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scPRINT: pre-training on 50 million cells allows robust gene network predictions

Author

Listed:
  • Jérémie Kalfon

    (Machine Learning for Integrative Genomics group)

  • Jules Samaran

    (Machine Learning for Integrative Genomics group)

  • Gabriel Peyré

    (Université PSL)

  • Laura Cantini

    (Machine Learning for Integrative Genomics group)

Abstract

A cell is governed by the interaction of myriads of macromolecules. Inferring such a network of interactions has remained an elusive milestone in cellular biology. Building on recent advances in large foundation models and their ability to learn without supervision, we present scPRINT, a large cell model for the inference of gene networks pre-trained on more than 50 million cells from the cellxgene database. Using innovative pretraining tasks and model architecture, scPRINT pushes large transformer models towards more interpretability and usability when uncovering the complex biology of the cell. Based on our atlas-level benchmarks, scPRINT demonstrates superior performance in gene network inference to the state of the art, as well as competitive zero-shot abilities in denoising, batch effect correction, and cell label prediction. On an atlas of benign prostatic hyperplasia, scPRINT highlights the profound connections between ion exchange, senescence, and chronic inflammation.

Suggested Citation

  • Jérémie Kalfon & Jules Samaran & Gabriel Peyré & Laura Cantini, 2025. "scPRINT: pre-training on 50 million cells allows robust gene network predictions," Nature Communications, Nature, vol. 16(1), pages 1-23, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58699-1
    DOI: 10.1038/s41467-025-58699-1
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    References listed on IDEAS

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