Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2019 Apr 5:10:317.
doi: 10.3389/fgene.2019.00317. eCollection 2019.

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

Geng Chen  1 , Baitang Ning  2 , Tieliu Shi  1
Affiliations
Review

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

Geng Chen et al. Front Genet. .

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.

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

PubMed Disclaimer

Figures

FIGURE 1
FIGURE 1
Overview of various analyses for scRNA-seq data.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Ahmed S., Rattray M., Boukouvalas A. (2019). GrandPrix: scaling up the Bayesian GPLVM for single-cell data. Bioinformatics 35 47–54. 10.1093/bioinformatics/bty533 - DOI - PMC - PubMed
    1. Aibar S., Gonzalez-Blas C. B., Moerman T., Huynh-Thu V. A., Imrichova H., Hulselmans G., et al. (2017). SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14 1083–1086. 10.1038/nmeth.4463 - DOI - PMC - PubMed
    1. Andrews T. S., Hemberg M. (2018a). Identifying cell populations with scRNASeq. Mol. Aspects Med. 59 114–122. 10.1016/j.mam.2017.07.002 - DOI - PubMed
    1. Andrews T. S., Hemberg M. (2018b). M3Drop: dropout-based feature selection for scRNASeq. Bioinformatics 10.1093/bioinformatics/bty1044 [Epub ahead of print]. - DOI - PMC - PubMed
    1. Angerer P., Haghverdi L., Buttner M., Theis F. J., Marr C., Buettner F. (2016). destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32 1241–1243. 10.1093/bioinformatics/btv715 - DOI - PubMed

LinkOut - more resources

-