{"entity": "publication", "iuid": "b2e6addab2f24f72a5f8964b5587a900", "timestamp": "2026-06-18T14:29:24.049Z", "links": {"self": {"href": "https://publications.scilifelab.se/publication/b2e6addab2f24f72a5f8964b5587a900.json"}, "display": {"href": "https://publications.scilifelab.se/publication/b2e6addab2f24f72a5f8964b5587a900"}}, "title": "Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis.", "authors": [{"family": "Pinto", "given": "Rui C", "initials": "RC"}, {"family": "Gerber", "given": "Lorenz", "initials": "L"}, {"family": "Eliasson", "given": "Mattias", "initials": "M"}, {"family": "Sundberg", "given": "Bj\u00f6rn", "initials": "B"}, {"family": "Trygg", "given": "Johan", "initials": "J"}], "type": "journal article", "published": "2012-10-16", "journal": {"volume": "84", "issn": "1520-6882", "issue": "20", "pages": "8675-8681", "title": "Anal. Chem.", "issn-l": "0003-2700"}, "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", "pmid": "22978754", "labels": {"Bioinformatics Support, Infrastructure and Training": null, "Bioinformatics Support and Infrastructure": null, "Bioinformatics (NBIS)": null}, "xrefs": [], "notes": [], "created": "2017-05-04T14:56:14.540Z", "modified": "2020-01-21T13:53:20.782Z"}