SGCD: High-Resolution Spatial Domain Characterization via Data Interpolation and Cell-Type Deconvolution.

The rapid advancement of spatial transcriptomics has provided a critical data foundation for the high-resolution characterization of tissue spatial domains. Traditional methods for spatial domain identification primarily rely on gene expression data from sampled spots in low-resolution spatial transcriptomic data, often overlooking valuable information between spots that can be crucial for domain identification. Furthermore, these methods are limited by their focus on gene expression data from neighboring spots, without fully integrating prior knowledge of cell types within the tissue's spatial structure. To address these challenges, SGCD, a novel method for tissue spatial domain identification based on data interpolation and cell type deconvolution is proposed. SGCD utilizes interpolation techniques to estimate gene expression data for cells in the gaps between spots and applies deconvolution to extract cell type information from both spots and interstitial regions. By integrating gene expression, cell type, and spatial location data, SGCD achieves accurate delineation of complex spatial domains through graph contrastive learning. Evaluations on various publicly available datasets, including the human dorsolateral prefrontal cortex, mouse brain, pancreatic ductal adenocarcinoma, and breast cancer, demonstrate that SGCD significantly outperforms existing methods in both accuracy and detail, offering strong support for advancing the understanding of tissue functions and disease mechanisms.