Cooperative integration of spatially resolved multi-omics data with COSMOS

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 COSM...

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Veröffentlicht in:Nature communications Jg. 16; H. 1; S. 27 - 10
Hauptverfasser: Zhou, Yuansheng, Xiao, Xue, Dong, Lei, Tang, Chen, Xiao, Guanghua, Xu, Lin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 02.01.2025
Nature Publishing Group
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45
631/114/2397
631/114/2401
Algorithms
Biological analysis
Data integration
Datasets
Design
Graph neural networks
Humanities and Social Sciences
Integration
multidisciplinary
Neural networks
Science
Science (multidisciplinary)
Software
Visualization
ISSN:2041-1723, 2041-1723
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Beschreibung
Zusammenfassung: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. Recent advancements in biological technologies have enabled the measurement of spatially resolved multi-omics data. Here, the authors present COSMOS and demonstrate its superior performance compared to existing methods for integrating spatially resolved multi-omics data.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-024-55204-y