title: Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.
abstract: 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.
  
  
  METHODS
  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.
authors: Boyce Thompson Institute for Plant Research, Ithaca, New York, United States of America.
authors: Matthew V
authors: MV
authors: DiLeo
authors: Gary D
authors: GD
authors: Strahan
authors: Meghan
authors: M
authors: den Bakker
authors: Owen A
authors: OA
authors: Hoekenga
mesh_terms: D018551:Solanum lycopersicum / Q000378:metabolism*; D055442:Metabolome; D019906:Nuclear Magnetic Resonance, Biomolecular; D025341:Principal Component Analysis