SPHENIC: Topology-Informed Multi-View Clustering for Spatial Transcriptomics
This work addresses spatial transcriptomics clustering for cell subpopulation identification, offering a novel method that is incremental in improving existing approaches.
The paper tackled the problem of noisy spatial transcriptomic data and insufficient spatial neighborhood modeling in clustering by proposing SPHENIC, which incorporates topological features and optimizes spatial embeddings, resulting in a 3.31%-6.54% performance improvement over state-of-the-art methods on 14 benchmark slices.
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.