Network Analysis of Microarray Data

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

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.

Original languageEnglish
Title of host publicationMicroarray Data Analysis
EditorsGiuseppe Agapito
PublisherHumana Press
Pages161-186
Number of pages26
ISBN (Electronic)978-1-0716-1839-4
ISBN (Print)978-1-0716-1841-7
DOIs
Publication statusPublished - 2022
Publication typeA3 Part of a book or another research book

Publication series

NameMethods in Molecular Biology
Volume2401
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

Keywords

  • Coexpression
  • Differential coexpression
  • Microarray
  • Multilayer networks
  • Pathways

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics

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