Deciphering cell–cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network
Wenyi Yang, Pingping Wang, Shouping Xu, Tao Wang, Meng Luo, Yideng Cai, Chang Xu, Guangfu Xue, Jinhao Que, Qian Ding, Xiyun Jin, Yuexin Yang, Fenglan Pang, Boran Pang, Yi Lin, Huan Nie, Zhaochun Xu, Yong Ji, Qinghua Jiang
Abstract
The inference of cell-cell communication (CCC) is crucial for a better understanding of complex cellular dynamics and regulatory mechanisms in biological systems. However, accurately inferring spatial CCCs at single-cell resolution remains a significant challenge. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. DeepTalk achieves excellent performance in discovering meaningful spatial CCCs on multiple cross-platform datasets, which demonstrates its superior ability to dissect cellular behavior within intricate biological processes.