Hsiao Tzu-Hung, Chiu Yu-Chiao, Hsu Pei-Yin, Lu Tzu-Pin, Lai Liang-Chuan, Tsai Mong-Hsun, Huang Tim H-M, Chuang Eric Y, Chen Yidong
Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States of America.
Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.
Sci Rep. 2016 Mar 14;6:23035. doi: 10.1038/srep23035.
Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to construct the estrogen receptor (ER) modulated gene and gene set (representing biological function) interaction networks in breast cancer. Several novel interaction hubs and functional interactions were discovered. ER+ dependent interaction between TGFβ and NFκB was further shown to be associated with patient survival. The findings were verified in independent datasets. Using MAGIC, we also assessed the essential roles of ER modulation in another hormonal cancer, ovarian cancer. Overall, MAGIC is a systematic framework for comprehensively identifying and constructing the modulated interaction networks in a whole-genome landscape. MATLAB implementation of MAGIC is available for academic uses at https://github.com/chiuyc/MAGIC.
已经开发了几种基于互信息（MI）的算法来识别由关键调节因子（基因、蛋白质等）控制的动态基因-基因和功能-功能相互作用。然而，由于计算量较大，这些方法严重依赖先验知识，并且在全基因组分析中受到限制。我们提出了调节基因/基因集相互作用（MAGIC）分析，以系统地识别全基因组范围内的相互作用网络调节。基于一种采用相关系数共轭Fisher变换的新型统计检验，MAGIC具有快速计算能力，并能适应临床队列的变化。在模拟数据集中，MAGIC比基于MI的方法具有显著提高的计算效率和整体优越的性能。我们应用MAGIC构建了乳腺癌中雌激素受体（ER）调节的基因和基因集（代表生物学功能）相互作用网络。发现了几个新的相互作用枢纽和功能相互作用。进一步表明，TGFβ和NFκB之间的ER+依赖性相互作用与患者生存相关。这些发现在独立数据集中得到了验证。使用MAGIC，我们还评估了ER调节在另一种激素癌症——卵巢癌中的重要作用。总体而言，MAGIC是一个用于在全基因组范围内全面识别和构建调节相互作用网络的系统框架。MAGIC的MATLAB实现可在https://github.com/chiuyc/MAGIC上用于学术用途。
