• Shan Gao.
• College of Life Sciences, Nankai University, Tianjin, People's Republic of China. gao_shan@mail.nankai.edu.cn.
• Methods Mol. Biol. 2018 Jan 1; 1754: 311-326.

AbstractSingle-cell transcriptome sequencing, often referred to as single-cell RNA sequencing (scRNA-seq), is used to measure gene expression at the single-cell level and provides a higher resolution of cellular differences than bulk RNA-seq. With more detailed and accurate information, scRNA-seq will greatly promote the understanding of cell functions, disease progression, and treatment response. Although the scRNA-seq experimental protocols have been improved very quickly, many challenges in the scRNA-seq data analysis still need to be overcome. In this chapter, we focus on the introduction and discussion of the research status in the field of scRNA-seq data normalization and cluster analysis, which are the two most important challenges in the scRNA-seq data analysis. Particularly, we present a protocol to discover and validate cancer stem cells (CSCs) using scRNA-seq. Suggestions have also been made to help researchers rationally design their scRNA-seq experiments and data analysis in their future studies.

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