Spartan: Spatial Activation Aware Transcriptomic Analysis Network

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 that explicitly models spatial transitions for high-resolution domain discovery. Spartan integrates spatial topology, Local Spatial Activation (LSA), a neighborhood deviation signal that amplifies localized transcriptomic shifts often attenuated by similarity-based clustering. By jointly modeling cohesion within domains and activation at interfaces, Spartan recovers anatomically aligned partitions across spatially resolved transcriptomics technologies including Visium HD, MERFISH, Stereo-seq, and STARmap. We 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 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.