| Name | scvi-tools JSON |
| Version |
1.2.1
JSON |
| download |
| home_page | None |
| Summary | Deep probabilistic analysis of single-cell omics data. |
| upload_time | 2024-12-04 11:24:04 |
| maintainer | None |
| docs_url | None |
| author | The scvi-tools development team |
| requires_python | >=3.10 |
| license | BSD 3-Clause License Copyright (c) 2024, Adam Gayoso, Romain Lopez, Martin Kim, Pierre Boyeau, Nir Yosef Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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No coveralls.
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<a href="https://scvi-tools.org/">
<img
src="https://github.com/scverse/scvi-tools/blob/main/docs/_static/scvi-tools-horizontal.svg?raw=true"
width="400"
alt="scvi-tools"
>
</a>
[![Stars][gh-stars-badge]][gh-stars-link]
[![PyPI][pypi-badge]][pypi-link]
[![PyPIDownloads][pepy-badge]][pepy-link]
[![CondaDownloads][conda-badge]][conda-link]
[![Docs][docs-badge]][docs-link]
[![Build][build-badge]][build-link]
[![Coverage][coverage-badge]][coverage-link]
[scvi-tools] (single-cell variational inference tools) is a package for probabilistic modeling and
analysis of single-cell omics data, built on top of [PyTorch] and [AnnData].
# Analysis of single-cell omics data
scvi-tools is composed of models that perform many analysis tasks across single-cell, multi, and
spatial omics data:
- Dimensionality reduction
- Data integration
- Automated annotation
- Factor analysis
- Doublet detection
- Spatial deconvolution
- and more!
In the [user guide], we provide an overview of each model. All model implementations have a
high-level API that interacts with [Scanpy] and includes standard save/load functions, GPU
acceleration, etc.
# Rapid development of novel probabilistic models
scvi-tools contains the building blocks to develop and deploy novel probablistic models. These
building blocks are powered by popular probabilistic and machine learning frameworks such as
[PyTorch Lightning] and [Pyro]. For an overview of how the scvi-tools package is structured, you
may refer to the [codebase overview] page.
We recommend checking out the [skeleton repository] as a starting point for developing and
deploying new models with scvi-tools.
# Basic installation
For conda,
```bash
conda install scvi-tools -c conda-forge
```
and for pip,
```bash
pip install scvi-tools
```
Please be sure to install a version of [PyTorch] that is compatible with your GPU (if applicable).
# Resources
- Tutorials, API reference, and installation guides are available in the [documentation].
- For discussion of usage, check out our [forum].
- Please use the [issues] to submit bug reports.
- If you'd like to contribute, check out our [contributing guide].
- If you find a model useful for your research, please consider citing the corresponding
publication.
# Reference
If you use `scvi-tools` in your work, please cite
> **A Python library for probabilistic analysis of single-cell omics data**
>
> Adam Gayoso, Romain Lopez, Galen Xing, Pierre Boyeau, Valeh Valiollah Pour Amiri, Justin Hong,
> Katherine Wu, Michael Jayasuriya, Edouard Mehlman, Maxime Langevin, Yining Liu, Jules Samaran,
> Gabriel Misrachi, Achille Nazaret, Oscar Clivio, Chenling Xu, Tal Ashuach, Mariano Gabitto,
> Mohammad Lotfollahi, Valentine Svensson, Eduardo da Veiga Beltrame, Vitalii Kleshchevnikov,
> Carlos Talavera-López, Lior Pachter, Fabian J. Theis, Aaron Streets, Michael I. Jordan,
> Jeffrey Regier & Nir Yosef
>
> _Nature Biotechnology_ 2022 Feb 07. doi: [10.1038/s41587-021-01206-w](https://doi.org/10.1038/s41587-021-01206-w).
along with the publicaton describing the model used.
You can cite the scverse publication as follows:
> **The scverse project provides a computational ecosystem for single-cell omics data analysis**
>
> Isaac Virshup, Danila Bredikhin, Lukas Heumos, Giovanni Palla, Gregor Sturm, Adam Gayoso,
> Ilia Kats, Mikaela Koutrouli, Scverse Community, Bonnie Berger, Dana Pe’er, Aviv Regev,
> Sarah A. Teichmann, Francesca Finotello, F. Alexander Wolf, Nir Yosef, Oliver Stegle &
> Fabian J. Theis
>
> _Nature Biotechnology_ 2023 Apr 10. doi: [10.1038/s41587-023-01733-8](https://doi.org/10.1038/s41587-023-01733-8).
scvi-tools is part of the scverse project ([website](https://scverse.org),
[governance](https://scverse.org/about/roles)) and is fiscally sponsored by [NumFOCUS]. Please
consider making a tax-deductible [donation] to help the project pay for developer time,
professional services, travel, workshops, and a variety of other needs.
<a href="https://numfocus.org/project/scverse">
<img
src="https://raw.githubusercontent.com/numfocus/templates/master/images/numfocus-logo.png"
width="200"
>
</a>
[anndata]: https://anndata.readthedocs.io/en/latest/
[build-badge]: https://github.com/scverse/scvi-tools/actions/workflows/build.yml/badge.svg
[build-link]: https://github.com/scverse/scvi-tools/actions/workflows/build.yml/
[codebase overview]: https://docs.scvi-tools.org/en/stable/user_guide/background/codebase_overview.html
[conda-badge]: https://img.shields.io/conda/dn/conda-forge/scvi-tools?logo=Anaconda
[conda-link]: https://anaconda.org/conda-forge/scvi-tools
[contributing guide]: https://docs.scvi-tools.org/en/stable/contributing/index.html
[coverage-badge]: https://codecov.io/gh/scverse/scvi-tools/branch/main/graph/badge.svg
[coverage-link]: https://codecov.io/gh/scverse/scvi-tools
[docs-badge]: https://readthedocs.org/projects/scvi/badge/?version=latest
[docs-link]: https://scvi.readthedocs.io/en/stable/?badge=stable
[documentation]: https://docs.scvi-tools.org/
[donation]: https://numfocus.org/donate-to-scverse
[forum]: https://discourse.scvi-tools.org
[gh-stars-badge]: https://img.shields.io/github/stars/scverse/scvi-tools?style=flat&logo=GitHub&color=blue
[gh-stars-link]: https://github.com/scverse/scvi-tools/stargazers
[issues]: https://github.com/scverse/scvi-tools/issues
[numfocus]: https://numfocus.org/
[pepy-badge]: https://static.pepy.tech/badge/scvi-tools
[pepy-link]: https://pepy.tech/project/scvi-tools
[pypi-badge]: https://img.shields.io/pypi/v/scvi-tools.svg
[pypi-link]: https://pypi.org/project/scvi-tools
[pyro]: https://pyro.ai/
[pytorch]: https://pytorch.org
[pytorch lightning]: https://lightning.ai/docs/pytorch/stable/
[scanpy]: http://scanpy.readthedocs.io/
[scvi-tools]: https://scvi-tools.org/
[skeleton repository]: https://github.com/scverse/simple-scvi
[user guide]: https://docs.scvi-tools.org/en/stable/user_guide/index.html
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