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https://github.com/owkin/PyDESeq2

A Python implementation of the DESeq2 pipeline for bulk RNA-seq DEA.
https://github.com/owkin/PyDESeq2

bioinformatics differential-expression python rna-seq transcriptomics

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A Python implementation of the DESeq2 pipeline for bulk RNA-seq DEA.

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README 

        



#

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PyDESeq2 is a python implementation of the [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) 

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](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) 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 and 

multi-factor analysis (with categorical or continuous factors) using Wald tests.

We plan to implement more in the future.

In case there is a feature you would particularly like to be implemented, feel free to open an issue.



## Table of Contents

- [PyDESeq2](#pydeseq2)

  - [Table of Contents](#table-of-contents)

  - [Installation](#installation)

    - [Requirements](#requirements)

  - [Getting started](#getting-started)

    - [Documentation](#documentation)

    - [Data](#data)

  - [Contributing](#contributing)

    - [1 - Download the repository](#1---download-the-repository)

    - [2 - Create a conda environment](#2---create-a-conda-environment)

  - [Development roadmap](#development-roadmap)

  - [Citing this work](#citing-this-work)

  - [References](#references)

  - [License](#license)



## Installation



### PyPI



`PyDESeq2` can be installed from PyPI using `pip`:



`pip install pydeseq2`



We recommend installing within a conda environment:



```

conda create -n pydeseq2

conda activate pydeseq2

conda install pip

pip install pydeseq2

```



### Bioconda



`PyDESeq2` can also be installed from Bioconda with `conda`:



`conda install -c bioconda pydeseq2`



If you're interested in contributing or want access to the development version, please see the [contributing](#contributing) section.



### Requirements



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



For reference, the code is being tested in a github workflow (CI) with python

3.9 to 3.11 and the following package versions:

```

- anndata 0.8.0

- numpy 1.23.0

- pandas 1.4.3

- scikit-learn 1.1.1

- scipy 1.11.0

```



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



## Getting started



The [Getting Started](https://pydeseq2.readthedocs.io/en/latest/auto_examples/index.html) section of the documentation

contains downloadable examples on how to use PyDESeq2.



### Documentation



The documentation is hosted [here on ReadTheDocs](https://pydeseq2.readthedocs.io/en/latest/). 

If you want to have the latest version of the documentation, you can build it from source.

Please go to the dedicated [README.md](https://github.com/owkin/PyDESeq2/blob/main/docs/README.md) for information on how to do so.



### Data



The quick start examples use synthetic data, provided in this repo (see [datasets](https://github.com/owkin/PyDESeq2/blob/main/datasets/README.md).)



The experiments described in the [PyDESeq2 article](https://academic.oup.com/bioinformatics/article/39/9/btad547/7260507) rely on data

from [The Cancer Genome Atlas](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga),

which may be obtained from this [portal](https://portal.gdc.cancer.gov/).



## Contributing



Please the [Contributing](https://pydeseq2.readthedocs.io/en/latest/usage/contributing.html) section of the

documentation to see how you can contribute to PyDESeq2.



### 1 - Download the repository



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



### 2 - Create a conda environment



Run `conda create -n pydeseq2 python=3.9` (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 [ruff](https://docs.astral.sh/ruff/), [black](https://black.readthedocs.io/en/stable/), and [mypy](https://mypy.readthedocs.io/en/stable/).



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



## Development Roadmap



Here are some of the features and improvements we plan to implement in the future:



- [x] Integration to the [scverse](https://scverse.org/) ecosystem:

  * [x] Refactoring to use the [AnnData](https://anndata.readthedocs.io/) data structure

  * [x] Submitting a PR to be listed as an [scverse ecosystem](https://github.com/scverse/ecosystem-packages/) package

- [x] Variance-stabilizing transformation

- [ ] Improving multi-factor analysis:

  * [x] Allowing n-level factors

  * [x] Support for continuous covariates

  * [ ] Implementing interaction terms



## Citing this work



```

@article{muzellec2023pydeseq2,

  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={2023},

  doi = {10.1093/bioinformatics/btad547},

  journal={Bioinformatics},

}

```



## 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.

        



[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.

        



## License



PyDESeq2 is released under an [MIT license](https://github.com/owkin/PyDESeq2/blob/main/LICENSE).