Improved batch correction in untargeted MS-based metabolomics

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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.

Original languageEnglish
Article number88
JournalMetabolomics
Volume12
Issue number5
DOIs
Publication statusPublished - 2016

Fingerprint

Metabolomics
Quality control
Mass spectrometry
Mass Spectrometry
Metabolites
Labels
Quality Control
Experiments
Injections
Arabidopsis
Gas Chromatography-Mass Spectrometry

Keywords

  • Arabidopsis thaliana
  • Batch correction
  • Mass spectrometry
  • Non-detects
  • Untargeted metabolomics

Cite this

@article{178039101fce4e688774828c40fab7e5,
title = "Improved batch correction in untargeted MS-based 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.",
keywords = "Arabidopsis thaliana, Batch correction, Mass spectrometry, Non-detects, Untargeted metabolomics",
author = "Ron Wehrens and Hageman, {Jos A.} and {van Eeuwijk}, Fred and Rik Kooke and Flood, {P{\'a}draic J.} and Erik Wijnker and Keurentjes, {Joost J.B.} and Arjen Lommen and {van Eekelen}, {Henri{\"e}tte D.L.M.} and Hall, {Robert D.} and Roland Mumm and {de Vos}, {Ric C.H.}",
year = "2016",
doi = "10.1007/s11306-016-1015-8",
language = "English",
volume = "12",
journal = "Metabolomics",
issn = "1573-3882",
publisher = "Springer New York",
number = "5",

}

TY - JOUR

T1 - Improved batch correction in untargeted MS-based metabolomics

AU - Wehrens, Ron

AU - Hageman, Jos A.

AU - van Eeuwijk, Fred

AU - Kooke, Rik

AU - Flood, Pádraic J.

AU - Wijnker, Erik

AU - Keurentjes, Joost J.B.

AU - Lommen, Arjen

AU - van Eekelen, Henriëtte D.L.M.

AU - Hall, Robert D.

AU - Mumm, Roland

AU - de Vos, Ric C.H.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Arabidopsis thaliana

KW - Batch correction

KW - Mass spectrometry

KW - Non-detects

KW - Untargeted metabolomics

U2 - 10.1007/s11306-016-1015-8

DO - 10.1007/s11306-016-1015-8

M3 - Article

VL - 12

JO - Metabolomics

JF - Metabolomics

SN - 1573-3882

IS - 5

M1 - 88

ER -