Chapter title |
Data Analysis in Single-Cell Transcriptome Sequencing
|
---|---|
Chapter number | 18 |
Book title |
Computational Systems Biology
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7717-8_18 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7716-1, 978-1-4939-7717-8
|
Authors |
Gao, Shan, 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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 20% |
Austria | 1 | 20% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 80% |
Scientists | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 106 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 25 | 24% |
Student > Master | 16 | 15% |
Student > Bachelor | 13 | 12% |
Researcher | 12 | 11% |
Student > Doctoral Student | 6 | 6% |
Other | 7 | 7% |
Unknown | 27 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 38 | 36% |
Agricultural and Biological Sciences | 10 | 9% |
Immunology and Microbiology | 7 | 7% |
Medicine and Dentistry | 7 | 7% |
Neuroscience | 6 | 6% |
Other | 9 | 8% |
Unknown | 29 | 27% |