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

TL;DR

SPHENIC introduces a topology-aware clustering method for spatial transcriptomics that enhances robustness to noise and preserves spatial coherence, outperforming existing methods on multiple datasets.

Contribution

It integrates topology-invariant features and dual regularization to improve clustering accuracy and spatial preservation in spatial transcriptomics data.

Findings

Outperforms state-of-the-art methods by 4.19%-9.14% on benchmark datasets.

Effectively captures complex tissue architectures.

Enhances robustness to noisy edges in cell interaction graphs.

Abstract

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.