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

Abstract

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.