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DECIPHER for learning disentangled cellular embeddings in large-scale heterogeneous spatial omics data

Author

Listed:
  • Chen-Rui Xia

    (Peking University
    Changping Laboratory)

  • Zhi-Jie Cao

    (Peking University
    Changping Laboratory)

  • Ge Gao

    (Peking University
    Changping Laboratory)

Abstract

The functional role of a cell, shaped by the sophisticated interplay between its molecular identity and spatial context, is often obscured in current spatial modeling. In efforts to model large-scale heterogeneous spatial data in silico effectively and efficiently, we introduce DECIPHER, which disentangles cells’ intra-cellular and extra-cellular representation through a novel cross-scale contrast learning strategy. In addition to superior performance over state-of-arts, systematic benchmarks and various real-world case studies showed that the disentangled embeddings produced by DECIPHER enable delineating cell-environment interaction across multiple scales. Of note, DECIPHER is highly scalable, capable of handling spatial atlases with millions of cells which is largely infeasible for existing methods.

Suggested Citation

  • Chen-Rui Xia & Zhi-Jie Cao & Ge Gao, 2025. "DECIPHER for learning disentangled cellular embeddings in large-scale heterogeneous spatial omics data," Nature Communications, Nature, vol. 16(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63140-8
    DOI: 10.1038/s41467-025-63140-8
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