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https://github.com/scverse/rapids_singlecell

Rapids_singlecell: A GPU-accelerated tool for scRNA analysis. Offers seamless scverse compatibility for efficient single-cell data processing and analysis.
https://github.com/scverse/rapids_singlecell

anndata bioinformatics gpu scverse single-cell

Last synced: 24 days ago
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Rapids_singlecell: A GPU-accelerated tool for scRNA analysis. Offers seamless scverse compatibility for efficient single-cell data processing and analysis.

Host: GitHub
URL: https://github.com/scverse/rapids_singlecell
Owner: scverse
License: mit
Created: 2021-05-05T12:46:51.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2024-04-12T11:50:37.000Z (3 months ago)
Last Synced: 2024-04-12T15:34:55.923Z (3 months ago)
Topics: anndata, bioinformatics, gpu, scverse, single-cell
Language: Python
Homepage: https://rapids-singlecell.readthedocs.io/
Size: 371 MB
Stars: 83
Watchers: 7
Forks: 14
Open Issues: 6
Metadata Files:
- Readme: README.md
- License: LICENSE

Lists

top-life-sciences - **scverse/rapids_singlecell** - accelerated tool for scRNA analysis. Offers seamless scverse compatibility for efficient single-cell data processing and analysis.<br>`anndata`, `bioinformatics`, `gpu`, `scverse`, `single-cell`<br><img src='https://github.com/HubTou/topgh/blob/main/icons/gstars.png'> 96 <img src='https://github.com/HubTou/topgh/blob/main/icons/forks.png'> 18 <img src='https://github.com/HubTou/topgh/blob/main/icons/code.png'> Python <img src='https://github.com/HubTou/topgh/blob/main/icons/license.png'> MIT License <img src='https://github.com/HubTou/topgh/blob/main/icons/last.png'> 2024-06-03 18:07:06 | (Ranked by starred repositories)
awesome-stars - rapids_singlecell - accelerated tool for scRNA analysis. Offers seamless scverse compatibility for efficient single-cell data processing and analysis. | scverse | 104 | (Python)

README

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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)