Cooperative integration of spatially resolved multi-omics data with COSMOS

Yuansheng Zhou; Xue Xiao; Lei Dong; Chen Tang; Guanghua Xiao; Lin Xu

doi:10.1038/s41467-024-55204-y

Cooperative integration of spatially resolved multi-omics data with COSMOS

Yuansheng Zhou, Xue Xiao, Lei Dong, Chen Tang, Guanghua Xiao, Lin Xu

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

Recent advancements in biological technologies have enabled the measurement of spatially resolved multi-omics data, yet computational algorithms for this purpose are scarce. Existing tools target either single omics or lack spatial integration. We generate a graph neural network algorithm named COSMOS to address this gap and demonstrated the superior performance of COSMOS in domain segmentation, visualization, and spatiotemporal map for spatially resolved multi-omics data integration tasks.

Original language	English (US)
Article number	27
Journal	Nature communications
Volume	16
Issue number	1
DOIs	https://doi.org/10.1038/s41467-024-55204-y
State	Published - Dec 2025

ASJC Scopus subject areas

General Chemistry
General Biochemistry, Genetics and Molecular Biology
General Physics and Astronomy

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10.1038/s41467-024-55204-y

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abstract = "Recent advancements in biological technologies have enabled the measurement of spatially resolved multi-omics data, yet computational algorithms for this purpose are scarce. Existing tools target either single omics or lack spatial integration. We generate a graph neural network algorithm named COSMOS to address this gap and demonstrated the superior performance of COSMOS in domain segmentation, visualization, and spatiotemporal map for spatially resolved multi-omics data integration tasks.",

author = "Yuansheng Zhou and Xue Xiao and Lei Dong and Chen Tang and Guanghua Xiao and Lin Xu",

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AU - Dong, Lei

AU - Tang, Chen

AU - Xiao, Guanghua

AU - Xu, Lin

N1 - Publisher Copyright: © The Author(s) 2024.

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Y1 - 2025/12

N2 - Recent advancements in biological technologies have enabled the measurement of spatially resolved multi-omics data, yet computational algorithms for this purpose are scarce. Existing tools target either single omics or lack spatial integration. We generate a graph neural network algorithm named COSMOS to address this gap and demonstrated the superior performance of COSMOS in domain segmentation, visualization, and spatiotemporal map for spatially resolved multi-omics data integration tasks.

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Cooperative integration of spatially resolved multi-omics data with COSMOS

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