MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions
MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions
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DOI：
10.1093/bioinformatics/bty650
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发表时间：
2019-02-01
期刊：
影响因子：
5.8
通讯作者：
Krumsiek, Jan
中科院分区：
文献类型：
--
作者：
Do, Kieu Trinh;Rasp, David J. N. -P.;Krumsiek, Jan
代谢组学数据与表型结果的关联预计将跨越功能模块，功能模块被定义为协调调节的相关代谢物的集合。此外，这些关联发生在不同的尺度上，从整个途径到仅几种代谢物;这是以前的方法没有解决的一个方面。在这里，我们提出了MoDentify，一个免费的R软件包，用于在不同的分辨率层识别代谢组学网络中的调节模块。重要的是，MoDentify显示出比经典关联分析更高的统计能力。此外，该软件包还提供了Cytoscape中结果的直接交互式可视化。我们提出了一个应用程序的例子，使用复杂的，多流体代谢组学数据。由于其通用性，该方法广泛适用于其他类型的数据。
Associations of metabolomics data with phenotypic outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites; an aspect that has not been addressed by previous methods. Here, we present MoDentify, a free R package to identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct interactive visualization of the results in Cytoscape. We present an application example using complex, multifluid metabolomics data. Due to its generic character, the method is widely applicable to other types of data.