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. 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. The high variability of scRNA-seq data raises computational challenges in data analysis. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. A number of scRNA-seq protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. 2National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, AR, United States.1Center 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.Geng Chen 1* Baitang Ning 2 Tieliu Shi 1*
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