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.
For more information please have a look through the documentation
If you use this code, please cite:
Please also consider citing: rapids-single-cell-examples and scanpy
In addition to that please cite the methods' original research articles in the scanpy documentation
If you use the accelerated decoupler functions please cite decoupler