Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.
Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.
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DOI：
10.1371/journal.pone.0026683
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Hoekenga OA
中科院分区：
文献类型：
--
作者：
DiLeo MV;Strahan GD;den Bakker M;Hoekenga OA
“组学”技术的进步使生物数据的收集发生了革命性的变化。然而，只有在对由此产生的“大数据”进行信息分析时取得类似进展，我们对生物系统的理解才会发生相应的革命。在这里，我们比较了三种传统和新颖的统计方法来总结和破译番茄代谢组的能力。采用主成分分析（PCA）、批量学习自组织图谱（BL-SOM）和加权基因共表达网络分析（WGCNA）对不同基因型番茄发育阶段果实的多变量核磁共振数据进行了分析。虽然PCA和BL-SOM是合适和常用的方法，但WGCNA在分析高度多元、复杂的数据方面具有几个优势。PCA分离了两个主要的遗传背景（AC和NC），但提供了很少的进一步信息。BL-SOM和WGCNA都通过表达对代谢物进行聚类，但WGCNA还明确定义了共表达代谢物的“模块”，并提供了描述番茄代谢网络系统特性的额外网络统计数据。我们首次将WGCNA应用于番茄代谢组学数据，确定了与成熟相关性状和遗传背景相关的代谢物的三个主要模块。
Advances in “omics” technologies have revolutionized the collection of biological data. A matching revolution in our understanding of biological systems, however, will only be realized when similar advances are made in informatic analysis of the resulting “big data.” Here, we compare the capabilities of three conventional and novel statistical approaches to summarize and decipher the tomato metabolome. Principal component analysis (PCA), batch learning self-organizing maps (BL-SOM) and weighted gene co-expression network analysis (WGCNA) were applied to a multivariate NMR dataset collected from developmentally staged tomato fruits belonging to several genotypes. While PCA and BL-SOM are appropriate and commonly used methods, WGCNA holds several advantages in the analysis of highly multivariate, complex data. PCA separated the two major genetic backgrounds (AC and NC), but provided little further information. Both BL-SOM and WGCNA clustered metabolites by expression, but WGCNA additionally defined “modules” of co-expressed metabolites explicitly and provided additional network statistics that described the systems properties of the tomato metabolic network. Our first application of WGCNA to tomato metabolomics data identified three major modules of metabolites that were associated with ripening-related traits and genetic background.
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