Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 499502
Title Improved batch correction in untargeted MS-based metabolomics
Author(s) Wehrens, Ron; Hageman, Jos A.; Eeuwijk, Fred van; Kooke, Rik; Flood, Pádraic J.; Wijnker, Erik; Keurentjes, Joost J.B.; Lommen, Arjen; Eekelen, Henriëtte D.L.M. van; Hall, Robert D.; Mumm, Roland; Vos, Ric C.H. de
Source Metabolomics 12 (2016)5. - ISSN 1573-3882
DOI https://doi.org/10.1007/s11306-016-1015-8
Department(s) PRI BIOS Applied Metabolic Systems
Biometris (WU MAT)
Biometris (PPO/PRI)
PE&RC
Groep KoornneefGroep Koornneef
Horticulture and Product Physiology Group
Laboratory of Genetics
EPS
RIKILT - Business unit Contaminants & Toxins
VLAG
Publication type Refereed Article in a scientific journal
Publication year 2016
Keyword(s) Arabidopsis thaliana - Batch correction - Mass spectrometry - Non-detects - Untargeted metabolomics
Abstract

Introduction: Batch effects in large untargeted metabolomics experiments are almost unavoidable, especially when sensitive detection techniques like mass spectrometry (MS) are employed. In order to obtain peak intensities that are comparable across all batches, corrections need to be performed. Since non-detects, i.e., signals with an intensity too low to be detected with certainty, are common in metabolomics studies, the batch correction methods need to take these into account. Objectives: This paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects. Methods: Batch correction methods usually consist of regression models, possibly also accounting for trends within batches. To fit these models quality control samples (QCs), injected at regular intervals, can be used. Also study samples can be used, provided that the injection order is properly randomized. Normalization methods, not using information on batch labels or injection order, can correct for batch effects as well. Introducing two easy-to-use quality criteria, we assess the merits of these batch correction strategies using three large LC–MS and GC–MS data sets of samples from Arabidopsis thaliana. Results: The three data sets have very different characteristics, leading to clearly distinct behaviour of the batch correction strategies studied. Explicit inclusion of information on batch and injection order in general leads to very good corrections; when enough QCs are available, also general normalization approaches perform well. Several approaches are shown to be able to handle non-detects—replacing them with very small numbers such as zero seems the worst of the approaches considered. Conclusion: The use of quality control samples for batch correction leads to good results when enough QCs are available. If an experiment is properly set up, batch correction using the study samples usually leads to a similar high-quality correction, but has the advantage that more metabolites are corrected. The strategy for handling non-detects is important: choosing small values like zero can lead to suboptimal batch corrections.

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