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

Chen-Rui Xia, Zhi-Jie Cao () and Ge Gao ()
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Chen-Rui Xia: Peking University
Zhi-Jie Cao: Peking University
Ge Gao: Peking University

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-63140-8

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