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Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. (Computational Life Science Cluster (CLiC))
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC). (Department of Forest Genetics and Plant Physiology)
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. (Computational Life Science Cluster (CLiC))
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC). (Department of Forest Genetics and Plant Physiology)
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2012 (Engelska)Ingår i: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 84, nr 20, s. 8675-8681Artikel i tidskrift (Refereegranskat) Published
Abstract [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.

Ort, förlag, år, upplaga, sidor
2012. Vol. 84, nr 20, s. 8675-8681
Nyckelord [en]
Phenotypic profiling, functional genomics, OPLS-DA, multivariate, Py-GC-MS, data integration
Nationell ämneskategori
Analytisk kemi
Identifikatorer
URN: urn:nbn:se:umu:diva-60030DOI: 10.1021/ac301869pOAI: oai:DiVA.org:umu-60030DiVA, id: diva2:557671
Forskningsfinansiär
Bio4EnergyeSSENCE - An eScience CollaborationFormasVetenskapsrådet, VR, Grant No. 2011-6044VinnovaTillgänglig från: 2012-09-28 Skapad: 2012-09-28 Senast uppdaterad: 2018-06-08Bibliografiskt granskad

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Pinto, Rui ClimacoTrygg, Johan

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Pinto, Rui ClimacoTrygg, Johan
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Kemiska institutionenUmeå Plant Science Centre (UPSC)
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Analytical Chemistry
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