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

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.

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Publication Details

Journal
Nature Communications
Published
2025-10-24
DOI
https://doi.org/10.1038/s41467-025-63418-x
Citations
3
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
2.42

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article

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

Ji Qi, Zhixiang Lin, Jingxue Xin, Can Yang et al.
3 citations
Nature Communications
Single-cell and spatial transcriptomics
2.42
article

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

Ji Qi, Zhixiang Lin, Jingxue Xin, Can Yang, Jia-Juan Tu, Xiaocheng Zhou, Muyang Ge, Jianhua Miao, Jinzhao Li, Ying Zhu
article en
3 citations

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.

Nature CommunicationsVol. 16(1)
Chinese University of Hong Kong (HK), Hong Kong University of Science and Technology (HK), HKUST Shenzhen Research Institute (CN), City University of Hong Kong, Shenzhen Research Institute (CN), Huashan Hospital (CN), Hubei University of Technology (CN), University of Hong Kong (HK)
Yale University, Research Grants Council, University Grants Committee, Innovation and Technology Commission, Central China Normal University, Hong Kong University of Science and Technology, Division of Mathematical Sciences
Openalex Percentile: Top 10%
Single-cell and spatial transcriptomics
2.42
DOIOpen Access PDFOpenAlex
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