Zhu Li, Ding Ying, Chen Cho-Yi, Wang Lin, Huo Zhiguang, Kim SungHwan, Sotiriou Christos, Oesterreich Steffi, Tseng George C
Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.
Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Bioinformatics. 2017 Apr 15;33(8):1121-1129. doi: 10.1093/bioinformatics/btw788.
Gene co-expression network analysis from transcriptomic studies can elucidate gene-gene interactions and regulatory mechanisms. Differential co-expression analysis helps further detect alterations of regulatory activities in case/control comparison. Co-expression networks estimated from single transcriptomic study is often unstable and not generalizable due to cohort bias and limited sample size. With the rapid accumulation of publicly available transcriptomic studies, co-expression analysis combining multiple transcriptomic studies can provide more accurate and robust results.
In this paper, we propose a meta-analytic framework for detecting differentially co-expressed networks (MetaDCN). Differentially co-expressed seed modules are first detected by optimizing an energy function via simulated annealing. Basic modules sharing common pathways are merged into pathway-centric supermodules and a Cytoscape plug-in (MetaDCNExplorer) is developed to visualize and explore the findings. We applied MetaDCN to two breast cancer applications: ER+/ER- comparison using five training and three testing studies, and ILC/IDC comparison with two training and two testing studies. We identified 20 and 4 supermodules for ER+/ER- and ILC/IDC comparisons, respectively. Ranking atop are 'immune response pathway' and 'complement cascades pathway' for ER comparison, and 'extracellular matrix pathway' for ILC/IDC comparison. Without the need for prior information, the results from MetaDCN confirm existing as well as discover novel disease mechanisms in a systems manner.
R package 'MetaDCN' and Cytoscape App 'MetaDCNExplorer' are available at http://tsenglab.biostat.pitt.edu/software.htm .
Supplementary data are available at Bioinformatics online.
转录组学研究中的基因共表达网络分析可以阐明基因-基因相互作用和调控机制。差异共表达分析有助于在病例/对照比较中进一步检测调控活动的变化。由于队列偏差和样本量有限，从单一转录组学研究估计的共表达网络通常不稳定且不可推广。随着公开可用转录组学研究的快速积累，结合多个转录组学研究的共表达分析可以提供更准确和可靠的结果。
在本文中，我们提出了一种用于检测差异共表达网络的元分析框架（MetaDCN）。首先通过模拟退火优化能量函数来检测差异共表达的种子模块。共享共同途径的基本模块被合并为以途径为中心的超级模块，并开发了一个Cytoscape插件（MetaDCNExplorer）来可视化和探索研究结果。我们将MetaDCN应用于两个乳腺癌应用：使用五项训练和三项测试研究进行ER+/ER-比较，以及使用两项训练和两项测试研究进行ILC/IDC比较。我们分别为ER+/ER-和ILC/IDC比较确定了20个和4个超级模块。在ER比较中排名靠前的是“免疫反应途径”和“补体级联途径”，在ILC/IDC比较中是“细胞外基质途径”。无需先验信息，MetaDCN的结果以系统的方式证实了现有的疾病机制，并发现了新的疾病机制。
R包“MetaDCN”和Cytoscape应用程序“MetaDCNExplorer”可在http://tsenglab.biostat.pitt.edu/software.htm获得。
补充数据可在《生物信息学》在线获取。
