References

References#

Citation#

If you are using this package for you research and find it helpful, please use this reference:

Kühn, Damin, and Michael T. Schaub. "Global Ground Metric Learning with Applications to scRNA data." Proceedings of the 28th International Conference on Artificial Intelligence and Statistics. PMLR, 2025.

In BibTeX format:

  @InProceedings{kuehn2025ggml,
    title     = {Global Ground Metric Learning with Applications to {scRNA} Data},
    author    = {K{\"u}hn, Damin and Schaub, Michael T.},
    booktitle = {Proceedings of the 28th International Conference on Artificial Intelligence and Statistics},
    pages     = {3295--3303},
    year      = {2025},
    volume    = {258},
    series    = {Proceedings of Machine Learning Research},
    month     = may,
    publisher = {PMLR},
    url       = {https://proceedings.mlr.press/v258/kuhn25a.html}
    }

Used datasets:#

  • Myocardial Infarction: Kuppe, Christoph, et al., “Spatial multi-omic map of human myocardial infarction.” Nature (2022)

  • Breast Cancer: Kumar, Tapsi, et al., “A spatially resolved single-cell genomic atlas of the adult human breast.” Nature (2023)

Helpful packages:#

  • Pytorch: Paszke, Adam, et al. “Pytorch: An imperative style, high-performance deep learning library.” NeurIPS (2019)

  • Python Optimal Transport: Flamary, Rémi, et al., “POT Python Optimal Transport library” JMLR (2021)

  • Numpy: Harris, C.R., et al., “Array programming with NumPy.” Nature (2020)

  • Scanpy: Wolf, Alexander, et al. “SCANPY: large-scale single-cell gene expression data analysis”, Genome Biology (2018)