SPHENIC: Topology-Informed Multi-View Clustering for Spatial Transcriptomics

• URL: http://arxiv.org/abs/2508.10646v1
• Date: Thu, 14 Aug 2025 13:43:28 GMT
• Title: SPHENIC: Topology-Informed Multi-View Clustering for Spatial Transcriptomics
• Authors: Chenkai Guo, Yikai Zhu, Jing Yangum, Renxiang Guan, Por Lip Yee, Guangdun Peng, Dayu Hu,
• Abstract summary: We propose SPHENIC, a novel Spatial Persistent Homology Enhanced Neighborhood Integrative Clustering method.<n>SPHENIC incorporates invariant topological features into the clustering network to achieve stable representation learning.<n>We show that SPHENIC achieves superior performance on the spatial clustering task, outperforming existing state-of-the-art methods by 3.31%-6.54%.
• Score: 0.8830938707508663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: By incorporating spatial location information, spatial-transcriptomics clustering yields more comprehensive insights into cell subpopulation identification. Despite recent progress, existing methods have at least two limitations: (i) topological learning typically considers only representations of individual cells or their interaction graphs; however, spatial transcriptomic profiles are often noisy, making these approaches vulnerable to low-quality topological signals, and (ii) insufficient modeling of spatial neighborhood information leads to low-quality spatial embeddings. To address these limitations, we propose SPHENIC, a novel Spatial Persistent Homology Enhanced Neighborhood Integrative Clustering method. Specifically, SPHENIC incorporates invariant topological features into the clustering network to achieve stable representation learning. Additionally, to construct high-quality spatial embeddings that reflect the true cellular distribution, we design the Spatial Constraint and Distribution Optimization Module (SCDOM). This module increases the similarity between a cell's embedding and those of its spatial neighbors, decreases similarity with non-neighboring cells, and thereby produces clustering-friendly spatial embeddings. Extensive experiments on 14 benchmark spatial transcriptomic slices demonstrate that SPHENIC achieves superior performance on the spatial clustering task, outperforming existing state-of-the-art methods by 3.31%-6.54% over the best alternative.

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