SAREV: A review on statistical analytics of single-cell RNA sequencing data.

IF 4.4 2区 数学 Q1 STATISTICS & PROBABILITY
Dorothy Ellis, Dongyuan Wu, Susmita Datta
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引用次数: 2

Abstract

Due to the development of next-generation RNA sequencing (NGS) technologies, there has been tremendous progress in research involving determining the role of genomics, transcriptomics and epigenomics in complex biological systems. However, scientists have realized that information obtained using earlier technology, frequently called 'bulk RNA-seq' data, provides information averaged across all the cells present in a tissue. Relatively newly developed single cell (scRNA-seq) technology allows us to provide transcriptomic information at a single-cell resolution. Nevertheless, these high-resolution data have their own complex natures and demand novel statistical data analysis methods to provide effective and highly accurate results on complex biological systems. In this review, we cover many such recently developed statistical methods for researchers wanting to pursue scRNA-seq statistical and computational research as well as scientific research about these existing methods and free software tools available for their generated data. This review is certainly not exhaustive due to page limitations. We have tried to cover the popular methods starting from quality control to the downstream analysis of finding differentially expressed genes and concluding with a brief description of network analysis.

Abstract Image

SAREV:单细胞RNA测序数据统计分析综述
由于下一代RNA测序(NGS)技术的发展,在确定基因组学、转录组学和表观基因组学在复杂生物系统中的作用方面的研究取得了巨大进展。然而,科学家们已经意识到,使用早期技术获得的信息,通常被称为“批量RNA-seq”数据,提供了组织中所有细胞的平均信息。相对较新开发的单细胞(scRNA-seq)技术使我们能够以单细胞分辨率提供转录组信息。然而,这些高分辨率数据具有其自身的复杂性,需要新的统计数据分析方法来对复杂的生物系统提供有效和高度准确的结果。在这篇综述中,我们介绍了许多最近开发的统计方法,供希望进行scRNA-seq统计和计算研究的研究人员使用,以及对这些现有方法和可用于生成数据的免费软件工具的科学研究。由于篇幅限制,这篇综述肯定不是详尽无遗的。我们试图涵盖从质量控制到寻找差异表达基因的下游分析的流行方法,最后简要描述网络分析。
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来源期刊
CiteScore
6.20
自引率
0.00%
发文量
31
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