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

2025-08-14

AI Summary

Existing spatial transcriptomics clustering methods face two key challenges: topological learning is highly susceptible to data noise, and inadequate spatial neighborhood modeling leads to suboptimal embedding quality. To address these, we propose a novel framework integrating persistent homology with multi-view clustering. Our core contributions are: (1) leveraging persistent homology to extract robust, topologically invariant features resilient to noise; and (2) introducing a Spatial Constraint and Distribution Optimization Module (SCDOM) that jointly enhances spatial neighborhood similarity modeling and representation stability. The framework synergistically combines topological data analysis, graph neural networks, and multi-view clustering to significantly improve robustness and spatial embedding accuracy—especially under low-signal conditions. Evaluated on 14 benchmark datasets, our method achieves average improvements of 3.31%–6.54% in clustering performance over state-of-the-art approaches.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Data transparency and provenance

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.

Problem

• Improves noisy spatial-transcriptomics clustering via topology
• Enhances spatial neighborhood modeling for better embeddings
• Integrates persistent homology for stable representation learning

Innovation

• Incorporates invariant topological features for stable learning
• Uses Spatial Constraint and Distribution Optimization Module
• Enhances similarity with neighbors, reduces non-neighbor similarity