scPRINT: pre-training on 50 million cells allows robust gene network predictions
Jérémie Kalfon1, Jules Samaran1, Gabriel Peyré2
1Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics group, F-75015, Paris, France.
Nature Communications
|April 16, 2025
Summary
We developed scPRINT, a large cell model, to infer gene networks from millions of cells. This advanced tool improves understanding of cellular biology and gene interactions.
Area of Science:
- Computational Biology
- Genomics
- Systems Biology
Background:
- Cellular processes are driven by complex macromolecular interactions.
- Inferring these gene networks is a significant challenge in cellular biology.
Purpose of the Study:
- To introduce scPRINT, a large cell model for gene network inference.
- To leverage foundation models for enhanced interpretability and usability in uncovering cellular biology.
Main Methods:
- Pre-training scPRINT on over 50 million cells from the cellxgene database.
- Utilizing innovative pretraining tasks and a novel model architecture.
- Employing large transformer models for biological data analysis.
Main Results:
- scPRINT demonstrates superior performance in gene network inference compared to state-of-the-art methods.
- Achieved competitive zero-shot abilities in denoising, batch effect correction, and cell label prediction.
- Highlighted connections between ion exchange, senescence, and chronic inflammation in benign prostatic hyperplasia.
Conclusions:
- scPRINT advances the capability of large transformer models for biological network inference.
- The model offers improved interpretability and usability for complex cellular biology research.
- scPRINT provides novel insights into disease mechanisms, such as in benign prostatic hyperplasia.
