Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.
Nucleic Acids Res. 2014 Apr;42(7):4208-19. doi: 10.1093/nar/gku102. Epub 2014 Feb 4.
Improved methods for integrated analysis of heterogeneous large-scale omic data are direly needed. Here, we take a network-based approach to this challenge. Given two networks, representing different types of gene interactions, we construct a map of linked modules, where modules are genes strongly connected in the first network and links represent strong inter-module connections in the second. We develop novel algorithms that considerably outperform prior art on simulated and real data from three distinct domains. First, by analyzing protein-protein interactions and negative genetic interactions in yeast, we discover epistatic relations among protein complexes. Second, we analyze protein-protein interactions and DNA damage-specific positive genetic interactions in yeast and reveal functional rewiring among protein complexes, suggesting novel mechanisms of DNA damage response. Finally, using transcriptomes of non-small-cell lung cancer patients, we analyze networks of global co-expression and disease-dependent differential co-expression and identify a sharp drop in correlation between two modules of immune activation processes, with possible microRNA control. Our study demonstrates that module maps are a powerful tool for deeper analysis of heterogeneous high-throughput omic data.
改进整合分析异质大规模组学数据的方法是迫切需要的。在这里，我们采用基于网络的方法来应对这一挑战。对于代表两种不同类型基因相互作用的两个网络，我们构建了一个连接模块的图谱，其中模块是在第一个网络中强连接的基因，而链接表示第二个网络中模块之间的强连接。我们开发了新的算法，在来自三个不同领域的模拟和真实数据上的表现明显优于先前的技术。首先，通过分析酵母中的蛋白质-蛋白质相互作用和负遗传相互作用，我们发现了蛋白质复合物之间的上位关系。其次，我们分析了酵母中的蛋白质-蛋白质相互作用和 DNA 损伤特异性正遗传相互作用，揭示了蛋白质复合物之间的功能重排，这表明了 DNA 损伤反应的新机制。最后，我们使用非小细胞肺癌患者的转录组数据，分析了全局共表达和疾病相关差异共表达的网络，发现免疫激活过程中两个模块之间的相关性急剧下降，可能存在 microRNA 控制。我们的研究表明，模块图谱是深入分析异质高通量组学数据的有力工具。
