Topography-aware optimal transport for alignment of spatial omics data

Recent advances in spatial omics technologies have provided unprecedented insight into tissue spatial organization, but challenges remain in aligning spatial slices and integrating complementary single-cell and spatial data. Here, we propose TOAST (topography-aware optimal alignment of spatially resolved tissues), an optimal transport (OT)-based framework that extends the classical fused Gromov-Wasserstein (FGW) objective to more comprehensively model the heterogeneity of local molecular interactions. By introducing "spatial coherence," quantified through the entropy of local neighborhoods, and "neighborhood consistency," which preserves the expression profiles of neighboring spots, TOAST's objective function improves the alignment of spatially resolved tissue slices and the mapping between single-cell and spatial data. Through comprehensive evaluations, we demonstrate that our method consistently outperforms traditional FGW and other OT-based alignment methods. By integrating spatial constraints into OT, our framework provides a principled approach to enhance the biological interpretability of spatially resolved omics data and facilitate multimodal data integration.