Mann-Krzisnik, D.; Li, Y.

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 teacher's 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. Accepted at the CIBB 2026 conference (https://cibb2026.teralab.ai/)