Welcome to the rapids-singlecell documentation

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Welcome to the rapids-singlecell documentation#

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.

Rapids-singlecell is part of the scverse® project (website, governance) and is fiscally sponsored by NumFOCUS. If you like scverse® and want to support our mission, 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.

News#

  • 07.08.23 rapids-singlecell is now part of scverse® ecosystem.

  • 04.08.23 Thanks to the great team at scverse® rapids-singlecell is now automatically tested with CI

  • 27.06.23 I’m very honored to announce that I was invited to co-author a technical blog post that demonstrates the capabilities and performance of rapids-singlecell for NVIDIA.

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