Strategy for Minimizing Between-Study Variation of Large-Scale Phenotypic Experiments Using Multivariate Analysis

• Published in: Analytical chemistry (Washington) 2012-10, Vol.84 (20), p.8675-8681
• Main Authors: Pinto, Rui C, Gerber, Lorenz, Eliasson, Mattias, Sundberg, Björn, Trygg, Johan
• Format: Article
• Language: English
• Subjects: Analytical Chemistry; Analytisk kemi; Biological variation; Chemistry; Chromatographic methods and physical methods associated with chromatography; Chromatography; Cluster Analysis; data integration; Discriminant Analysis; Exact sciences and technology; functional genomics; Gas chromatographic methods; Gas Chromatography-Mass Spectrometry; Genotype & phenotype; Mass spectrometry; Models, Statistical; multivariate; Multivariate Analysis; OPLS-DA; Phenotype; Phenotypic profiling; Plants, Genetically Modified - chemistry; Plants, Genetically Modified - genetics; Populus - chemistry; Populus - genetics; Principal Component Analysis; Py-GC-MS; Spectrometric and optical methods; Trees; Trees - chemistry; Trees - genetics; Wood - chemistry; Wood - genetics
• ISSN: 0003-2700; 1520-6882; 1520-6882
• DOI: 10.1021/ac301869p

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.