Clustering and classification methods for single-cell RNA-sequencing data

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 to...

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Bibliographic Details
Published in:Briefings in bioinformatics 2019-07, Vol.21 (4), p.1196-1208
Main Authors: Qi, Ren, Ma, Anjun, Ma, Qin, Zou, Quan
Format: Article
Language:English
Subjects:
Bioinformatics
Classification
Clustering
Data integration
Gene sequencing
Review
Ribonucleic acid
RNA
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Summary: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.
ISSN:1477-4054
1467-5463
1477-4054
DOI:10.1093/bib/bbz062