title: Improved batch correction in untargeted MS-based metabolomics.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  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 fromArabidopsis 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Biometris, Wageningen UR, Wageningen, The Netherlands ; Bioscience, Wageningen UR, Wageningen, The Netherlands.
authors.forename: Ron
authors.identifier: 0000-0002-8798-5599
authors.initials: R
authors.lastname: Wehrens
authors.affiliation: Biometris, Wageningen UR, Wageningen, The Netherlands.
authors.forename: Jos A
authors.initials: JA
authors.lastname: Hageman
authors.affiliation: Biometris, Wageningen UR, Wageningen, The Netherlands.
authors.forename: Fred
authors.initials: F
authors.lastname: van Eeuwijk
authors.affiliation: Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands ; Laboratory of Plant Physiology, Wageningen UR, Wageningen, The Netherlands.
authors.forename: Rik
authors.initials: R
authors.lastname: Kooke
authors.affiliation: Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands ; Horticulture and Production Physiology, Wageningen UR, Wageningen, The Netherlands ; Max Planck Institute For Plant Breeding Research, Cologne, Germany.
authors.forename: Pádraic J
authors.initials: PJ
authors.lastname: Flood
authors.affiliation: Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands ; Developmental Biology, Hamburg University, Hamburg, Germany.
authors.forename: Erik
authors.initials: E
authors.lastname: Wijnker
authors.affiliation: Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands.
authors.forename: Joost J B
authors.initials: JJ
authors.lastname: Keurentjes
authors.affiliation: RIKILT, Wageningen UR, Wageningen, The Netherlands.
authors.forename: Arjen
authors.initials: A
authors.lastname: Lommen
authors.affiliation: Bioscience, Wageningen UR, Wageningen, The Netherlands.
authors.forename: Henriëtte D L M
authors.initials: HD
authors.lastname: van Eekelen
authors.affiliation: Bioscience, Wageningen UR, Wageningen, The Netherlands ; Laboratory of Plant Physiology, Wageningen UR, Wageningen, The Netherlands.
authors.forename: Robert D
authors.initials: RD
authors.lastname: Hall
authors.affiliation: Bioscience, Wageningen UR, Wageningen, The Netherlands.
authors.forename: Roland
authors.initials: R
authors.lastname: Mumm
authors.affiliation: Bioscience, Wageningen UR, Wageningen, The Netherlands.
authors.forename: Ric C H
authors.initials: RC
authors.lastname: de Vos
keywords: Arabidopsis thaliana; Batch correction; Mass spectrometry; Non-detects; Untargeted metabolomics