Kinzy Tyler G, Starr Timothy K, Tseng George C, Ho Yen-Yi
Case Western Reserve University, Cleveland, USA.
University of Minnesota, Minneapolis, USA.
Stat Appl Genet Mol Biol. 2019 Feb 9;18(1):/j/sagmb.2019.18.issue-1/sagmb-2017-0052/sagmb-2017-0052.xml. doi: 10.1515/sagmb-2017-0052.
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.
为了超越单基因分析，人们开发了探索基因相互作用的方法。由于生物分子在各种细胞条件下经常参与不同的过程，研究各种生物条件下基因共表达模式的变化可以揭示重要的调控机制。一种捕捉基因共表达动态的方法，称为液体关联（LA），量化了两个基因之间的共表达受第三个“协调”基因调控的关系。这种LA测量为研究基因共表达变化提供了一个自然框架，并越来越多地应用于研究基因间的调控网络。鉴于有大量公开可用的基因表达数据，有必要开发一个用于LA分析的元分析框架。在本文中，我们在对相关性建模时纳入了混合效应，以考虑研究间的异质性。对于LA的统计推断，我们通过贝叶斯分层框架开发了一种马尔可夫链蒙特卡罗（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集合中的应用。第一个数据集结合了10项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通路中的作用。第二个实验数据集由通过许多微生物微阵列数据库网站（http://m3d.bu.edu/）公开提供的来自多项研究的907个基因表达微阵列大肠杆菌实验组成，并研究了在协调基因Lrp存在下与serA共表达的基因。
