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Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.

DiLeo MV, Strahan GD, den Bakker M, Hoekenga OA - PLoS ONE (2011)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Boyce Thompson Institute for Plant Research, Ithaca, New York, United States of America.

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.

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.

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Related in: MedlinePlus

Principal component analysis (PCA) of metabolic profiles of whole tomato fruit.Six tomato genotypes from two genetic backgrounds were analyzed by PCA using 46 NMR-profiled metabolites. Within each background (AC, an heirloom variety; NC, modern production varieties), variation existed at the Rin locus such that one fully ripening type (red squares), one partially ripening type (orange diamonds), and one non-ripening type (yellow circles) existed. Ten individual fruits were profiled per genotype.
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pone-0026683-g001: Principal component analysis (PCA) of metabolic profiles of whole tomato fruit.Six tomato genotypes from two genetic backgrounds were analyzed by PCA using 46 NMR-profiled metabolites. Within each background (AC, an heirloom variety; NC, modern production varieties), variation existed at the Rin locus such that one fully ripening type (red squares), one partially ripening type (orange diamonds), and one non-ripening type (yellow circles) existed. Ten individual fruits were profiled per genotype.

Mentions: PCA of non-targeted NMR profiles grouped the six genotypes into two primary clusters with clear separation by genetic background (Figure 1). Principal component 1 explained 25.2 % of the variation in the data and roughly separated genotypes based on ripening phenotype and Rin allele status. This component was associated positively with formate, threonine, glutamate, phenylalanine, leucine, GABA, τ-methylhistidine, tyrosine and NADP (loading values >0.20) and negatively with malic acid (loading value <− 0.10). While principal component 1 separated the three varieties in the AC background by apparent degree of ripeness, an explanation for the relative positions of the three NC varieties was not as apparent. Principal component 2 was positively associated with this difference between AC and NC backgrounds and explained 15.7 % of the variation in the data. This component was associated positively with indole-3-acetic acid, malic acid, an incompletely identified sterol, chlorogenic acid, citrate, and coumaric acid (loading values >0.20) and negatively with aspartate, indole, glucose and cytidine (loading values <−0.20).


Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.

DiLeo MV, Strahan GD, den Bakker M, Hoekenga OA - PLoS ONE (2011)

Principal component analysis (PCA) of metabolic profiles of whole tomato fruit.Six tomato genotypes from two genetic backgrounds were analyzed by PCA using 46 NMR-profiled metabolites. Within each background (AC, an heirloom variety; NC, modern production varieties), variation existed at the Rin locus such that one fully ripening type (red squares), one partially ripening type (orange diamonds), and one non-ripening type (yellow circles) existed. Ten individual fruits were profiled per genotype.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3198806&req=5

pone-0026683-g001: Principal component analysis (PCA) of metabolic profiles of whole tomato fruit.Six tomato genotypes from two genetic backgrounds were analyzed by PCA using 46 NMR-profiled metabolites. Within each background (AC, an heirloom variety; NC, modern production varieties), variation existed at the Rin locus such that one fully ripening type (red squares), one partially ripening type (orange diamonds), and one non-ripening type (yellow circles) existed. Ten individual fruits were profiled per genotype.
Mentions: PCA of non-targeted NMR profiles grouped the six genotypes into two primary clusters with clear separation by genetic background (Figure 1). Principal component 1 explained 25.2 % of the variation in the data and roughly separated genotypes based on ripening phenotype and Rin allele status. This component was associated positively with formate, threonine, glutamate, phenylalanine, leucine, GABA, τ-methylhistidine, tyrosine and NADP (loading values >0.20) and negatively with malic acid (loading value <− 0.10). While principal component 1 separated the three varieties in the AC background by apparent degree of ripeness, an explanation for the relative positions of the three NC varieties was not as apparent. Principal component 2 was positively associated with this difference between AC and NC backgrounds and explained 15.7 % of the variation in the data. This component was associated positively with indole-3-acetic acid, malic acid, an incompletely identified sterol, chlorogenic acid, citrate, and coumaric acid (loading values >0.20) and negatively with aspartate, indole, glucose and cytidine (loading values <−0.20).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Boyce Thompson Institute for Plant Research, Ithaca, New York, United States of America.

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.

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.

Show MeSH
Related in: MedlinePlus