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Background <p>Cells are the fundamental units of life, and understanding their diversity and functionality requires detailed characterization. The rise of single-cell omics data enables this, yet current deep learning approaches lack multi-scale interpretability.</p> Results <p>We introduce Cell Decoder, a model that integrates biological prior knowledge to provide a multi-scale representation of cells. Using automated machine learning and post hoc analysis, Cell Decoder decodes cell identity and outperforms existing methods. It offers multi-view interpretability and facilitates data integration.</p> Conclusions <p>Applied to human bone and mouse embryonic data, Cell Decoder reveals the multi-scale heterogeneity of cell identities, providing a powerful framework for advancing our understanding of cellular diversity.</p>

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Cell Decoder: decoding cell identity with multi-scale explainable deep learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月20日

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  • Jun Zhu,
  • Zeyang Zhang,
  • Yujia Xiang,
  • Beini Xie,
  • Xinwen Dong,
  • Linhai Xie,
  • Peijie Zhou,
  • Rongyan Yao,
  • Xiaowen Wang,
  • Yang Li,
  • Fuchu He,
  • Wenwu Zhu,
  • Ziwei Zhang,
  • Cheng Chang

摘要

Background

Cells are the fundamental units of life, and understanding their diversity and functionality requires detailed characterization. The rise of single-cell omics data enables this, yet current deep learning approaches lack multi-scale interpretability.

Results

We introduce Cell Decoder, a model that integrates biological prior knowledge to provide a multi-scale representation of cells. Using automated machine learning and post hoc analysis, Cell Decoder decodes cell identity and outperforms existing methods. It offers multi-view interpretability and facilitates data integration.

Conclusions

Applied to human bone and mouse embryonic data, Cell Decoder reveals the multi-scale heterogeneity of cell identities, providing a powerful framework for advancing our understanding of cellular diversity.