Protocol for unsupervised inference of cell-cell communication using matrix decomposition

Yi Liu; Xiao Chang; Xiaoping Liu

doi:10.1016/j.xpro.2024.103006

Protocol for unsupervised inference of cell-cell communication using matrix decomposition

STAR Protoc. 2024 Apr 10;5(2):103006. doi: 10.1016/j.xpro.2024.103006. Online ahead of print.

Authors

Yi Liu¹, Xiao Chang², Xiaoping Liu³

Affiliations

¹ Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233030, China; Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China.
² Institute of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Bengbu 233030, China. Electronic address: chxlaugh@aufe.edu.cn.
³ Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China. Electronic address: xpliu@ucas.ac.cn.

Abstract

Exploring cell-cell communication is pivotal for understanding biological processes in multicellular life forms. Here, we present a protocol that details the use of matrix decomposition to infer cell-cell communication (MDIC3) for unsupervised cell-cell communication inference. We describe steps for using the MDIC3 Python scripts to deduce cell-cell communication and identify key ligand-receptor pairs between a specific cell type pair from a single-cell gene expression dataset. This protocol has potential application in cell-cell communication inference on any species. For complete details on the use and execution of this protocol, please refer to Liu et al.¹.

Keywords: Bioinformatics; Cell Biology; Gene Expression; Single Cell.