Prom-On Santitham, Chanthaphan Atthawut, Chan Jonathan Hoyin, Meechai Asawin
Computer Engineering Department, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Prachauthit Road, Bangmod, Thungkhru, Bangkok 10140, Thailand.
J Bioinform Comput Biol. 2011 Feb;9(1):111-29. doi: 10.1142/s0219720011005252.
Relationships among gene expression levels may be associated with the mechanisms of the disease. While identifying a direct association such as a difference in expression levels between case and control groups links genes to disease mechanisms, uncovering an indirect association in the form of a network structure may help reveal the underlying functional module associated with the disease under scrutiny. This paper presents a method to improve the biological relevance in functional module identification from the gene expression microarray data by enhancing the structure of a weighted gene co-expression network using minimum spanning tree. The enhanced network, which is called a backbone network, contains only the essential structural information to represent the gene co-expression network. The entire backbone network is decoupled into a number of coherent sub-networks, and then the functional modules are reconstructed from these sub-networks to ensure minimum redundancy. The method was tested with a simulated gene expression dataset and case-control expression datasets of autism spectrum disorder and colorectal cancer studies. The results indicate that the proposed method can accurately identify clusters in the simulated dataset, and the functional modules of the backbone network are more biologically relevant than those obtained from the original approach.
基因表达水平之间的关系可能与疾病机制相关。虽然识别直接关联（如病例组和对照组之间表达水平的差异）可将基因与疾病机制联系起来，但以网络结构形式揭示间接关联可能有助于揭示与所研究疾病相关的潜在功能模块。本文提出了一种方法，通过使用最小生成树增强加权基因共表达网络的结构，来提高从基因表达微阵列数据中识别功能模块时的生物学相关性。增强后的网络称为骨干网络，仅包含表示基因共表达网络的基本结构信息。整个骨干网络被解耦为多个连贯的子网络，然后从这些子网络重建功能模块以确保最小冗余。该方法用模拟基因表达数据集以及自闭症谱系障碍和结直肠癌研究的病例对照表达数据集进行了测试。结果表明，所提出的方法能够准确识别模拟数据集中的聚类，并且骨干网络的功能模块比从原始方法获得的功能模块具有更强的生物学相关性。
