MultiGATE: integrative analysis and regulatory inference in spatial multi-omics data via graph representation learning

Jishuai Miao, Jinzhao Li, Jingxue Xin, Jiajuan Tu, Muyang Ge, Ji Qi, Xiaocheng Zhou, Ying Zhu, Can Yang () and Zhixiang Lin ()
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Jishuai Miao: The Chinese University of Hong Kong
Jinzhao Li: The Chinese University of Hong Kong
Jingxue Xin: The Chinese University of Hong Kong
Jiajuan Tu: Hubei University of Technology
Muyang Ge: The Chinese University of Hong Kong
Ji Qi: The Chinese University of Hong Kong
Xiaocheng Zhou: The Chinese University of Hong Kong
Ying Zhu: Fudan University
Can Yang: The Hong Kong University of Science and Technology
Zhixiang Lin: The Chinese University of Hong Kong

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

Abstract: Abstract New spatial multi-omics technologies, which jointly profile transcriptome and epigenome/protein markers for the same tissue section, expand the frontiers of spatial techniques. Here, we introduce MultiGATE, which utilizes a two-level graph attention auto-encoder to integrate the multi-modality and spatial information in spatial multi-omics data. The key feature of MultiGATE is that it simultaneously performs embedding of the spatial pixels and infers the cross-modality regulatory relationship, which allows deeper data integration and provides insights on transcriptional regulation. We evaluate the performance of MultiGATE on spatial multi-omics datasets obtained from different tissues and platforms. Through effectively integrating spatial multi-omics data, MultiGATE both enhances the extraction of latent embeddings of the pixels and boosts the inference of transcriptional regulation for cross-modality genomic features.

Date: 2025
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DOI: 10.1038/s41467-025-63418-x

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