scib-rapids

scib-rapids#

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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 in a conda environment with RAPIDS pre-installed.

  1. Install the latest release on PyPI:

pip install scib-rapids

  1. Install the latest development version:

pip install 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}
}