Censi Federica, Calcagnini Giovanni, Mattei Eugenio, Giuliani Alessandro
Department of Cardiovascular, Dysmetabolic and Aging-associated Diseases, Italian National Institute of Health, Viale Regina Elena 299, 00161, Rome, Italy.
Department of Environment and Health, Italian National Institute of Health, Viale Regina Elena 299, 00161, Rome, Italy.
Methods Mol Biol. 2018;1687:75-89. doi: 10.1007/978-1-4939-7374-3_6.
Phenotypic changes at different organization levels from cell to entire organism are associated to changes in the pattern of gene expression. These changes involve the entire genome expression pattern and heavily rely upon correlation patterns among genes. The classical approach used to analyze gene expression data builds upon the application of supervised statistical techniques to detect genes differentially expressed among two or more phenotypes (e.g., normal vs. disease). The use of an a posteriori, unsupervised approach based on principal component analysis (PCA) and the subsequent construction of gene correlation networks can shed a light on unexpected behaviour of gene regulation system while maintaining a more naturalistic view on the studied system.In this chapter we applied an unsupervised method to discriminate DMD patient and controls. The genes having the highest absolute scores in the discrimination between the groups were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.
从细胞到整个生物体的不同组织水平上的表型变化与基因表达模式的变化相关。这些变化涉及整个基因组的表达模式，并严重依赖于基因之间的关联模式。用于分析基因表达数据的经典方法基于应用监督统计技术来检测在两种或更多种表型（例如，正常与疾病）之间差异表达的基因。使用基于主成分分析（PCA）的后验无监督方法以及随后构建基因相关网络，可以揭示基因调控系统的意外行为，同时对所研究的系统保持更自然主义的观点。在本章中，我们应用了一种无监督方法来区分杜氏肌营养不良症（DMD）患者和对照组。然后，根据两组中基因的相互关联，对在两组之间的区分中具有最高绝对分数的基因进行基因表达网络分析。相关网络结构表明两组中存在两种不同的基因调控模式，这让人联想到DMD发病机制的重要方面。
