SpatialJEPA: JEPA-inspired graph-context distillation for spatially aware multiomics integration

Abstract

Computational frameworks for integrating spatial genomics modalities extend cell-based representation learning across molecular layers, but many paired RNA-ATAC datasets are dissociated and lack spatial coordinates. We introduce SpatialJEPA, a JEPA-inspired teacher-student framework for transferring spatial context from spatial multiomics data to non-spatial multiome data. In contrast to patch- or feature-masking objectives, SpatialJEPA masks spatial context by replacing the teachers spatial neighborhood graph with a self-only identity graph during student training, making the spatial sample appear dissociated to the student. The student learns to match teacher embeddings from this graph-context-restricted view and can therefore be applied to dissociated RNA-ATAC data at inference time. In mouse brain multiomics, the resulting representation supports source-target alignment, recovers spatially organized transcriptomic and chromatin-accessibility programs, and shows concordance with ligand-receptor pathway structure compared with non-spatial references.