Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis
2012 (English)In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 84, no 20, 8675-8681 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2012. Vol. 84, no 20, 8675-8681 p.
Phenotypic profiling, functional genomics, OPLS-DA, multivariate, Py-GC-MS, data integration
IdentifiersURN: urn:nbn:se:umu:diva-60030DOI: 10.1021/ac301869pOAI: oai:DiVA.org:umu-60030DiVA: diva2:557671
FunderBio4EnergyeSSENCE - An eScience CollaborationFormasSwedish Research Council, VR, Grant No. 2011-6044Vinnova