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Background

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.

Methodology

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.

Conclusions

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.



Autor: Matthew V. DiLeo , Gary D. Strahan , Meghan den Bakker, Owen A. Hoekenga

Fuente: http://plos.srce.hr/



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