CellMemory: hierarchical interpretation of out-of-distribution cells using bottlenecked transformer
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13059_2025_Article_3638.pdf
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Author(s)
Wang, Qifei
Zhu, He
Hu, Yiwen
Chen, Yanjie
Wang, Yuwei
Li, Guochao
Li, Yun
Chen, Jinfeng
Zhang, Xuegong
Zou, James
Date Issued
June 23, 2025
Journal
Genome Biology
Publisher
BioMed Central
Citation
Wang, Q., Zhu, H., Hu, Y. et al. CellMemory: hierarchical interpretation of out-of-distribution cells using bottlenecked transformer. Genome Biol 26, 178 (2025).
Version
Final published version
Abstract
Machine learning methods, especially Transformer architectures, have been widely employed in single-cell omics studies. However, interpretability and accurate representation of out-of-distribution (OOD) cells remains challenging. Inspired by the global workspace theory in cognitive neuroscience, we introduce CellMemory, a bottlenecked Transformer with improved generalizability designed for the hierarchical interpretation of OOD cells. Without pre-training, CellMemory outperforms existing single-cell foundation models and accurately deciphers spatial transcriptomics at high resolution. Leveraging its robust representations, we further elucidate malignant cells and their founder cells across patients, providing reliable characterizations of the cellular changes caused by the disease.
MIT Department
Broad Institute of MIT and Harvard
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivatives
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s13059-025-03638-y
