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bioRxiv · 10.1101/2024.11.29.626126

High-fidelity disentangled cellular embeddings for large-scale heterogeneous spatial omics via DECIPHER

Xia, C.-R.·Cao, Z.-J.·Gao, G.
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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. Aiming to model large-scale heterogeneous spatial data in silico properly, DECIPHER produces high-fidelity disentangled embeddings, not only achieving superior performance in systematic benchmarks, but also empowering various real-world applications. We further demonstrated that DECIPHER is scalable to atlas-scale datasets, enabling global analysis which is largely infeasible to current state-of-the-arts.

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Xia, C.-R., Cao, Z.-J., Gao, G.. 2024-12-05. High-fidelity disentangled cellular embeddings for large-scale heterogeneous spatial omics via DECIPHER. https://doi.org/10.1101/2024.11.29.626126

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