Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

Chen, Geng and Ning, Baitang and Shi, Tieliu (2019) Single-Cell RNA-Seq Technologies and Related Computational Data Analysis. Frontiers in Genetics, 10. ISSN 1664-8021

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Abstract

Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies.

Item Type: Article
Subjects: AP Academic Press > Medical Science
Depositing User: Unnamed user with email support@apacademicpress.com
Date Deposited: 08 Feb 2023 07:53
Last Modified: 23 May 2024 06:27
URI: http://info.openarchivespress.com/id/eprint/449

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