Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases.
Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases.
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DOI：
10.1186/1752-0509-6-65
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发表时间：
2012-06-13
影响因子：
--
通讯作者：
Chance MR
中科院分区：
文献类型：
--
作者：
Liu Y;Koyutürk M;Barnholtz-Sloan JS;Chance MR
生物系统的分子行为可以用三个基本组成部分来描述：（i）物理实体，（ii）这些实体之间的相互作用，以及（iii）这些实体和相互作用的动力学。驱动复杂疾病的机制可以在这些组件的扰动的背景下有效地查看。这方面的一个挑战是确定在特定疾病中改变的途径。为了应对这一挑战，已经开发了基因集富集分析（GSEA）和其他分析，其专注于实体的个体特性（例如基因表达）的改变。然而，关于疾病的相互作用的动力学还没有得到很好的研究（即，组分II和III的性质）。在这里，我们提出了一种新的方法，称为基因相互作用富集和网络分析（GIENA），以确定失调的基因相互作用，即，在疾病和控制之间关系不同的基因对。定义了四个功能来模拟生物学相关的基因相互作用的合作（mRNA表达的总和），竞争（mRNA表达之间的差异），冗余（表达的最大值），或表达水平之间的依赖性（表达的最小值）。建议的框架确定失调的相互作用和途径，丰富的失调的相互作用，指出干扰跨途径的相互作用，而且，基于生物注释的每种类型的失调的相互作用提供了线索的监管逻辑管理系统水平的扰动。我们使用已发表的与癌症相关的数据集证明了GIENA的潜力。我们发现，GIENA识别了基于单个基因特性的传统富集方法所错过的失调途径，并且使用传统方法与GIENA相结合提供了最大数量的相关途径的覆盖。此外，使用GIENA检测到的相互作用，构建和分析了与相关表型相关的通路内和通路间的特定基因网络。
The molecular behavior of biological systems can be described in terms of three fundamental components: (i) the physical entities, (ii) the interactions among these entities, and (iii) the dynamics of these entities and interactions. The mechanisms that drive complex disease can be productively viewed in the context of the perturbations of these components. One challenge in this regard is to identify the pathways altered in specific diseases. To address this challenge, Gene Set Enrichment Analysis (GSEA) and others have been developed, which focus on alterations of individual properties of the entities (such as gene expression). However, the dynamics of the interactions with respect to disease have been less well studied (i.e., properties of components ii and iii). Here, we present a novel method called Gene Interaction Enrichment and Network Analysis (GIENA) to identify dysregulated gene interactions, i.e., pairs of genes whose relationships differ between disease and control. Four functions are defined to model the biologically relevant gene interactions of cooperation (sum of mRNA expression), competition (difference between mRNA expression), redundancy (maximum of expression), or dependency (minimum of expression) among the expression levels. The proposed framework identifies dysregulated interactions and pathways enriched in dysregulated interactions; points out interactions that are perturbed across pathways; and moreover, based on the biological annotation of each type of dysregulated interaction gives clues about the regulatory logic governing the systems level perturbation. We demonstrated the potential of GIENA using published datasets related to cancer. We showed that GIENA identifies dysregulated pathways that are missed by traditional enrichment methods based on the individual gene properties and that use of traditional methods combined with GIENA provides coverage of the largest number of relevant pathways. In addition, using the interactions detected by GIENA, specific gene networks both within and across pathways associated with the relevant phenotypes are constructed and analyzed.
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影响因子：
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通讯作者：
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