Deciphering spatial domains from spatial multi-omics with SpatialGlue

Abstract Integration of multiple data modalities in a spatially informed manner remains an unmet need for exploiting spatial multi-omics data. Here, we introduce SpatialGlue, a novel graph neural network with dual-attention mechanism, to decipher spatial domains by capturing the significance of each modality and neighbor graph in cross-omics and intra-omics integration. We demonstrate that SpatialGlue can accurately aggregate cell types into spatial domains at a higher resolution across different tissue types and technology platforms, as well as gain biological insights into cross-modality spatial correlations.