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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Functional Module Analysis for Gene Coexpression Networks with Network Integration
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Functional Module Analysis for Gene Coexpression Networks with Network Integration

机译：具有网络集成的基因共表达网络的功能模块分析

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摘要

Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.
机译：网络已经成为研究不同基因，蛋白质和其他小分子之间复杂相互作用的通用工具。作为许多生物网络的基本属性的模块已经被广泛研究，并且已经提出了许多计算方法来识别单个网络中的模块。然而，在许多情况下，由于构建生物网络时数据中的噪声或参数的调整，单个网络不足以进行模块分析。大量生物网络的可用性使网络集成研究成为可能。通过集成这样的网络，可以从由不同组织构建的网络中获取有关某些特定疾病的更多信息模块，并可以推断出不同疾病的一致因素。在本文中，我们开发了一种有效的方法，用于在不同条件下从多个网络识别模块。该问题被表述为优化模型，该模型将每个单独网络中的模块标识与来自不同网络的模块的对齐方式组合在一起。提出了一种基于特征向量计算的近似算法。我们的方法优于现有方法，尤其是当多个网络中的基础模块在仿真研究中不同时。我们还将我们的方法应用于人类的两组基因共表达网络，其中包括来自病态肥胖患者的三种癌症和一组三种组织。我们分别确定了13个具有三个完整子图的模块和11个具有两个完整子图的模块。通过基因本体论富集和KEGG途径富集分析验证了模块。我们还表明，其他模块已经解决了大多数模块对相应疾病的主要功能，这可能为进一步实验研究模块提供理论依据。

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