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Cooperative integration of spatially resolved multi-omics data with COSMOS

Author

Listed:
  • Yuansheng Zhou

    (University of Texas Southwestern Medical Center)

  • Xue Xiao

    (University of Texas Southwestern Medical Center)

  • Lei Dong

    (University of Texas Southwestern Medical Center)

  • Chen Tang

    (University of Texas Southwestern Medical Center)

  • Guanghua Xiao

    (University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

  • Lin Xu

    (University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

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.

Suggested Citation

  • Yuansheng Zhou & Xue Xiao & Lei Dong & Chen Tang & Guanghua Xiao & Lin Xu, 2025. "Cooperative integration of spatially resolved multi-omics data with COSMOS," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55204-y
    DOI: 10.1038/s41467-024-55204-y
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    References listed on IDEAS

    as
    1. Honglei Ren & Benjamin L. Walker & Zixuan Cang & Qing Nie, 2022. "Identifying multicellular spatiotemporal organization of cells with SpaceFlow," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. S. Vickovic & B. Lötstedt & J. Klughammer & S. Mages & Å Segerstolpe & O. Rozenblatt-Rosen & A. Regev, 2022. "SM-Omics is an automated platform for high-throughput spatial multi-omics," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
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