Spatial transcriptomics clustering is pivotal for identifying cell subpopulations by leveraging spatial location information. While recent graph-based methods modeling cell-cell interactions have improved clustering accuracy, they remain limited in two key aspects: (i) reliance on local aggregation in static graphs often fails to capture robust global topological structures (e.g., loops and voids) and is vulnerable to noisy edges; and (ii) dimensionality reduction techniques frequently neglect spatial coherence, causing physically adjacent spots to be erroneously separated in the latent space. To overcome these challenges, we propose SPHENIC, a Spatial Persistent Homology-Enhanced Neighborhood Integrative Clustering method. Specifically, it explicitly incorporates topology-invariant features into the clustering network to ensure robust representation learning against noise. Furthermore, we design a dual-regularized optimization module that imposes spatial constraints alongside distributional optimization, ensuring that the embedding space preserves the physical proximity of cells. Extensive experiments on 11 benchmark datasets demonstrate that SPHENIC outperforms state-of-the-art methods by 4.19%-9.14%, validating its superiority in characterizing complex tissue architectures.

翻译：空间转录组聚类对于通过利用空间位置信息识别细胞亚群至关重要。尽管近期基于图的方法通过建模细胞间相互作用提升了聚类精度，但它们仍存在两个关键局限：（i）依赖静态图中的局部聚合往往难以捕捉鲁棒的全局拓扑结构（如环状与空洞结构），且易受噪声边干扰；（ii）降维技术常忽略空间连贯性，导致物理相邻的位点在隐空间中被错误分离。为克服这些挑战，我们提出SPHENIC——一种空间持续同调增强的邻域整合聚类方法。该方法显式地将拓扑不变特征融入聚类网络，以确保针对噪声的鲁棒表征学习。此外，我们设计了双正则化优化模块，在分布优化基础上施加空间约束，确保嵌入空间保持细胞的物理邻近性。在11个基准数据集上的大量实验表明，SPHENIC以4.19%-9.14%的优势超越现有最优方法，验证了其在表征复杂组织结构方面的优越性。