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 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.