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scMultiBench

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Multitask benchmarking of single-cell multimodal omics integration methods, with multibench: a Python API that runs 36 integration methods across four categories (vertical, diagonal, mosaic, cross), scores them with scIB metrics, and draws scIB-style bubble tables.

Documentation and tutorials: https://dsichang.github.io/scMultiBench/

Citation

Liu C, Ding S, Kim HJ, Long S, Xiao D, Ghazanfar S, Yang P. Multitask benchmarking of single-cell multimodal omics integration methods. Nature Methods 22, 2449-2460 (2025). https://doi.org/10.1038/s41592-025-02856-3

Each method you run has its own paper; please cite it alongside the benchmark. print(mtb.cite("Matilda", "MOFA2")) prints the benchmark's reference and one line per method (multibench cite Matilda MOFA2 for BibTeX).

The benchmark and the method scripts live in PYangLab/scMultiBench.

Metadata

Release files for multibench-sc 0.3.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for multibench-sc 0.3.6
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Built distribution (wheel)

Table of built distributions (wheels) for multibench-sc 0.3.6
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multibench_sc-0.3.6-py3-none-any.whl Python 3 none any Details

Total release size: 1.1 MB

Release files / multibench_sc-0.3.6.tar.gz

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