Computational Systems Biology Methods and Protocols.7z

Chapter 18

Data Analysis in Single-Cell Transcriptome Sequencing

Shan Gao

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 wordsscRNA-seq, Single-cell transcriptome sequencing, Normalization, Cluster analysis

1 Introduction

Single-cell transcriptome sequencing is used to simultaneously

measure the expression levels of genes from a single cell and pro-

vides a higher resolution of cellular differences than bulk RNA-seq,

which can only produce an expression value for each gene by

averaging its expression levels across a large population of cells.

Single-cell transcriptome sequencing is also referred to as single-

cell RNA sequencing (scRNA-seq) that often uses the next-

generation sequencing (NGS) technologies, although it can also

use the single molecule sequencing (SMS) technologies [1]. Like

bulk RNA-seq, a complete scRNA-seq procedure contains RNA

extraction, cDNA amplification, sequencing library preparation,

sequencing, and data analysis. In addition, scRNA-seq needs isola-

tion of single cells by manual fluorescence-activated cell sorting

(FACS) or by using a microfluidics-based system.

To ensure that scRNA-seq data are fully exploited and inter-

preted correctly, it is crucial to apply appropriate computational and

statistical methods in the data analysis [2]. The scRNA-seq data

Tao Huang (ed.),Computational Systems Biology: Methods and Protocols, Methods in Molecular Biology, vol. 1754,
https://doi.org/10.1007/978-1-4939-7717-8_18,©Springer Science+Business Media, LLC, part of Springer Nature 2018

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