Metadata-Version: 2.3
Name: rapids_singlecell
Version: 0.10.4
Summary: running single cell analysis on Nvidia GPUs
Project-URL: Documentation, https://rapids-singlecell.readthedocs.io
Project-URL: Source, https://github.com/scverse/rapids_singlecell
Author: Severin Dicks
License: MIT License
        
        Copyright (c) 2022 Severin Dicks
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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License-File: LICENSE
Requires-Python: >=3.8
Requires-Dist: anndata>=0.10.0
Requires-Dist: decoupler>=1.3.2
Requires-Dist: matplotlib>=3.4
Requires-Dist: natsort
Requires-Dist: numpy>=1.17.0
Requires-Dist: pandas
Requires-Dist: scanpy>=1.10.0
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Provides-Extra: rapids11
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Provides-Extra: test
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Requires-Dist: pytest; extra == 'test'
Requires-Dist: scanpy[test]>=1.10.0; extra == 'test'
Description-Content-Type: text/markdown

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# rapids-singlecell: GPU-Accelerated Single-Cell Analysis within scverse

Rapids-singlecell offers enhanced single-cell data analysis as a near drop-in replacement predominantly for scanpy, while also incorporating select functionalities from squidpy and decoupler. Utilizing GPU computing with cupy and Nvidia’s RAPIDS, it emphasizes high computational efficiency. As part of the scverse ecosystem, rapids-singlecell continuously aims to maintain compatibility, adapting and growing through community collaboration.

* **Broad GPU Optimization:** Facilitates accelerated processing of large datasets, with GPU-enabled AnnData objects.
* **Selective scverse Library Integration:** Incorporates key functionalities from scanpy, with additional features from squidpy and decoupler.
* **Easy Installation Process:** Available via Conda and PyPI, with detailed setup guidelines.
* **Accessible Documentation:** Provides comprehensive guides and examples tailored for efficient application.

Our commitment with rapids-singlecell is to deliver a powerful, user-centric tool that significantly enhances single-cell data analysis capabilities in bioinformatics.

## Documentation

For more information please have a look through the [documentation](https://rapids-singlecell.readthedocs.io/en/latest/)


## Citation

If you use this code, please cite: [![DOI](https://zenodo.org/badge/364573913.svg)](https://zenodo.org/badge/latestdoi/364573913)

Please also consider citing: [rapids-single-cell-examples](https://zenodo.org/badge/latestdoi/265649968) and  [scanpy](https://doi.org/10.1186/s13059-017-1382-0)

In addition to that please cite the methods' original research articles in the [scanpy documentation](https://scanpy.readthedocs.io/en/latest/references.html)

If you use the accelerated decoupler functions please cite [decoupler](https://doi.org/10.1093/bioadv/vbac016)
