title: Strategy for optimizing LC-MS data processing in metabolomics: a design of experiments approach.
abstract: A strategy for optimizing LC-MS metabolomics data processing is proposed. We applied this strategy on the XCMS open source package written in R on both human and plant biology data. The strategy is a sequential design of experiments (DoE) based on a dilution series from a pooled sample and a measure of correlation between diluted concentrations and integrated peak areas. The reliability index metric, used to define peak quality, simultaneously favors reliable peaks and disfavors unreliable peaks using a weighted ratio between peaks with high and low response linearity. DoE optimization resulted in the case studies in more than 57% improvement in the reliability index compared to the use of the default settings. The proposed strategy can be applied to any other data processing software involving parameters to be tuned, e.g., MZmine 2. It can also be fully automated and used as a module in a complete metabolomics data processing pipeline.
authors: Computational Life Science Cluster, Department of Chemistry, Umeå University, SE-901 87 Umeå, Sweden.
authors: Mattias
authors: M
authors: Eliasson
authors: Stefan
authors: S
authors: Rännar
authors: Rasmus
authors: R
authors: Madsen
authors: Magdalena A
authors: MA
authors: Donten
authors: Emma
authors: E
authors: Marsden-Edwards
authors: Thomas
authors: T
authors: Moritz
authors: John P
authors: JP
authors: Shockcor
authors: Erik
authors: E
authors: Johansson
authors: Johan
authors: J
authors: Trygg
mesh_terms: D000328:Adult; D002853:Chromatography, Liquid; D006801:Humans; D008297:Male; D013058:Mass Spectrometry; D055432:Metabolomics; D010944:Plants / Q000378:metabolism; D012107:Research Design; D016482:Urinalysis