Improved batch correction in untargeted MS-based metabolomics View Full Text


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Article Info

DATE

2016-03-18

AUTHORS

Ron Wehrens, Jos. A. Hageman, Fred van Eeuwijk, Rik Kooke, Pádraic J. Flood, Erik Wijnker, Joost J. B. Keurentjes, Arjen Lommen, Henriëtte D. L. M. van Eekelen, Robert D. Hall, Roland Mumm, Ric C. H. de Vos

ABSTRACT

IntroductionBatch 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. ObjectivesThis paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects.MethodsBatch 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.ResultsThe 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.ConclusionThe 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. More... »

PAGES

88

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    22 schema:description IntroductionBatch 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. ObjectivesThis paper aims to compare several batch correction methods, and investigates the effect of different strategies for handling non-detects.MethodsBatch 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.ResultsThe 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.ConclusionThe 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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    28 schema:keywords Arabidopsis thaliana
    29 GC-MS data sets
    30 LC-MS
    31 account
    32 advantages
    33 approach
    34 batch
    35 batch correction
    36 batch correction methods
    37 batch effects
    38 behavior
    39 better correction
    40 better results
    41 certainty
    42 characteristics
    43 control samples
    44 correction
    45 correction method
    46 correction strategy
    47 criteria
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    49 detection techniques
    50 different characteristics
    51 different strategies
    52 distinct behaviors
    53 effect
    54 experiments
    55 explicit inclusion
    56 general lead
    57 high-quality correction
    58 inclusion
    59 information
    60 injection order
    61 intensity
    62 interval
    63 labels
    64 lead
    65 mass spectrometry
    66 merits
    67 metabolites
    68 metabolomics
    69 metabolomics experiments
    70 metabolomics studies
    71 method
    72 model
    73 more metabolites
    74 normalization approach
    75 normalization method
    76 number
    77 order
    78 paper
    79 peak intensity
    80 quality control samples
    81 quality criteria
    82 regression models
    83 regular intervals
    84 results
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    86 sensitive detection techniques
    87 set
    88 signals
    89 small number
    90 small values
    91 spectrometry
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