Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

Military Medical Research

Review

Chun-Jie Jiang¹  Xia Li²  Wei-Wei Zhou²  Ya Zhang³  Gang Xu³  Yong-Sheng Li³  Tao Pan³  Si Li³  Xiao He⁴  Murray J. Cairns⁵  Shi-Cai Fan⁶  Qiu-Zhen Chen⁷  Xi Wang⁷  Min Su⁷  Huan-Yu Yan⁷  Qiao-Zhen Shi⁷  Yi Gong⁸ 

[1] Baylor College of Medicine, 77030, Houston, TX, USA;College of Bioinformatics Science and Technology, Harbin Medical University, 150081, Harbin, Heilongjiang, China;College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, 571199, Haikou, Hainan, China;Department of Laboratory Medicine, Women and Children’s Hospital of Chongqing Medical University, 401174, Chongqing, China;School of Biomedical Sciences and Pharmacy, Faculty of Health and Medicine, the University of Newcastle, University Drive, 2308, Callaghan, NSW, Australia;Precision Medicine Research Program, Hunter Medical Research Institute, 2305, New Lambton Heights, NSW, Australia;Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, 518110, Shenzhen, Guangdong, China;State Key Laboratory of Reproductive Medicine, Nanjing Medical University, 211166, Nanjing, China;State Key Laboratory of Reproductive Medicine, Nanjing Medical University, 211166, Nanjing, China;Department of Immunology, Nanjing Medical University, 211166, Nanjing, China;

关键词: Single-cell RNA-sequencing (scRNA-seq);    Data analysis;    Biomedical research;    Clinical applications;   

DOI  :  10.1186/s40779-022-00434-8

 received in 2022-09-27, accepted in 2022-11-18,  发布年份 2022

来源: Springer

【 摘 要 】

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

41408_2022_764_Article_IEq15.gif

41408_2022_764_Article_IEq7.gif

Fig. 2

40249_2022_1045_Article_IEq22.gif

40249_2022_1045_Article_IEq13.gif