Graph-Aware Latent Representation Learning for Multimodal Spatial Omics Integration.
Multimodal spatial omics integration offers a powerful paradigm to decipher the hierarchical regulatory mechanisms underlying cellular function and tissue architecture. In this study, a novel method of multimodal spatial omics fusion, named mmspao, is proposed to obtain cross-modal interactive features and combine them with the features of each modality to obtain fusion results at the spatial resolution. This method integrates the data of spatial transcriptomics, epigenomics, and proteomics. It further integrates information from each modality using adjacency graph modeling and latent spatial representation. We demonstrate the effectiveness of this method on simulated and real multiomics data. By leveraging adjacency graph modeling and latent spatial representation, mmspao effectively aligns multiomics modalities within shared spatial domains, preserving individual gene expression profiles while generating globally integrated fusion maps that advance the decoding of tissue architecture and cellular regulatory hierarchies.