CSMVL: Cluster Structure Aware Multi-View Representation Learning for Domain Identification in Spatial Transcriptomics

Spatial Transcriptomics offers unprecedented opportunities to explore tissue architecture by capturing gene expression with spatial context. However, effectively learning discriminative and spatially smooth representations for accurate spatial domain identification remains a significant challenge. To address this, we propose CSMVL, a multi-view representation learning framework to learn high-quality spot representations by synergistically enhancing both discriminability and spatial continuity. CSMVL introduces a cluster structure learning strategy that guides cell representations within the same domain toward their cluster center while simultaneously separating distinct cluster centers, thereby improving intra-domain compactness and inter-domain separability. Furthermore, graph smoothness regularization is introduced to ensure that representations of spatially adjacent cells within the same domain transition smoothly, reflecting the inherent spatial continuity of biological tissues. Extensive experiments on public ST datasets demonstrate CSMVL’s superiority, achieving an average ARI of 71.64% and NMI of 73.43%, outperforming existing state-of-the-art methods.