Clustering and classification methods for single-cell RNA-sequencing data. (4th July 2019)

• Record Type: Journal Article
• Authors: Qi, Ren; Ma, Anjun; Ma, Qin; Zou, Quan
• Is Part Of: Briefings in bioinformatics. Volume 21:Number 4(2020)
• Page Start: 1196
• Page End: 1208
• Publication Date: 2019-07-04
• Subjects: single-cell RNA-seq -- clustering -- classification -- similarity metric -- sequences analysis -- machine learning; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbz062
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: 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.