SpaCCC:Large Language Model-Based Cell-Cell Communication Inference for Spatially Resolved Transcriptomic Data  

作　　者：Boya Ji Xiaoqi Wang Debin Qiao Liwen Xu Shaoliang Peng 

机构地区：[1]College of Computer Science and Electronic Engineering,Hunan University,Changsha 410082,China [2]School of Computer Science,Northwestern Polytechnical University,Xi’an 710000,China [3]School of Computer and Artificial Intelligence and National Supercomputing Center in Zhengzhou,Zhengzhou University,Zhengzhou 450001,China

出　　处：《Big Data Mining and Analytics》2024年第4期1129-1147,共19页大数据挖掘与分析(英文)

基　　金：supported by the National Natural Science Foundation of China-Science and Technology Development Fund(No.62361166662);the National Key R&D Program of China(Nos.2023YFC3503400 and 2022YFC3400400);the Key R&D Program of Hunan Province(Nos.2023GK2004,2023SK2059,and 2023SK2060);the Top 10 Technical Key Project in Hunan Province(No.2023GK1010);the Key Technologies R&D Program of Guangdong Province(No.2023B1111030004 to FFH);the Funds of State Key Laboratory of Chemo/Biosensing and Chemometrics,the National Supercomputing Center in Changsha(http://nscc.hnu.edu.cn/);Peng Cheng Lab.Graduate Research Innovation Project of Hunan Province(No.QL20230101).

摘　　要：Drawing parallels between linguistic constructs and cellular biology,Large Language Models(LLMs)have achieved success in diverse downstream applications for single-cell data analysis.However,to date,it still lacks methods to take advantage of LLMs to infer Ligand-Receptor(LR)-mediated cell-cell communications for spatially resolved transcriptomic data.Here,we propose SpaCCC to facilitate the inference of spatially resolved cell-cell communications,which relies on our fine-tuned single-cell LLM and functional gene interaction network to embed ligand and receptor genes into a unified latent space.The LR pairs with a significant closer distance in latent space are taken to be more likely to interact with each other.After that,the molecular diffusion and permutation test strategies are respectively employed to calculate the communication strength and filter out communications with low specificities.The benchmarked performance of SpaCCC is evaluated on real single-cell spatial transcriptomic datasets with superiority over other methods.SpaCCC also infers known LR pairs concealed by existing aggregative methods and then identifies communication patterns for specific cell types and their signaling pathways.Furthermore,SpaCCC provides various cell-cell communication visualization results at both single-cell and cell type resolution.In summary,SpaCCC provides a sophisticated and practical tool allowing researchers to decipher spatially resolved cell-cell communications and related communication patterns and signaling pathways based on spatial transcriptome data.

关 键 词：Large Language Models(LLM) spatial transcriptome data Cell-Cell Communications(CCCs) functional gene interaction networks unified latent space 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TP309[自动化与计算机技术—计算机科学与技术]