Kalfon Jérémie, Samaran Jules, Peyré Gabriel, Cantini Laura
Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics group, F-75015, Paris, France.
CNRS and DMA de l'Ecole Normale Supérieure, CNRS, Ecole Normale Supérieure, Université PSL, 75005, Paris, France.
Nat Commun. 2025 Apr 16;16(1):3607. doi: 10.1038/s41467-025-58699-1.
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.
细胞受无数大分子相互作用的支配。推断这样一个相互作用网络一直是细胞生物学中难以实现的里程碑。基于大型基础模型的最新进展及其无监督学习能力，我们提出了scPRINT，这是一个用于推断基因网络的大型细胞模型，它在cellxgene数据库中超过5000万个细胞上进行了预训练。通过创新的预训练任务和模型架构，scPRINT在揭示细胞复杂生物学特性时，推动大型变压器模型朝着更高的可解释性和可用性发展。基于我们的图谱级基准，scPRINT在基因网络推理方面表现出优于现有技术的性能，以及在去噪、批次效应校正和细胞标签预测方面具有竞争力的零样本能力。在良性前列腺增生图谱上，scPRINT突出了离子交换、衰老和慢性炎症之间的深刻联系。
