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Cell Decoder: decoding cell identity with multi-scale explainable deep learning
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DOI：10.1186/s13059-025-03832-y.png)
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Abstract
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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. 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. 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.
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