CellCall: integrating paired ligand–receptor and transcription factor activities for cell–cell communication. Issue 15 (31st July 2021)

• Record Type: Journal Article
• Authors: Zhang, Yang; Liu, Tianyuan; Hu, Xuesong; Wang, Mei; Wang, Jing; Zou, Bohao; Tan, Puwen; Cui, Tianyu; Dou, Yiying; Ning, Lin; huang, Yan; Rao, Shuan; Wang, Dong; Zhao, Xiaoyang
• Is Part Of: Nucleic acids research. Volume 49:Issue 15(2021)
• Page Start: 8520
• Page End: 8534
• Publication Date: 2021-07-31
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkab638
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: With the dramatic development of single-cell RNA sequencing (scRNA-seq) technologies, the systematic decoding of cell-cell communication has received great research interest. To date, several in-silico methods have been developed, but most of them lack the ability to predict the communication pathways connecting the insides and outsides of cells. Here, we developed CellCall, a toolkit to infer inter- and intracellular communication pathways by integrating paired ligand-receptor and transcription factor (TF) activity. Moreover, CellCall uses an embedded pathway activity analysis method to identify the significantly activated pathways involved in intercellular crosstalk between certain cell types. Additionally, CellCall offers a rich suite of visualization options (Circos plot, Sankey plot, bubble plot, ridge plot, etc.) to present the analysis results. Case studies on scRNA-seq datasets of human testicular cells and the tumor immune microenvironment demonstrated the reliable and unique functionality of CellCall in intercellular communication analysis and internal TF activity exploration, which were further validated experimentally. Comparative analysis of CellCall and other tools indicated that CellCall was more accurate and offered more functions. In summary, CellCall provides a sophisticated and practical tool allowing researchers to decipher intercellular communication and related internal regulatory signals based on scRNA-seq data. CellCall is freely available at https://github.com/ShellyCoder/cellcall .