SpaDCN: Deciphering Spatial Functional Landscape from Spatially Resolved Transcriptomics by Aligning Cell-Cell...

Spatially resolved transcriptomics (SRT) has emerged as a transformative technology for elucidating cellular organization and tissue architecture. However, a significant challenge remains in identifying pathology-relevant spatial functional landscapes within the tissue microenvironment, primarily due to the limited integration of cell-cell communication dynamics. To address this limitation, SpaDCN, a Spatially Dynamic graph Convolutional Network framework is proposed, which aligns cell-cell communications and gene expression within a spatial context to reveal the spatial functional regions with the coherent cellular organization. To effectively transfer the influence of cell-cell communications on expression variation, SpaDCN respectively generates the node layer and edge layer of spatial graph representation from expression data and the ligand-receptor complex contributions and then employs a dynamic graph convolution to switch the propagation of node graph and edge graph. It is demonstrated that SpaDCN outperforms existing methods in identifying spatial domains and denoising expression across various platforms and species. Notably, SpaDCN excels in identifying marker genes with significant prognostic potential in cancer tissues. In conclusion, SpaDCN offers a powerful and precise tool for spatial domain detection in spatial transcriptomics, with broad applicability across various tissue types and research disciplines.