Spartan: activation-aware framework for spatial domain and variable gene discovery

Abstract Spatial transcriptomics is rapidly advancing toward single-cell-level resolution, revealing complex tissue architectures organized across continuous anatomical gradients. However, accurate identification of spatial domains remains a central computational challenge, as many existing clustering approaches blur anatomical boundaries, merge transitional zones, or fail to resolve localized microstructures. Here we introduce Spartan, an activation-aware multiplex graph framework for high-resolution domain discovery. Spartan integrates spatial topology and Local Spatial Activation (LSA), a neighborhood deviation signal that captures localized transcriptional heterogeneity often attenuated by similarity-based clustering. By jointly modeling cohesion within domains and localized activation structure, Spartan recovers anatomically aligned partitions across spatially resolved transcriptomics technologies including Visium HD, MERFISH, Stereo-seq, and STARmap. We further demonstrate its utility in a high-resolution Visium HD section of developing human esophagus and stomach, where activation-aware graph integration enables precise delineation of complex transitional regions such as the gastroesophageal junction and supports stable multi-scale domain recovery without fragile hyperparameter tuning. Beyond domain identification, Spartan leverages activation-aware structure to detect spatially variable genes associated with localized tissue remodeling. Spartan scales near-linearly with dataset size, providing a robust and interpretable framework for spatial systems-level analysis. Competing Interest Statement The authors have declared no competing interest. Footnotes Refinement of the manuscript title. Figure 1 is refined and LSA conceptual figure added to explain LSA clearly Figure 2 is refined to give a clear Spartan overview Figure 2 is further refined to clearly explain the different components of Spartan. Refinement and text improvements of Abstract, Introduction, Methods, Results and Discussion. Additional explanation, validation experiments and results for LSA graph and SAQ metric added to the manuscript. Additional pairwise statistical tests added for various sub-domains in developing oesophagus spatial transcriptomics data added. Addition of supplementary methods and figures related to validation experiments in the supplementary file.