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Front Genet. 2019 Apr 5;10:317. doi: 10.3389/fgene.2019.00317. eCollection 2019.

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

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Center for Bioinformatics and Computational Biology, and Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai, China.
National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR, United States.


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.


allelic expression; alternative splicing; cell clustering; cell trajectory; single-cell RNA-seq

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