scib-rapids
GPU-accelerated metrics for benchmarking single-cell integration outputs using RAPIDS (cuML, CuPy).
This package provides the same metrics as scib-metrics but replaces JAX with RAPIDS (CuPy, cuML) for GPU acceleration. All implementations leverage CuPy for device-level computation on NVIDIA GPUs.
Metrics
- Silhouette:
silhouette_label,silhouette_batch,bras - LISI:
lisi_knn,ilisi_knn,clisi_knn - kBET:
kbet,kbet_per_label - Clustering:
nmi_ari_cluster_labels_kmeans,nmi_ari_cluster_labels_leiden - Graph connectivity:
graph_connectivity - Isolated labels:
isolated_labels - PCR comparison:
pcr_comparison
Getting started
Please refer to the documentation.
Installation
You need to have Python 3.11 or newer and a CUDA-capable GPU.
We recommend installing rapids-singlecell and scib-rapids using uv pip rather than conda, as conda often causes dependency conflicts (e.g. cupy vs cupy-cuda12x).
uv pip install rapids-singlecell scib-rapids
Alternatively, install the latest development version:
uv pip install rapids-singlecell git+https://github.com/maarten-devries/scib-rapids.git@main
Release notes
See the changelog.
Contact
For questions and help requests, you can reach out in the scverse Discourse. If you found a bug, please use the issue tracker.
Citation
If you use scib-rapids, please cite the original single-cell integration benchmarking work:
@article{luecken2022benchmarking,
title={Benchmarking atlas-level data integration in single-cell genomics},
author={Luecken, Malte D and B{\"u}ttner, Maren and Chaichoompu, Kridsadakorn and Danese, Anna and Interlandi, Marta and M{\"u}ller, Michaela F and Strobl, Daniel C and Zappia, Luke and Dugas, Martin and Colom{\'e}-Tatch{\'e}, Maria and others},
journal={Nature methods},
volume={19},
number={1},
pages={41--50},
year={2022},
publisher={Nature Publishing Group}
}
1 maintainer
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