STWave: Fine-Scale Spatial Structure Discovery in Microscopic-Resolution Spatial Transcriptomics via Patchwise...

The advent of microscopic-resolution spatial transcriptomics ( $\mu \mathrm{ST}$ ) enables the mapping of tissue complexity at subcellular resolution. However, the massive increase in data volume introduces a critical "memory wall," significantly limiting the applicability of existing computational methods on deployable hardware platforms. In addition, traditional single-scale modeling approaches often struggle to disentangle weak biological signals from technical noise. To overcome these challenges, STWave is proposed as a scalable framework for spatial domain identification. Specifically, STWave first enables efficient computation during both training and inference by leveraging a decoupled patch-based learning strategy, thereby mitigating hardware limitations without sacrificing global contextual information. Furthermore, STWave adopts a discrete wavelet transform to encode gene expression features across multiple scales, effectively capturing both global trends and fine-grained details. Extensive experimental results show that STWave attains state-of-the-art clustering performance while maintaining exceptional computational efficiency, enabling scalable analysis of ultra-large spatial transcriptomics datasets under constrained computational resources. Across platforms including Visium HD, Xenium, and CosMx, the proposed STWave accurately resolves complex tissue structures, enabling the identification of distinct immune niches within the tumor microenvironment and revealing fine-grained developmental substructures. These results support STWave as a robust and efficient tool for large-scale $\mu \mathrm{ST}$  analysis.