期刊
BRIEFINGS IN BIOINFORMATICS
卷 21, 期 4, 页码 1196-1208出版社
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbz062
关键词
single-cell RNA-seq; clustering; classification; similarity metric; sequences analysis; machine learning
资金
- National Key R&D Program of China [2018YFC0910405]
- Natural Science Foundation of China [61771331, 1R01GM131399-01]
- National Institute of General Medical Sciences of the National Institutes of Health
Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classification methods to process scRNA-seq data is generally difficult. This has led to the emergence of integrated methods and tools that aim to automatically process specific problems associated with scRNA-seq data. These approaches have attracted a lot of interest in bioinformatics and related fields. In this paper, we systematically review the integrated methods and tools, highlighting the pros and cons of each approach. We not only pay particular attention to clustering and classification methods but also discuss methods that have emerged recently as powerful alternatives, including nonlinear and linear methods and descending dimension methods. Finally, we focus on clustering and classification methods for scRNA-seq data, in particular, integrated methods, and provide a comprehensive description of scRNA-seq data and download URLs.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据