<img src="docs/source/_static/pydeseq2_logo_green.png" width="600">
#
[](https://pypi.org/project/pydeseq2)
[](https://pepy.tech/project/pydeseq2)
[](https://anaconda.org/bioconda/pydeseq2)
[](LICENSE)
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.
<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](https://github.com/owkin/PyDESeq2/blob/main/LICENSE).
Raw data
{
"_id": null,
"home_page": null,
"name": "pydeseq2",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9.0",
"maintainer_email": null,
"keywords": null,
"author": "Boris Muzellec, Maria Telenczuk, Vincent Cabelli and Mathieu Andreux",
"author_email": "boris.muzellec@owkin.com",
"download_url": "https://files.pythonhosted.org/packages/7b/25/03897b33329ba0a4d10a5c725d1acd31ae8f44b79e387d2312a2764de32b/pydeseq2-0.4.12.tar.gz",
"platform": null,
"description": "<img src=\"docs/source/_static/pydeseq2_logo_green.png\" width=\"600\">\n\n#\n[](https://pypi.org/project/pydeseq2)\n[](https://pepy.tech/project/pydeseq2)\n[](https://anaconda.org/bioconda/pydeseq2)\n[](LICENSE)\n\nPyDESeq2 is a python implementation of the [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) \nmethod [1] for differential expression analysis (DEA) with bulk RNA-seq data, originally in R.\nIt aims to facilitate DEA experiments for python users.\n\nAs PyDESeq2 is a re-implementation of [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html) from \nscratch, you may experience some differences in terms of retrieved values or available features.\n\nCurrently, available features broadly correspond to the default settings of DESeq2 (v1.34.0) for single-factor and \nmulti-factor analysis (with categorical or continuous factors) using Wald tests.\nWe plan to implement more in the future.\nIn case there is a feature you would particularly like to be implemented, feel free to open an issue.\n\n## Table of Contents\n- [PyDESeq2](#pydeseq2)\n - [Table of Contents](#table-of-contents)\n - [Installation](#installation)\n - [Requirements](#requirements)\n - [Getting started](#getting-started)\n - [Documentation](#documentation)\n - [Data](#data)\n - [Contributing](#contributing)\n - [1 - Download the repository](#1---download-the-repository)\n - [2 - Create a conda environment](#2---create-a-conda-environment)\n - [Development roadmap](#development-roadmap)\n - [Citing this work](#citing-this-work)\n - [References](#references)\n - [License](#license)\n\n## Installation\n\n### PyPI\n\n`PyDESeq2` can be installed from PyPI using `pip`:\n\n`pip install pydeseq2`\n\nWe recommend installing within a conda environment:\n\n```\nconda create -n pydeseq2\nconda activate pydeseq2\nconda install pip\npip install pydeseq2\n```\n\n### Bioconda\n\n`PyDESeq2` can also be installed from Bioconda with `conda`:\n\n`conda install -c bioconda pydeseq2`\n\nIf you're interested in contributing or want access to the development version, please see the [contributing](#contributing) section.\n\n### Requirements\n\nThe list of package version requirements is available in `setup.py`.\n\nFor reference, the code is being tested in a github workflow (CI) with python\n3.9 to 3.11 and the following package versions:\n```\n- anndata 0.8.0\n- numpy 1.23.0\n- pandas 1.4.3\n- scikit-learn 1.1.1\n- scipy 1.11.0\n```\n\nPlease don't hesitate to open an issue in case you encounter any issue due to possible deprecations.\n\n\n## Getting started\n\nThe [Getting Started](https://pydeseq2.readthedocs.io/en/latest/auto_examples/index.html) section of the documentation\ncontains downloadable examples on how to use PyDESeq2.\n\n\n### Documentation\n\nThe documentation is hosted [here on ReadTheDocs](https://pydeseq2.readthedocs.io/en/latest/). \nIf you want to have the latest version of the documentation, you can build it from source.\nPlease go to the dedicated [README.md](https://github.com/owkin/PyDESeq2/blob/main/docs/README.md) for information on how to do so.\n\n### Data\n\nThe quick start examples use synthetic data, provided in this repo (see [datasets](https://github.com/owkin/PyDESeq2/blob/main/datasets/README.md).)\n\nThe experiments described in the [PyDESeq2 article](https://academic.oup.com/bioinformatics/article/39/9/btad547/7260507) rely on data\nfrom [The Cancer Genome Atlas](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga),\nwhich may be obtained from this [portal](https://portal.gdc.cancer.gov/).\n\n## Contributing\n\nPlease the [Contributing](https://pydeseq2.readthedocs.io/en/latest/usage/contributing.html) section of the\ndocumentation to see how you can contribute to PyDESeq2.\n\n### 1 - Download the repository\n\n`git clone https://github.com/owkin/PyDESeq2.git`\n\n### 2 - Create a conda environment\n\nRun `conda create -n pydeseq2 python=3.9` (or higher python version) to create the `pydeseq2` environment and then activate it:\n`conda activate pydeseq2`.\n\n`cd` to the root of the repo and run `pip install -e .\"[dev]\"` to install in developer mode.\n\nThen, run `pre-commit install`.\n\nThe `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/).\n\nPyDESeq2 is a living project and any contributions are welcome! Feel free to open new PRs or issues.\n\n## Development Roadmap\n\nHere are some of the features and improvements we plan to implement in the future:\n\n- [x] Integration to the [scverse](https://scverse.org/) ecosystem:\n * [x] Refactoring to use the [AnnData](https://anndata.readthedocs.io/) data structure\n * [x] Submitting a PR to be listed as an [scverse ecosystem](https://github.com/scverse/ecosystem-packages/) package\n- [x] Variance-stabilizing transformation\n- [ ] Improving multi-factor analysis:\n * [x] Allowing n-level factors\n * [x] Support for continuous covariates\n * [ ] Implementing interaction terms\n\n\n## Citing this work\n\n```\n@article{muzellec2023pydeseq2,\n title={PyDESeq2: a python package for bulk RNA-seq differential expression analysis},\n author={Muzellec, Boris and Telenczuk, Maria and Cabeli, Vincent and Andreux, Mathieu},\n year={2023},\n doi = {10.1093/bioinformatics/btad547},\n journal={Bioinformatics},\n}\n```\n\n## References\n\n[1] Love, M. I., Huber, W., & Anders, S. (2014). \"Moderated estimation of fold\n change and dispersion for RNA-seq data with DESeq2.\" Genome biology, 15(12), 1-21.\n <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8>\n\n[2] Zhu, A., Ibrahim, J. G., & Love, M. I. (2019).\n \"Heavy-tailed prior distributions for sequence count data:\n removing the noise and preserving large differences.\"\n Bioinformatics, 35(12), 2084-2092.\n <https://academic.oup.com/bioinformatics/article/35/12/2084/5159452>\n\n## License\n\nPyDESeq2 is released under an [MIT license](https://github.com/owkin/PyDESeq2/blob/main/LICENSE).\n\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "A python implementation of DESeq2.",
"version": "0.4.12",
"project_urls": null,
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "0ee4831483ae3fa850709f98be4ef3a5d1ba6a8ca193b99bcd280ed9e98307f8",
"md5": "780abb59b5e2ff84f4afcb86dd8e6076",
"sha256": "474233b87f1ccf8741d20c742661eea2075690d48981c16c418e221c996ccfc5"
},
"downloads": -1,
"filename": "pydeseq2-0.4.12-py3-none-any.whl",
"has_sig": false,
"md5_digest": "780abb59b5e2ff84f4afcb86dd8e6076",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9.0",
"size": 46145,
"upload_time": "2024-10-25T08:28:33",
"upload_time_iso_8601": "2024-10-25T08:28:33.113731Z",
"url": "https://files.pythonhosted.org/packages/0e/e4/831483ae3fa850709f98be4ef3a5d1ba6a8ca193b99bcd280ed9e98307f8/pydeseq2-0.4.12-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "7b2503897b33329ba0a4d10a5c725d1acd31ae8f44b79e387d2312a2764de32b",
"md5": "7354439b8ef7a5db19b0e8deb030a447",
"sha256": "dfe1051bd1639caef8be42761f4aa6e93fee0920bb0c35ef51fb454151644015"
},
"downloads": -1,
"filename": "pydeseq2-0.4.12.tar.gz",
"has_sig": false,
"md5_digest": "7354439b8ef7a5db19b0e8deb030a447",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.9.0",
"size": 51508,
"upload_time": "2024-10-25T08:28:34",
"upload_time_iso_8601": "2024-10-25T08:28:34.430850Z",
"url": "https://files.pythonhosted.org/packages/7b/25/03897b33329ba0a4d10a5c725d1acd31ae8f44b79e387d2312a2764de32b/pydeseq2-0.4.12.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-10-25 08:28:34",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "pydeseq2"
}
JSON