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Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis.

Identifieur interne : 002908 ( Main/Exploration ); précédent : 002907; suivant : 002909

Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis.

Auteurs : Rui C. Pinto [Suède] ; Lorenz Gerber ; Mattias Eliasson ; Björn Sundberg ; Johan Trygg

Source :

Analytical chemistry [ 1520-6882 ] ; 2012.

RBID : pubmed:22978754

Descripteurs français

English descriptors

KwdEn :
- Cluster Analysis (MeSH), Discriminant Analysis (MeSH), Gas Chromatography-Mass Spectrometry (MeSH), Models, Statistical (MeSH), Multivariate Analysis (MeSH), Phenotype (MeSH), Plants, Genetically Modified (chemistry), Plants, Genetically Modified (genetics), Populus (chemistry), Populus (genetics), Principal Component Analysis (MeSH), Trees (chemistry), Trees (genetics), Wood (chemistry), Wood (genetics).
MESH :
- chemistry : Plants, Genetically Modified, Populus, Trees, Wood.
- genetics : Plants, Genetically Modified, Populus, Trees, Wood.
- Cluster Analysis, Discriminant Analysis, Gas Chromatography-Mass Spectrometry, Models, Statistical, Multivariate Analysis, Phenotype, Principal Component Analysis.

Abstract

We have developed a multistep strategy that integrates data from several large-scale experiments that suffer from systematic between-experiment variation. This strategy removes such variation that would otherwise mask differences of interest. It was applied to the evaluation of wood chemical analysis of 736 hybrid aspen trees: wild-type controls and transgenic trees potentially involved in wood formation. The trees were grown in four different greenhouse experiments imposing significant variation between experiments. Pyrolysis coupled to gas chromatography/mass spectrometry (Py-GC/MS) was used as a high throughput-screening platform for fingerprinting of wood chemotype. Our proposed strategy includes quality control, outlier detection, gene specific classification, and consensus analysis. The orthogonal projections to latent structures discriminant analysis (OPLS-DA) method was used to generate the consensus chemotype profiles for each transgenic line. These were thereafter compiled to generate a global dataset. Multivariate analysis and cluster analysis techniques revealed a drastic reduction in between-experiment variation that enabled a global analysis of all transgenic lines from the four independent experiments. Information from in-depth analysis of specific transgenic lines and independent peak identification validated our proposed strategy.

DOI: 10.1021/ac301869p
PubMed: 22978754

Affiliations:

Suède

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<keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de regroupements (MeSH)</term>
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<term>Bois (composition chimique)</term>
<term>Bois (génétique)</term>
<term>Chromatographie gazeuse-spectrométrie de masse (MeSH)</term>
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<front><div type="abstract" xml:lang="en">We have developed a multistep strategy that integrates data from several large-scale experiments that suffer from systematic between-experiment variation. This strategy removes such variation that would otherwise mask differences of interest. It was applied to the evaluation of wood chemical analysis of 736 hybrid aspen trees: wild-type controls and transgenic trees potentially involved in wood formation. The trees were grown in four different greenhouse experiments imposing significant variation between experiments. Pyrolysis coupled to gas chromatography/mass spectrometry (Py-GC/MS) was used as a high throughput-screening platform for fingerprinting of wood chemotype. Our proposed strategy includes quality control, outlier detection, gene specific classification, and consensus analysis. The orthogonal projections to latent structures discriminant analysis (OPLS-DA) method was used to generate the consensus chemotype profiles for each transgenic line. These were thereafter compiled to generate a global dataset. Multivariate analysis and cluster analysis techniques revealed a drastic reduction in between-experiment variation that enabled a global analysis of all transgenic lines from the four independent experiments. Information from in-depth analysis of specific transgenic lines and independent peak identification validated our proposed strategy.</div>
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Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis.

Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis.

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki