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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.

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

Installation

Conda

The easiest way to install rapids-singlecell is to use one of the yaml file provided in the conda folder. These yaml files install everything needed to run the example notbooks and get you started.

conda env create -f conda/rsc_rapids_24.02.yml
# or
mamba env create -f conda/rsc_rapids_23.12.yml

PyPI

pip install rapids-singlecell

The default installer doesn't cover RAPIDS nor cupy. Information on how to install RAPIDS & cupy can be found here.

If you want to use RAPIDS PyPI packages, the whole library with all dependencies can be install with:

pip install 'rapids-singlecell[rapids11]' --extra-index-url=https://pypi.nvidia.com #CUDA11.X
pip install 'rapids-singlecell[rapids12]' --extra-index-url=https://pypi.nvidia.com #CUDA12

It is important to ensure that the CUDA environment is set up correctly so that RAPIDS and Cupy can locate the necessary libraries.

Documentation

Please have a look through the documentation

Citation

If you use this code, please cite: DOI

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