A list by @biology · 3 papers · 0 followers
Min Dai, Tivadar Torok, Dawei Sun, Vallari Shende, Grace Wang, Yuesang Lin, Sherry Jingjing Wu, Alyssa Rukshin, Gord Fishell, Fei Chen
LARIS identifies cell type-specific and spatially restricted ligand-receptor interactions in spatial transcriptomics data with high accuracy and scalability.
bioRxiv · Nov 26, 2025 · PreprintBioinformatics
Jialin Liu, Hiroaki Manabe, Weizhou Qian, Shion Orikasa, Javid Ghaemmaghami, Yichen Wang, Yichen Gu, Angel Ka Yan Chu, Gaurav T. Gadhvi, Yuxuan Song, Patrick McClinden, Vibha N. Lama, Noriaki Ono, Joshua D. Welch
CytoSignal detects and quantifies ligand-receptor signaling interactions at cellular resolution from spatial transcriptomic data, enabling spatial gradient analysis and temporal dynamics prediction.
Nature Genetics · Jun 1, 2026Peer-reviewedBioinformatics
Lei Shi, Min Dai, Yongbo Zhang, Song Wu, M Wang, Xiu‐Jie Wang
CellClick is an interactive platform that improves cell type annotation accuracy in single-cell and spatial omics data through automated and user-guided tools.
bioRxiv · Jun 3, 2026 · PreprintBioinformatics
Papers closest to the centre of this list, not already in it.
Zhuoxuan LI, Tianjie Wang, Pengtao Liu, Yuanhua Huang
SpatialDM identifies spatially co-expressed ligand-receptor pairs and communication patterns in spatial transcriptomics data with high accuracy and scalability.
bioRxiv · Aug 19, 2022 · PreprintBioinformatics
Suoqin Jin, Maksim V. Plikus, Qing Hua Nie
The paper introduces CellChat v2, a tool for analyzing cell-cell communication from single-cell and spatial transcriptomics data, including spatial proximity analysis and expanded ligand-receptor databases.
Nature Protocols · Sep 16, 2024Peer-reviewedBioinformatics
Zixuan Cang, Yanxiang Zhao, Axel A. Almet, Adam R. Stabell, Raúl Ramos, Maksim V. Plikus, Scott X. Atwood, Qing Hua Nie
This paper introduces COMMOT, a method for inferring cell-cell communication in spatial transcriptomics, and demonstrates its effectiveness across multiple datasets and technologies.
Nature Methods · Jan 23, 2023Peer-reviewedBioinformatics
Jia Zhao, Xinning Shan, Gefei Wang, Tinyi Chu, Chen Lin, Rui Chang, Hongyu Zhao
CONCISE improves spatial ligand-receptor interaction inference by accounting for spatial autocorrelation and measurement errors, reducing false positives and enhancing detection power in spatial transcriptomics data.
bioRxiv · Jun 28, 2026 · PreprintBioinformatics
Shuai Wang, Jiayin Wang, Jiayi Wang, Zhiyuan Yuan, Yi Xu
CellSTIC decodes hierarchical cell-cell communication in spatial multi-omics data by integrating multimodal evidence and organizing interactions into a hierarchical semantic representation.
bioRxiv · Jun 5, 2026 · PreprintBioinformatics
James J. Zhu, Yunguan Wang, Woo Yong Chang, Alicia Malewska, Fabiana Napolitano, Jeffrey C. Gahan, Nisha G Unni, Min Zhao, Rongqing Yuan, Fangjiang Wu, Lauren Yue, Lei Ming Guo, Zhuo Zhao, Danny Z. Chen, Raquibul Hannan, , Tao Wang
The paper introduces a Bayesian framework to map cell-cell interactions from spatial transcriptomics data, revealing new biological insights in cancer and immune responses.
Nature Methods · Sep 3, 2024Peer-reviewedBioinformatics
Lucas Dubois, Alexander Petrov, John Jack
This paper introduces a proximity-aware graph attention network to infer cell-cell communication from spatial transcriptomics data, achieving improved performance over existing methods.
bioRxiv · Apr 23, 2026 · PreprintBioinformatics
Xin Shao, Chengyu Li, Haihong Yang, Xiaoyan Lu, Jie Liao, Jingyang Qian, Kai Wang, Junyun Cheng, Penghui Yang, Huajun Chen, Xiao Xu, Xiaohui Fan
SpaTalk infers cell-cell communication from spatial transcriptomic data using a knowledge graph and graph network, outperforming existing methods on public datasets and revealing insights into tissue communication mechanisms.
Nature Communications · Jul 30, 2022Peer-reviewedBioinformatics