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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.
Highlights
Bulk RNA-seq technologies have been widely used to study gene expression patterns at population level in the past decade
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
With the innovation of sequencing technologies, some different scRNA-seq protocols have been proposed in the past few years, which largely facilitated the understanding of dynamic gene
Summary
Bulk RNA-seq technologies have been widely used to study gene expression patterns at population level in the past decade. The advent of single-cell RNA sequencing (scRNA-seq) provides unprecedented opportunities for exploring gene expression profile at the single-cell level. With the innovation of sequencing technologies, some different scRNA-seq protocols have been proposed in the past few years, which largely facilitated the understanding of dynamic gene. Available scRNA-seq protocols can be mainly split into two categories based on the captured transcript coverage: (i) full-length transcript sequencing approaches [such as Smart-seq (Picelli et al, 2013), MATQ-seq (Sheng et al, 2017) and SUPeR-seq (Fan X. et al, 2015)]; and (ii) 3 -end [e.g., Drop-seq (Macosko et al, 2015), Seq-Well (Gierahn et al, 2017), Chromium (Zheng et al, 2017), and DroNC-seq (Habib et al, 2017)] or 5 -end [such as STRT-seq (Islam et al, 2011, 2012)] transcript sequencing technologies. In conducting single-cell transcriptomic study, specific scRNA-seq technology may need to be employed in consideration of the balance between research goal and sequencing cost
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