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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月27日

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  • Chen-Rui Xia,
  • Zhi-Jie Cao,
  • Ge Gao

摘要

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.