生物技术通报 ›› 2021, Vol. 37 ›› Issue (1): 52-59.doi: 10.13560/j.cnki.biotech.bull.1985.2020-0469

• 组学生物技术专题 • 上一篇    下一篇

单细胞RNA测序数据分析方法研究进展

张淼(), 孙祥瑞, 徐春明()   

  1. 北京工商大学轻工科学技术学院,北京 100048
  • 收稿日期:2020-04-21 出版日期:2021-01-26 发布日期:2021-01-15
  • 作者简介:张淼,女,硕士研究生,研究方向:轻工技术与工程;E-mail: 792151970@qq.com
  • 基金资助:
    国家科技重点研发计划项目(2017ZX07301004)

Research Progress of Approaches in Single Cell RNA Sequencing Data Analysis

ZHANG Miao(), SUN Xiang-rui, XU Chun-ming()   

  1. School of Light Industry Science and Technology,Beijing Technology and Business University,Beijing 100048
  • Received:2020-04-21 Published:2021-01-26 Online:2021-01-15

摘要:

单细胞RNA测序(Single cell RNA sequencing,scRNA-Seq)已经广泛应用于细胞分化、肿瘤微环境及多种疾病病因学研究。目前,由于scRNA-Seq具有低捕获率、高噪声、高变异性等特点,通过优化数据分析方法提高测序结果准确性已经成为测序领域的研究热点。对近年来数据分析过程中利用的数学方法进行了总结,讨论了数据分析的优势及存在的问题,以期为新算法的开发和应用提供参考,逐步提高测序结果的可靠性。

关键词: 单细胞测序, 数据分析, 质量控制, 差异表达, 算法

Abstract:

Single cell RNA sequencing(scRNA-Seq)has been widely used in cell differentiation,tumor microenvironment and etiology of various diseases. Currently improving the accuracy of sequencing results by optimizing data analysis methods has become a research hotspot in the field of sequencing due to the characteristics of low capture rate,high noise,and high variability of scRNA-Seq. Here an overview of the mathematical methods used in the process of data analysis in recent years is summarized,and the advantages and issues in data analysis are discussed,aiming to provide a reference for the development and application of new algorithms,and to gradually improve the reliability of sequencing results.

Key words: single cell sequencing, data analysis methods, quality control, differential expression, algorithm