| Literature DB >> 34902128 |
Alisa Pavel1,2,3, Angela Serra1,2,3, Luca Cattelani1,2,3, Antonio Federico1,2,3, Dario Greco4,5,6,7.
Abstract
DNA microarrays are widely used to investigate gene expression. Even though the classical analysis of microarray data is based on the study of differentially expressed genes, it is well known that genes do not act individually. Network analysis can be applied to study association patterns of the genes in a biological system. Moreover, it finds wide application in differential coexpression analysis between different systems. Network based coexpression studies have for example been used in (complex) disease gene prioritization, disease subtyping, and patient stratification.In this chapter we provide an overview of the methods and tools used to create networks from microarray data and describe multiple methods on how to analyze a single network or a group of networks. The described methods range from topological metrics, functional group identification to data integration strategies, topological pathway analysis as well as graphical models.Entities:
Keywords: Coexpression; Differential coexpression; Microarray; Multilayer networks; Pathways
Mesh：
Year: 2022 PMID： 34902128 DOI： 10.1007/978-1-0716-1839-4_11
Source DB: PubMed Journal: Methods Mol Biol ISSN： 1064-3745