SAGE: Spatially Aware Gene Selection and Dual-View Embedding Fusion for Domain Identification in Spatial Transcriptomics

Despite enabling high-resolution mapping of gene expression within tissues, spatial transcriptomics (ST) still faces challenges in accurately segmenting spatial domains due to complex tissue architecture and limitations of current methods. Most approaches rely on local spatial priors, lack gene-level interpretability, and fall short in capturing structure-discriminative genes or long-range functional relationships, limiting their ability to resolve biologically meaningful architectures. We present Spatially Aware Gene selection and dual-view Embedding fusion (SAGE), a unified and reproducible framework for domain identification in spatial transcriptomics that combines topic-driven gene selection with dual-view embedding fusion to address these gaps. SAGE integrates non-negative matrix factorization (NMF)-based topic modeling with classifier-based importance scoring to identify highly spatially informative genes, and fuses a local expression graph with a topic-driven non-local graph via consensus refinement and contrastive graph representation learning to jointly learn spatial and functional embeddings. Evaluated on 34 real-world datasets, SAGE not only outperforms existing methods in clustering accuracy but also reveals functionally coherent regions and interpretable gene expression patterns. In case studies, SAGE reveals spatial heterogeneity associated with a pre-malignant activation state in human breast cancer. Moreover, in zebrafish melanoma, it refines the tumor-muscle interface into transcriptionally distinct subdomains and uncovers shared vascular signatures between anatomically separate tissues. Together, these results demonstrate that SAGE can be used not only for accurate spatial domain delineation across diverse ST platforms, but also for dissecting microenvironmental niches and long-range tissue interactions underlying disease progression.