Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk.
IF: 17.694
Cited by: 20


Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics.


10X Visium
Spatial Transcriptomics

MeSH terms

Cell Communication
Single-Cell Analysis


Shao, Xin
Li, Chengyu
Yang, Haihong
Lu, Xiaoyan
Liao, Jie
Qian, Jingyang
Wang, Kai
Cheng, Junyun
Yang, Penghui
Chen, Huajun
Xu, Xiao
Fan, Xiaohui