone https://github.com/owkin/PyDESeq2.git

2 - Create a conda environment

Run conda env create -n pydeseq2 python=3.8 (or higher python version) to create the pydeseq2 environment and then activate it: conda activate pydeseq2.

cd to the root of the repo and run pip install -e ."[dev]" to install in developer mode.

Then, run pre-commit install.

The pre-commit tool will automatically run black and isort, and check flake8 compatibility

PyDESeq2 is a living project and any contributions are welcome! Feel free to open new PRs or issues.

Citing this work

@article{muzellec2022pydeseq2,
  title={PyDESeq2: a python package for bulk RNA-seq differential expression analysis},
  author={Muzellec, Boris and Telenczuk, Maria and Cabeli, Vincent and Andreux, Mathieu},
  year={2022},
  doi = {10.1101/2022.12.14.520412},
  journal={bioRxiv},
}

References

[1] Love, M. I., Huber, W., & Anders, S. (2014). "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome biology, 15(12), 1-21. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8

[2] Zhu, A., Ibrahim, J. G., & Love, M. I. (2019). "Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences." Bioinformatics, 35(12), 2084-2092. https://academic.oup.com/bioinformatics/article/35/12/2084/5159452

License

PyDESeq2 is released under an MIT license.