Cooperative integration of spatially resolved multi-omics data with COSMOS - Zhou, Yuansheng; Xiao, Xue; Dong, Lei; Tang, Chen; Xiao, Guanghua; Xu, Lin (Nature communications, 2025)
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.