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Data Analysis in Single-Cell Transcriptome Sequencing

  • Shan Gao
Part of the Methods in Molecular Biology book series (MIMB, volume 1754)

Abstract

Single-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.

Key words

scRNA-seq Single-cell transcriptome sequencing Normalization Cluster analysis 

Notes

Acknowledgments

I appreciate help equally from the people listed below. They are Professor Wenjun Bu; Professor Lin Liu; Ph.D. student Hua Wang; Master’s student Yu Sun and Deshui Yu from College of Life Sciences, Nankai University; Professor Jishou Ruan; PhD student Zhenfeng Wu from School of Mathematical Sciences, Nankai University; and Associate Professor Weixiang Liu from Shenzhen University.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Life SciencesNankai UniversityTianjinPeople’s Republic of China
  2. 2.Institute of StatisticsNankai UniversityTianjinPeople’s Republic of China

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