Chapter title |
Pathway and Network Analysis of Differentially Expressed Genes in Transcriptomes
|
---|---|
Chapter number | 3 |
Book title |
Transcriptome Data Analysis
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7710-9_3 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7709-3, 978-1-4939-7710-9
|
Authors |
Qianli Huang, Ming-an Sun, Ping Yan |
Abstract |
In recent years, transcriptome sequencing has become very popular, encompassing a wide variety of applications from simple mRNA profiling to discovery and analysis of the entire transcriptome. One of the most common aims of transcriptome sequencing is to identify genes that are differentially expressed (DE) between two or more biological conditions, and to infer associated pathways and gene networks from expression profiles. It can provide avenues for further systematic investigation into potential biologic mechanisms. Gene Set (GS) enrichment analysis is a popular approach to identify pathways or sets of genes that are significantly enriched in the context of differentially expressed genes. However, the approach considers a pathway as a simple gene collection disregarding knowledge of gene or protein interactions. In contrast, topology-based methods integrate the topological structure of a pathway and gene network into the analysis. To provide a panoramic view of such approaches, this chapter demonstrates several recent computational workflows, including gene set enrichment and topology-based methods, for analysis of the DE pathways and gene networks from transcriptome-wide sequencing data. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 1 | 33% |
Germany | 1 | 33% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 27 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 7 | 26% |
Student > Master | 4 | 15% |
Student > Bachelor | 4 | 15% |
Researcher | 4 | 15% |
Unspecified | 1 | 4% |
Other | 1 | 4% |
Unknown | 6 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 7 | 26% |
Agricultural and Biological Sciences | 5 | 19% |
Immunology and Microbiology | 4 | 15% |
Medicine and Dentistry | 2 | 7% |
Computer Science | 1 | 4% |
Other | 1 | 4% |
Unknown | 7 | 26% |