Spatial transcriptomics maps gene expression across tissues, yet data sparsity and noise challenge long-range dependency modeling, limiting accurate spatial domain delineation. In this study, we present TOGAR, a token-gated generative refinement model that unifies denoising, spatial enhancement, and clustering for spatial transcriptomics. Firstly, the model combines a graph convolutional network loss with a loss based on the zero-inflated negative binomial distribution to reduce noise and enhance signal clarity in sparse count data. It then employs a UGate-based diffusion backbone, which integrates token gating, gated linear attention, and rotary positional embedding for generative spatial refinement. Finally, similarity-guided averaging along diffusion trajectories provides stable spot-level estimates, and clustering of the refined representations produces spatial domains with sharp boundaries suitable for downstream analyses. We evaluate TOGAR across three spatial transcriptomics platforms. In benchmarks on twelve slices against seven popular methods, TOGAR consistently achieves or exceeds clustering accuracy, demonstrating superior stability. TOGAR effectively recovers coherent cortical layer organization, delineates fine-grained tumor subdomains associated with immune activity and extracellular matrix remodeling, and generates clearer, biologically interpretable domain boundaries. Notably, TOGAR excels in detecting extremely small and rare spatial structures, successfully identifying biologically important regions that other methods completely miss, while maintaining boundary integrity in complex multi-cluster structures and avoiding issues of over-connectivity or incomplete detection.
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