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

Jérémie Kalfon, Jules Samaran, Gabriel Peyré and Laura Cantini ()
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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

Nature Communications, 2025, vol. 16, issue 1, 1-23

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-58699-1

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