Liu Yi, Zhang Yuelei, Chang Xiao, Liu Xiaoping
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.
School of Mathematics and Statistics, Shandong University, Weihai 364209, China.
Patterns (N Y). 2024 Jan 11;5(2):100911. doi: 10.1016/j.patter.2023.100911. eCollection 2024 Feb 9.
Crosstalk among cells is vital for maintaining the biological function and intactness of systems. Most existing methods for investigating cell-cell communications are based on ligand-receptor (L-R) expression, and they focus on the study between two cells. Thus, the final communication inference results are particularly sensitive to the completeness and accuracy of the prior biological knowledge. Because existing L-R research focuses mainly on humans, most existing methods can only examine cell-cell communication for humans. As far as we know, there is currently no effective method to overcome this species limitation. Here, we propose MDIC3 (matrix decomposition to infer cell-cell communication), an unsupervised tool to investigate cell-cell communication in any species, and the results are not limited by specific L-R pairs or signaling pathways. By comparing it with existing methods for the inference of cell-cell communication, MDIC3 obtained better performance in both humans and mice.
细胞间的串扰对于维持系统的生物学功能和完整性至关重要。大多数现有的研究细胞间通讯的方法都是基于配体-受体(L-R)表达,并且它们专注于两个细胞之间的研究。因此,最终的通讯推断结果对先验生物学知识的完整性和准确性特别敏感。由于现有的L-R研究主要集中在人类,大多数现有方法只能检测人类的细胞间通讯。据我们所知,目前没有有效的方法来克服这种物种限制。在这里,我们提出了MDIC3(矩阵分解推断细胞间通讯),这是一种用于研究任何物种细胞间通讯的无监督工具,其结果不受特定L-R对或信号通路的限制。通过将其与现有的细胞间通讯推断方法进行比较,MDIC3在人类和小鼠中均表现出更好的性能。