Enhancing Spatial Domain Identification in Spatially Resolved Transcriptomics Using Graph Convolutional Networks with Adaptively Feature-Spatial Balance and Contrastive Learning.

Recent advancements in spatially transcriptomics (ST) technologies have enabled the comprehensive measurement of gene expression profiles while preserving the spatial information of cells. Combining gene expression profiles and spatial information has been the most commonly used method to identify spatial functional domains and genes. However, most existing spatial domain decipherer methods are more focused on spatially neighboring structures and fail to take into account balancing the self-characteristics and the spatial structure dependency of spots.