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

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.