RobustCCC: a robustness evaluation tool for cell-cell communication methods - Zhang, Chenxing; Gao, Lin; Hu, Yuxuan; Huang, Zhengyang (Frontiers in Genetics, 2023)
Cell-cell communication (CCC) inference has become a routine task in single-cell data analysis. Many computational tools are developed for this purpose. However, the robustness of existing CCC methods remains underexplored. We develop a user-friendly tool, RobustCCC, to facilitate the robustness evaluation of CCC methods with respect to three perspectives, including replicated data, transcriptomic data noise and prior knowledge noise. RobustCCC currently integrates 14 state-of-theart CCC methods and 6 simulated single-cell transcriptomics datasets to generate robustness evaluation reports in tabular form for easy interpretation. We find that these methods exhibit substantially different robustness performances using different simulation datasets, implying a strong impact of the input data on resulting CCC patterns. In summary, RobustCCC represents a scalable tool that can easily integrate more CCC methods, more single-cell datasets from different species (e.g. mouse and human) to provide guidance in selecting methods for identification of consistent and stable CCC patterns in tissue microenvironments. RobustCCC is freely available at https://github.com/GaoLabXDU/RobustCCC This is a provisional file, not the final typeset article intra-cellular regulatory networks using mutual information and identify significant communication patterns through the Steiner Forest algorithm. CytoTalk is used to analyze the differences of signaling networks across tissues and developmental stages (Hu et al., 2021).A large number of CCC methods have been developed, raising the question of how to systematically evaluate these methods. Dimitrov et al. conduct a comparative study of 16 CCC resources and 7 CCC methods. Their focus is on assessing the impact of different inference resources on the methods, designing a framework called LIANA for integrating multiple resources and methods (Dimitrov et al., 2022). In another study, Liu et al. hypothesize that cells with close spatial distances would recognize "short-range" ligand-receptor pairs, while cells with far spatial distances would recognize "long-range" ligand-receptor pairs. They systematically evaluate the accuracy of 16 CCC methods based on single-cell spatial transcriptome data (Li et al., 2022). In fact, CCC methods are susceptible to various factors, including replicated data(