Front Physiol. 2026 Oct 7;17:1967219. doi: 10.3389/fphys.2026.1967219. eCollection 2026.
ABSTRACT
INTRODUCTION: Spatial domain identification from spatial transcriptomics (ST) data is essential for characterizing the spatial organization of complex tissues. Existing contrastive learning methods face a persistent trade-off: instance-level approaches are susceptible to false negatives, whereas cluster-level methods may overlook fine-grained local structures. Moreover, direct multi-view fusion may introduce redundant or noisy correlations, thereby degrading representation quality.
METHODS: To address these limitations, we propose MGCF-Net, a multi-view granular-ball contrastive fusion network for spatial domain identification. MGCF-Net constructs a spatial graph and a feature graph to represent physical proximity and transcriptomic similarity, respectively. A redundancy-reduction objective preserves spot-level correspondence across the two views while suppressing spurious similarities between different spots. Multi-view granular-ball contrastive learning groups spot-level representations into local structural units and aligns them across views according to their membership overlap.
RESULTS: On eight dorsolateral prefrontal cortex (DLPFC) tissue sections, MGCF-Net attains the highest mean adjusted Rand index (ARI) among the evaluated methods. Its normalized mutual information (NMI) remains competitive with the comparison methods.
DISCUSSION: By contrasting local structural units rather than individual spots, MGCF-Net reduces the influence of false negatives arising from instance-level contrastive learning. This group-level formulation preserves coherent spatial structures without requiring a generative decoder.
PMID:42908645 | PMC:PMC13683150 | DOI:10.3389/fphys.2026.1967219