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

<h4>Background</h4>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 &qu...

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Autores principales:	Matthew V DiLeo, Gary D Strahan, Meghan den Bakker, Owen A Hoekenga
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/dbd64d8a15704019a08d2988e1af1b04
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spelling	oai:doaj.org-article:dbd64d8a15704019a08d2988e1af1b042021-11-18T07:35:55ZWeighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.1932-620310.1371/journal.pone.0026683https://doaj.org/article/dbd64d8a15704019a08d2988e1af1b042011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22039529/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Methodology</h4>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.<h4>Conclusions</h4>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.Matthew V DiLeoGary D StrahanMeghan den BakkerOwen A HoekengaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 10, p e26683 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matthew V DiLeo Gary D Strahan Meghan den Bakker Owen A Hoekenga Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.
description	<h4>Background</h4>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.<h4>Methodology</h4>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.<h4>Conclusions</h4>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.
format	article
author	Matthew V DiLeo Gary D Strahan Meghan den Bakker Owen A Hoekenga
author_facet	Matthew V DiLeo Gary D Strahan Meghan den Bakker Owen A Hoekenga
author_sort	Matthew V DiLeo
title	Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.
title_short	Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.
title_full	Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.
title_fullStr	Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.
title_full_unstemmed	Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.
title_sort	weighted correlation network analysis (wgcna) applied to the tomato fruit metabolome.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/dbd64d8a15704019a08d2988e1af1b04
work_keys_str_mv	AT matthewvdileo weightedcorrelationnetworkanalysiswgcnaappliedtothetomatofruitmetabolome AT garydstrahan weightedcorrelationnetworkanalysiswgcnaappliedtothetomatofruitmetabolome AT meghandenbakker weightedcorrelationnetworkanalysiswgcnaappliedtothetomatofruitmetabolome AT owenahoekenga weightedcorrelationnetworkanalysiswgcnaappliedtothetomatofruitmetabolome
_version_	1718423254468657152

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

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