SALVG: Latent Variable Gene Augmented Graph Learning for Multi-View Clustering in Spatial Transcriptomics

Spatial transcriptomics technologies enable the integration of gene expression profiles with spatial context, facilitating a deeper understanding of tissue architecture through downstream tasks such as clustering. However, existing approaches predominantly focus on highly variable genes (HVGs), while the informative structural and contextual signals embedded in low variability genes (LVGs) remain largely underutilized. To bridge this gap, we propose SALVG (Spatial Augmentation via Latent Variable Genes), a novel and plug-and-play framework that leverages LVG-derived structural priors to enhance HVG representation learning for spatial clustering. Specifically, SALVG constructs spatial, feature, and combined graphs for both HVGs and LVGs, and introduces two graph-based augmentation strategies to inject LVG information into HVG graphs. The first strategy enhances the HVG combined graph directly using the LVG combined graph, while the other individually augments HVG spatial and feature graphs with their LVG counterparts before fusing them into a new combined representation. These enhanced graph structures are subsequently employed for downstream clustering. To the best of our knowledge, SALVG is the first framework to exploit LVG signals for assisting HVG-centric spatial transcriptomics clustering, effectively capturing complementary structural and contextual cues. Experiments on multiple benchmarks demonstrate its effectiveness, robustness, and transferability. Case studies further confirm that LVG-derived structure enhances biological interpretability by revealing coherent spatial and cellular patterns.