Authors
Zhenchao Tang, Haohuai He, Shouzhi Chen, Jun Zhu, Tianxu Lv, Jiale Zhou, Jiehui Huang, Yaokun Li, Guanxing Chen, Linlin You, Calvin Yu-Chian Chen
Published in
Science advances. Volume 12. Issue 35. Pages eaef0286. Aug 28, 2026. Epub Aug 28, 2026.
Abstract
The rapid accumulation of single-cell data has made it possible to comprehensively characterize biological systems at molecular, cellular, and donor levels. However, scalable reference mapping across different resolutions remains a major challenge in current research. Here, we propose scProtoTransformer, a prototype-based Transformer architecture designed to achieve scalable reference mapping across molecular, cell, and donor levels. scProtoTransformer introduces a knowledge-guided prototype tokenizer that projects gene expression into biologically interpretable pathway prototypes, effectively reducing numerical batch effects while preserving biological semantic patterns. Furthermore, by leveraging knowledge distilled from the foundation model and a dynamic supervised fine-tuning strategy, scProtoTransformer achieves robust biological representations with reduced pretraining requirements. Benchmark experiments across molecular, cell, and donor-level reference mapping demonstrate that scProtoTransformer delivers competitive or even superior performance compared with state-of-the-art approaches while providing interpretability through biological prototypes. Together, these results establish scProtoTransformer as a unified framework for scalable reference mapping, laying the foundation for systematic understanding from genes to individuals.
PMID:
42664347
Bibliographic data and abstract were imported from PubMed on 29 Aug 2026.
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