Department of Computer Science and Engineering, Tezpur University, Tezpur, Assam, 784028, India.
Department of Computer Science and Engineering, Tezpur University, Tezpur, Assam, 784028, India.
Comput Biol Med. 2019 Oct;113:103380. doi: 10.1016/j.compbiomed.2019.103380. Epub 2019 Aug 10.
In the recent past, a number of methods have been developed for analysis of biological data. Among these methods, gene co-expression networks have the ability to mine functionally related genes with similar co-expression patterns, because of which such networks have been most widely used. However, gene co-expression networks cannot identify genes, which undergo condition specific changes in their relationships with other genes. In contrast, differential co-expression analysis enables finding co-expressed genes exhibiting significant changes across disease conditions. In this paper, we present some significant outcomes of a comparative study of four co-expression network module detection techniques, namely, THD-Module Extractor, DiffCoEx, MODA, and WGCNA, which can perform differential co-expression analysis on both gene and miRNA expression data (microarray and RNA-seq) and discuss the applications to Alzheimer's disease and Parkinson's disease research. Our observations reveal that compared to other methods, THD-Module Extractor is the most effective in finding modules with higher functional relevance and biological significance.
在最近的一段时间里，已经开发出了许多用于分析生物数据的方法。在这些方法中，基因共表达网络具有挖掘功能相关基因的能力，这些基因具有相似的共表达模式，因此这种网络得到了最广泛的应用。然而，基因共表达网络无法识别在与其他基因的关系中发生特定条件变化的基因。相比之下，差异共表达分析可以找到在疾病状态下表现出显著变化的共表达基因。在本文中，我们介绍了对四种共表达网络模块检测技术（即 THD-Module Extractor、DiffCoEx、MODA 和 WGCNA）的比较研究的一些重要结果，这些技术可以对基因和 miRNA 表达数据（微阵列和 RNA-seq）进行差异共表达分析，并讨论了它们在阿尔茨海默病和帕金森病研究中的应用。我们的观察结果表明，与其他方法相比，THD-Module Extractor 在发现具有更高功能相关性和生物学意义的模块方面最为有效。
