SWINGOT: Spatially Weighted Integration of Multi-Omics with Graph Optimal Transport

Background: Spatial multi-omics technologies help study tissue organization and spatial molecular patterns, but integration remains challenging. We aimed to integrate paired spatial multi-omics data while preserving relationships among spatial spots. Methods: We developed SWINGOT (Spatially Weighted Integration of Multi-Omics with Graph Optimal Transport), a topology-aware framework combining Wasserstein-based distribution alignment and Gromov–Wasserstein-based relational regularization. A conservative residual gated fusion strategy integrates modality-specific representations while limiting structural changes. We evaluated SWINGOT on mouse embryo RNA–ATAC datasets and a human lymph-node RNA–ADT dataset using clustering, spatial-coherence, and cross-modal matching metrics. Gene-expression reconstruction used the modality-specific encoder–decoder pathway. Results: SWINGOT achieved strong performance in spatial-domain identification and spatial coherence compared with existing multimodal integration methods. Fused representations supported spatial-domain identification, low-dimensional visualization, and downstream molecular analyses. In the E18 dataset, reconstructed expression showed clearer and more spatially continuous patterns for representative genes. Human lymph-node analysis revealed distinct molecular patterns among spatial regions. Conclusions: SWINGOT provides a strategy for paired spatial multi-omics integration, supporting spatial-domain identification and the analysis of regional molecular differences.