Chapter title |
Single-Cell Transcriptome Analysis Using SINCERA Pipeline
|
---|---|
Chapter number | 15 |
Book title |
Transcriptome Data Analysis
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7710-9_15 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7709-3, 978-1-4939-7710-9
|
Authors |
Minzhe Guo, Yan Xu, Guo, Minzhe, Xu, Yan |
Abstract |
Genome-scale single-cell biology has recently emerged as a powerful technology with important implications for both basic and medical research. There are urgent needs for the development of computational methods or analytic pipelines to facilitate large amounts of single-cell RNA-Seq data analysis. Here, we present a detailed protocol for SINCERA (SINgle CEll RNA-Seq profiling Analysis), a generally applicable analytic pipeline for processing single-cell data from a whole organ or sorted cells. The pipeline supports the analysis for the identification of major cell types, cell type-specific gene signatures, and driving forces of given cell types. In this chapter, we provide step-by-step instructions for the functions and features of SINCERA together with application examples to provide a practical guide for the research community. SINCERA is implemented in R, licensed under the GNU General Public License v3, and freely available from CCHMC PBGE website, https://research.cchmc.org/pbge/sincera.html . |
X Demographics
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 40% |
Austria | 1 | 20% |
Unknown | 2 | 40% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 80% |
Scientists | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 17 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 5 | 29% |
Researcher | 3 | 18% |
Student > Doctoral Student | 1 | 6% |
Lecturer > Senior Lecturer | 1 | 6% |
Student > Master | 1 | 6% |
Other | 1 | 6% |
Unknown | 5 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 3 | 18% |
Agricultural and Biological Sciences | 3 | 18% |
Computer Science | 2 | 12% |
Medicine and Dentistry | 2 | 12% |
Neuroscience | 2 | 12% |
Other | 1 | 6% |
Unknown | 4 | 24% |