SPARSE: Semantic-Preserving Adapter for Robust Spatial Embeddings

Spatial transcriptomic representations should incorporate local tissue context without erasing expression-derived cell-type structure. We introduce SPARSE, a parameter-efficient hierarchical adapter for frozen transcriptomic embeddings. Within each centered spatial neighborhood, SPARSE builds a multi-resolution token hierarchy through feature-guided token merging with coordinate propagation, spatial rotary position encoding, cross-resolution attention, and top-down fusion. Masked expression reconstruction encourages retention of molecular information. With scGPT as the main backbone, SPARSE improves spatial-domain identification on two held-out human-brain slices and the held-out final stage of a mouse-embryo dataset. Across four transcriptomic backbones, it retains or improves cell-type separability on a held-out mouse-heart slice. On a held-out mouse whole-brain section, the same fixed embeddings support cell-type label transfer, neighborhood-composition prediction, and cell-wise tissue-region classification. Controlled comparisons show gains beyond parameter-matched adapters and graph-Laplacian regularization. With approximately 5M trainable adapter parameters, SPARSE complements dedicated spatial pretraining.