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首页> 外文期刊>Briefings in bioinformatics >Clustering and classification methods for single-cell RNA-sequencing data
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Clustering and classification methods for single-cell RNA-sequencing data

机译：单细胞RNA测序数据的聚类和分类方法

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摘要

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.
机译：测量单细胞RNA测序（ScRNA-SEQ）数据之间的相似性的适当方法在生物信息学中普遍存在，但是使用单一聚类或分类方法来处理SCRNA-SEQ数据通常很困难。这导致了旨在自动处理与ScrNA-SEQ数据相关的特定问题的集成方法和工具的出现。这些方法引起了对生物信息学和相关领域的大量兴趣。在本文中，我们系统地审查了综合方法和工具，突出了各种方法的利弊。我们不仅特别注意聚类和分类方法，还要讨论最近作为强大的替代品出现的方法，包括非线性和线性方法和下行尺寸方法。最后，我们专注于SCRNA-SEQ数据的聚类和分类方法，特别是集成方法，并提供SCRNA-SEQ数据的全面描述和下载URL。

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