SpaGraphCCI: Spatial cell-cell communication inference through GAT-based co-convolutional feature integration.

ABSTRACT: Spatially resolved transcriptomics technologies potentially provide the extra spatial position information and tissue image to better infer spatial cell-cell interactions (CCIs) in processes such as tissue homeostasis, development, and disease progression. However, methods for effectively integrating spatial multimodal data to infer CCIs are still lacking. Here, the authors propose a deep learning method for integrating features through co-convolution, called SpaGraphCCI, to effectively integrate data from different modalities of SRT by projecting gene expression and image feature into a low-dimensional space. SpaGraphCCI can achieve significant performance on datasets from multiple platforms including single-cell resolution datasets (AUC reaches 0.860-0.907) and spot resolution datasets (

SUBMITTER: Zhang H 

PROVIDER: S-EPMC11771809 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature