Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
BioMediTech Institute, Tampere University, Tampere, Finland.
Methods Mol Biol. 2022;2401:161-186. doi: 10.1007/978-1-0716-1839-4_11.
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.
DNA 微阵列被广泛用于研究基因表达。尽管微阵列数据的经典分析基于差异表达基因的研究，但众所周知，基因不是单独起作用的。网络分析可用于研究生物系统中基因的关联模式。此外，它在不同系统之间的差异共表达分析中得到了广泛应用。基于网络的共表达研究已例如用于（复杂）疾病基因优先级排序、疾病亚型分析和患者分层。在本章中，我们提供了从微阵列数据创建网络的方法和工具概述，并描述了分析单个网络或一组网络的多种方法。所描述的方法范围从拓扑度量、功能组识别到数据集成策略、拓扑通路分析以及图形模型。
