title: Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate 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.
authors.affiliation: Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden.
authors.forename: Rui C
authors.initials: RC
authors.lastname: Pinto
authors.forename: Lorenz
authors.initials: L
authors.lastname: Gerber
authors.forename: Mattias
authors.initials: M
authors.lastname: Eliasson
authors.forename: Björn
authors.initials: B
authors.lastname: Sundberg
authors.forename: Johan
authors.initials: J
authors.lastname: Trygg
mesh_terms: D016000:Cluster Analysis; D016002:Discriminant Analysis; D008401:Gas Chromatography-Mass Spectrometry; D015233:Models, Statistical; D015999:Multivariate Analysis; D010641:Phenotype; D030821:Plants, Genetically Modified / Q000737:chemistry / Q000235:genetics*; D032107:Populus / Q000737:chemistry / Q000235:genetics*; D025341:Principal Component Analysis; D014197:Trees / Q000737:chemistry / Q000235:genetics*; D014934:Wood / Q000737:chemistry / Q000235:genetics*