Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk
Xin Shao,Chengyu Li,Haihong Yang,Xiaoyan Lu,Jie Liao,Jingyang Qian,Kai Wang,Junyun Cheng,Penghui Yang,Huali Chen,Xiao Xu,Xiao-Yu Fan +11 more
TL;DR: Li et al. as mentioned in this paper used 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.
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Abstract: Abstract 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.
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Citations
Screening cell–cell communication in spatial transcriptomics via collective optimal transport
Zixuan Cang,Yangxing Zhao,Axel A. Almet,Adam R. Stabell,Raul Ramos,Maksim V. Plikus,Scott X. Atwood,Qing Nie +7 more
TL;DR: In this article , a collective optimal transport method is developed to handle complex molecular interactions and spatial constraints in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells.
Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues
Duy Pham,Xiao Tan,Brad Balderson,Laura F. Grice,Sohye Yoon,Emily F. Willis,Minh Tran,Pui Yeng Lam,Arti Raghubar,Priyakshi Kalita-de Croft,Sunil Lakhani,Jana Vukovic,Marc J. Ruitenberg,Quan H Nguyen +13 more
TL;DR: Three computational-statistical algorithms that integrate all three data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology, allow for robust interrogation of biological processes within healthy and diseased tissues.
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CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics
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Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk
Xin Shao,Chengyu Li,Haihong Yang,Xiaoyan Lu,Jie Liao,Jingyang Qian,Kai Wang,Junyun Cheng,Penghui Yang,Huali Chen,Xiao Xu,Xiao-Yu Fan +11 more
TL;DR: Li et al. as mentioned in this paper used 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.
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