Spatially multimodal and multiscale network for representation learning from spatial multi-omics

Spatial multi-omics techniques provide powerful tools to decipher tissue architectures in multilayer perspectives, including gene expressions, gene regulations and microenvironments. The multimodal data integration is a critical step in spatial multi-omics data processing involved in many downstream analyses, such as spatial domain clustering. Thus, it is important to learn reliable latent representation by efficiently integrating the spatial multi-omics for downstream analyses. In this work, we developed a Spatially Multimodal and Multiscale Network (SpaMM-Net) to learn the latent representations from spatial multi-omics data. Due to the intrinsic noises and the complex relationship among multiple omics, SpaMM-Net utilized spatially-guided multiscale graph attention networks to integrate the multimodal omics features at different spatial-scale levels for representation learning. We evaluated our method for downstream spatial clustering task on several spatial multi-omics datasets. The results show that SpaMM-Net achieves good performance in deciphering both detailed regions and tissue architectures. Our scale-wise weight analyses further reveal that the SpaMM-Net effectively leverages multiscale spatial information to enhance the robustness of the learned representations. SpaMM-Net is an efficient tool to capture and integrate latent representations from spatial multi-omics for spatial domain identification. This strategy can be extended to other multi-omics modalities with the rapid development of spatial multi-omics technologies.