Methods for exploring genetic interactions have been developed in an attempt to move beyond single gene analyses. Because biological molecules frequently participate in different processes under various cellular conditions, investigating the changes in gene coexpression patterns under various biological conditions could reveal important regulatory mechanisms. One of the methods for capturing gene coexpression dynamics, named liquid association (LA), quantifies the relationship where the coexpression between two genes is modulated by a third "coordinator" gene. This LA measure offers a natural framework for studying gene coexpression changes and has been applied increasingly to study regulatory networks among genes. With a wealth of publicly available gene expression data, there is a need to develop a meta-analytic framework for LA analysis. In this paper, we incorporated mixed effects when modeling correlation to account for between-studies heterogeneity. For statistical inference about LA, we developed a Markov chain Monte Carlo (MCMC) estimation procedure through a Bayesian hierarchical framework. We evaluated the proposed methods in a set of simulations and illustrated their use in two collections of experimental data sets. The first data set combined 10 pancreatic ductal adenocarcinoma gene expression studies to determine the role of possible coordinator gene USP9X in the Hippo pathway. The second experimental data set consisted of 907 gene expression microarray Escherichia coli experiments from multiple studies publicly available through the Many Microbe Microarray Database website (http://m3d.bu.edu/) and examined genes that coexpress with serA in the presence of coordinator gene Lrp.

中文翻译：

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建立遗传共表达动力学模型的元分析框架。

为了超越单基因分析，已经开发了探索遗传相互作用的方法。由于生物分子在各种细胞条件下经常参与不同的过程，因此研究在各种生物条件下基因共表达模式的变化可能揭示重要的调节机制。一种捕获基因共表达动态的方法，称为液体缔合（LA），用于量化其中两个基因之间的共表达受第三个“协调子”基因调控的关系。此LA措施为研究基因共表达变化提供了自然的框架，并已越来越多地用于研究基因之间的调控网络。借助大量公开可用的基因表达数据，有必要开发用于洛杉矶分析的元分析框架。在本文中，我们在对相关性进行建模时纳入了混合效应，以解决研究之间的异质性。为了进行关于LA的统计推断，我们通过贝叶斯层次框架开发了一个马尔可夫链蒙特卡洛（MCMC）估计程序。我们在一组模拟中评估了所提出的方法，并说明了它们在两个实验数据集中的使用。第一个数据集结合了10个胰腺导管腺癌基因表达研究，以确定可能的协调基因USP9X在Hippo途径中的作用。第二个实验数据集包含907个基因表达微阵列大肠杆菌实验，这些实验来自可通过Many Microbe Microarray Database网站（http：//m3d.bu。