Weighted Correlation Network Analysis (WGCNA) Applied to the Tomato Fruit Metabolome

• DOI: 10.1371/journal.pone.0026683
• Publication Date: 2011-10-21T17:30:57Z
• Authors: Matthew V. DiLeo; Gary D. Strahan; Meghan den Bakker; Owen A. Hoekenga
• Keywords: Metabolome

Background Advances in "omics" technologies have revolutionized the collection of biological data. A matching revolution our understanding systems, however, will only be realized when similar advances are made informatic analysis resulting "big data." Here, we compare capabilities three conventional and novel statistical approaches to summarize decipher tomato metabolome. Methodology Principal component (PCA), batch learning self-organizing maps (BL-SOM) weighted gene co-expression network (WGCNA) were applied a multivariate NMR dataset collected from developmentally staged fruits belonging several genotypes. While PCA BL-SOM appropriate commonly used methods, WGCNA holds advantages highly multivariate, complex Conclusions separated two major genetic backgrounds (AC NC), but provided little further information. Both clustered metabolites by expression, additionally defined "modules" co-expressed explicitly additional statistics that described systems properties metabolic network. Our first application metabolomics data identified modules associated with ripening-related traits background.