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


Ontology type: schema:ScholarlyArticle      Open Access: True


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

References to SciGraph publications

  • 2016-01-23. Multi-platform metabolomics analyses of a broad collection of fragrant and non-fragrant rice varieties reveals the high complexity of grain quality characteristics in METABOLOMICS
  • 2012-05-26. Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics in METABOLOMICS
  • 2011-10-15. MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data in METABOLOMICS
  • 2014-02-01. Metabolomics reveals organ-specific metabolic rearrangements during early tomato seedling development in METABOLOMICS
  • 2011-06-30. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry in NATURE PROTOCOLS
  • 2014-11-02. Experiment design beyond gut feeling: statistical tests and power to detect differential metabolites in mass spectrometry data in METABOLOMICS
  • 2011. Solid Phase Micro-Extraction GC–MS Analysis of Natural Volatile Components in Melon and Rice in PLANT METABOLOMICS
  • 2014-08-24. Normalization of RNA-seq data using factor analysis of control genes or samples in NATURE BIOTECHNOLOGY
  • 1986. Principal Component Analysis in NONE
  • 2013-03-01. Characterising and correcting batch variation in an automated direct infusion mass spectrometry (DIMS) metabolomics workflow in ANALYTICAL AND BIOANALYTICAL CHEMISTRY
  • 2012-03-22. Metabolomics: the apogee of the omics trilogy in NATURE REVIEWS MOLECULAR CELL BIOLOGY
  • 2008. Applied Econometrics with R in NONE
  • 2008-10-17. Evaluation of regression methods when immunological measurements are constrained by detection limits in BMC IMMUNOLOGY
  • 2011. Chemometrics with R, Multivariate Data Analysis in the Natural Sciences and Life Sciences in NONE
  • 2007-04-05. Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry in NATURE PROTOCOLS
  • 2012-01-08. Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel in NATURE GENETICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11306-016-1015-8

    DOI

    http://dx.doi.org/10.1007/s11306-016-1015-8

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1010819562

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27073351


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/03", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Chemical Sciences", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0301", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Analytical Chemistry", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Bioscience, Wageningen UR, Wageningen, The Netherlands", 
              "id": "http://www.grid.ac/institutes/grid.4818.5", 
              "name": [
                "Biometris, Wageningen UR, Wageningen, The Netherlands", 
                "Bioscience, Wageningen UR, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wehrens", 
            "givenName": "Ron", 
            "id": "sg:person.0707004771.27", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0707004771.27"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Biometris, Wageningen UR, Wageningen, The Netherlands", 
              "id": "http://www.grid.ac/institutes/grid.4818.5", 
              "name": [
                "Biometris, Wageningen UR, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hageman", 
            "givenName": "Jos. A.", 
            "id": "sg:person.0770101362.75", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0770101362.75"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Biometris, Wageningen UR, Wageningen, The Netherlands", 
              "id": "http://www.grid.ac/institutes/grid.4818.5", 
              "name": [
                "Biometris, Wageningen UR, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "van Eeuwijk", 
            "givenName": "Fred", 
            "id": "sg:person.0650756633.82", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0650756633.82"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Laboratory of Plant Physiology, Wageningen UR, Wageningen, The Netherlands", 
              "id": "http://www.grid.ac/institutes/grid.4818.5", 
              "name": [
                "Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands", 
                "Laboratory of Plant Physiology, Wageningen UR, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kooke", 
            "givenName": "Rik", 
            "id": "sg:person.0607514543.46", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0607514543.46"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Max Planck Institute For Plant Breeding Research, Cologne, Germany", 
              "id": "http://www.grid.ac/institutes/grid.419498.9", 
              "name": [
                "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"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Flood", 
            "givenName": "P\u00e1draic J.", 
            "id": "sg:person.01314356234.66", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01314356234.66"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Developmental Biology, Hamburg University, Hamburg, Germany", 
              "id": "http://www.grid.ac/institutes/grid.9026.d", 
              "name": [
                "Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands", 
                "Developmental Biology, Hamburg University, Hamburg, Germany"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Wijnker", 
            "givenName": "Erik", 
            "id": "sg:person.01124432620.04", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01124432620.04"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands", 
              "id": "http://www.grid.ac/institutes/grid.4818.5", 
              "name": [
                "Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Keurentjes", 
            "givenName": "Joost J. B.", 
            "id": "sg:person.010104047604.94", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010104047604.94"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "RIKILT, Wageningen UR, Wageningen, The Netherlands", 
              "id": "http://www.grid.ac/institutes/grid.4818.5", 
              "name": [
                "RIKILT, Wageningen UR, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lommen", 
            "givenName": "Arjen", 
            "id": "sg:person.0713030357.17", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0713030357.17"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Bioscience, Wageningen UR, Wageningen, The Netherlands", 
              "id": "http://www.grid.ac/institutes/grid.4818.5", 
              "name": [
                "Bioscience, Wageningen UR, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "van Eekelen", 
            "givenName": "Henri\u00ebtte D. L. M.", 
            "id": "sg:person.0605446667.25", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0605446667.25"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Laboratory of Plant Physiology, Wageningen UR, Wageningen, The Netherlands", 
              "id": "http://www.grid.ac/institutes/grid.4818.5", 
              "name": [
                "Bioscience, Wageningen UR, Wageningen, The Netherlands", 
                "Laboratory of Plant Physiology, Wageningen UR, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hall", 
            "givenName": "Robert D.", 
            "id": "sg:person.0704576323.20", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0704576323.20"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Bioscience, Wageningen UR, Wageningen, The Netherlands", 
              "id": "http://www.grid.ac/institutes/grid.4818.5", 
              "name": [
                "Bioscience, Wageningen UR, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Mumm", 
            "givenName": "Roland", 
            "id": "sg:person.01366416351.05", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01366416351.05"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Bioscience, Wageningen UR, Wageningen, The Netherlands", 
              "id": "http://www.grid.ac/institutes/grid.4818.5", 
              "name": [
                "Bioscience, Wageningen UR, Wageningen, The Netherlands"
              ], 
              "type": "Organization"
            }, 
            "familyName": "de Vos", 
            "givenName": "Ric C. H.", 
            "id": "sg:person.014575024362.22", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014575024362.22"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/s11306-015-0925-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016892332", 
              "https://doi.org/10.1007/s11306-015-0925-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nrm3314", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023146162", 
              "https://doi.org/10.1038/nrm3314"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-17841-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026545920", 
              "https://doi.org/10.1007/978-3-642-17841-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-0-387-77318-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018506708", 
              "https://doi.org/10.1007/978-0-387-77318-6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nbt.2931", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1008162673", 
              "https://doi.org/10.1038/nbt.2931"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-014-0742-y", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041306727", 
              "https://doi.org/10.1007/s11306-014-0742-y"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-014-0625-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003227544", 
              "https://doi.org/10.1007/s11306-014-0625-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nprot.2007.95", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029308877", 
              "https://doi.org/10.1038/nprot.2007.95"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1186/1471-2172-9-59", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007480674", 
              "https://doi.org/10.1186/1471-2172-9-59"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4757-1904-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031639131", 
              "https://doi.org/10.1007/978-1-4757-1904-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00216-013-6856-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1035732243", 
              "https://doi.org/10.1007/s00216-013-6856-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/nprot.2011.335", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015396743", 
              "https://doi.org/10.1038/nprot.2011.335"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-61779-594-7_6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052181974", 
              "https://doi.org/10.1007/978-1-61779-594-7_6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1038/ng.1042", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044562954", 
              "https://doi.org/10.1038/ng.1042"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-012-0434-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1002880572", 
              "https://doi.org/10.1007/s11306-012-0434-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11306-011-0368-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042363301", 
              "https://doi.org/10.1007/s11306-011-0368-2"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2016-03-18", 
        "datePublishedReg": "2016-03-18", 
        "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.\nObjectivesThis 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\u2013MS and GC\u2013MS 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\u2014replacing 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.", 
        "genre": "article", 
        "id": "sg:pub.10.1007/s11306-016-1015-8", 
        "inLanguage": "en", 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1036887", 
            "issn": [
              "1573-3882", 
              "1573-3890"
            ], 
            "name": "Metabolomics", 
            "publisher": "Springer Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "5", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "12"
          }
        ], 
        "keywords": [
          "untargeted mass spectrometry", 
          "quality control samples", 
          "better correction", 
          "study sample", 
          "injection order", 
          "control samples", 
          "regression models", 
          "metabolomics studies", 
          "regular intervals", 
          "more metabolites", 
          "mass spectrometry", 
          "effect", 
          "small number", 
          "LC-MS", 
          "metabolites", 
          "samples", 
          "metabolomics", 
          "strategies", 
          "correction", 
          "sensitive detection techniques", 
          "interval", 
          "better results", 
          "criteria", 
          "batch correction", 
          "study", 
          "quality criteria", 
          "different strategies", 
          "use", 
          "method", 
          "information", 
          "correction strategy", 
          "inclusion", 
          "intensity", 
          "certainty", 
          "number", 
          "lead", 
          "approach", 
          "trends", 
          "batch effects", 
          "results", 
          "characteristics", 
          "metabolomics experiments", 
          "different characteristics", 
          "untargeted metabolomics experiments", 
          "labels", 
          "values", 
          "spectrometry", 
          "order", 
          "technique", 
          "model", 
          "experiments", 
          "GC-MS data sets", 
          "data sets", 
          "batch", 
          "signals", 
          "distinct behaviors", 
          "advantages", 
          "behavior", 
          "normalization approach", 
          "normalization method", 
          "general lead", 
          "account", 
          "set", 
          "peak intensity", 
          "correction method", 
          "detection techniques", 
          "explicit inclusion", 
          "merits", 
          "paper", 
          "small values", 
          "batch correction methods", 
          "Arabidopsis thaliana", 
          "high-quality correction", 
          "thaliana"
        ], 
        "name": "Improved batch correction in untargeted MS-based metabolomics", 
        "pagination": "88", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1010819562"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11306-016-1015-8"
            ]
          }, 
          {
            "name": "pubmed_id", 
            "type": "PropertyValue", 
            "value": [
              "27073351"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11306-016-1015-8", 
          "https://app.dimensions.ai/details/publication/pub.1010819562"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2022-05-10T10:13", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/article/article_711.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://doi.org/10.1007/s11306-016-1015-8"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11306-016-1015-8'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11306-016-1015-8'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11306-016-1015-8'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11306-016-1015-8'


     

    This table displays all metadata directly associated to this object as RDF triples.

    294 TRIPLES      22 PREDICATES      116 URIs      92 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11306-016-1015-8 schema:about anzsrc-for:03
    2 anzsrc-for:0301
    3 schema:author Nb5ebf286c4e044c5bab9dfd0891d5c7e
    4 schema:citation sg:pub.10.1007/978-0-387-77318-6
    5 sg:pub.10.1007/978-1-4757-1904-8
    6 sg:pub.10.1007/978-1-61779-594-7_6
    7 sg:pub.10.1007/978-3-642-17841-2
    8 sg:pub.10.1007/s00216-013-6856-7
    9 sg:pub.10.1007/s11306-011-0368-2
    10 sg:pub.10.1007/s11306-012-0434-4
    11 sg:pub.10.1007/s11306-014-0625-2
    12 sg:pub.10.1007/s11306-014-0742-y
    13 sg:pub.10.1007/s11306-015-0925-1
    14 sg:pub.10.1038/nbt.2931
    15 sg:pub.10.1038/ng.1042
    16 sg:pub.10.1038/nprot.2007.95
    17 sg:pub.10.1038/nprot.2011.335
    18 sg:pub.10.1038/nrm3314
    19 sg:pub.10.1186/1471-2172-9-59
    20 schema:datePublished 2016-03-18
    21 schema:datePublishedReg 2016-03-18
    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.
    23 schema:genre article
    24 schema:inLanguage en
    25 schema:isAccessibleForFree true
    26 schema:isPartOf N5750f039247d4e35ba3d3e7d8e90364c
    27 Nf471e7703d6e4b31b8623528b69da091
    28 sg:journal.1036887
    29 schema:keywords Arabidopsis thaliana
    30 GC-MS data sets
    31 LC-MS
    32 account
    33 advantages
    34 approach
    35 batch
    36 batch correction
    37 batch correction methods
    38 batch effects
    39 behavior
    40 better correction
    41 better results
    42 certainty
    43 characteristics
    44 control samples
    45 correction
    46 correction method
    47 correction strategy
    48 criteria
    49 data sets
    50 detection techniques
    51 different characteristics
    52 different strategies
    53 distinct behaviors
    54 effect
    55 experiments
    56 explicit inclusion
    57 general lead
    58 high-quality correction
    59 inclusion
    60 information
    61 injection order
    62 intensity
    63 interval
    64 labels
    65 lead
    66 mass spectrometry
    67 merits
    68 metabolites
    69 metabolomics
    70 metabolomics experiments
    71 metabolomics studies
    72 method
    73 model
    74 more metabolites
    75 normalization approach
    76 normalization method
    77 number
    78 order
    79 paper
    80 peak intensity
    81 quality control samples
    82 quality criteria
    83 regression models
    84 regular intervals
    85 results
    86 samples
    87 sensitive detection techniques
    88 set
    89 signals
    90 small number
    91 small values
    92 spectrometry
    93 strategies
    94 study
    95 study sample
    96 technique
    97 thaliana
    98 trends
    99 untargeted mass spectrometry
    100 untargeted metabolomics experiments
    101 use
    102 values
    103 schema:name Improved batch correction in untargeted MS-based metabolomics
    104 schema:pagination 88
    105 schema:productId N204cc53cf83941c7ab317211036c4626
    106 N42b1327a36464d598f83a202ccc18c58
    107 Nfeb1ce18bf1b40c49282b435cff4557c
    108 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010819562
    109 https://doi.org/10.1007/s11306-016-1015-8
    110 schema:sdDatePublished 2022-05-10T10:13
    111 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    112 schema:sdPublisher N77e305c4798e4ceb8d1cf6a3845fcc79
    113 schema:url https://doi.org/10.1007/s11306-016-1015-8
    114 sgo:license sg:explorer/license/
    115 sgo:sdDataset articles
    116 rdf:type schema:ScholarlyArticle
    117 N0d2b5929a6e049b0a29b5a25aa3942a7 rdf:first sg:person.0607514543.46
    118 rdf:rest Nfc05bdea44184c05b6b4613cda0bf003
    119 N204cc53cf83941c7ab317211036c4626 schema:name pubmed_id
    120 schema:value 27073351
    121 rdf:type schema:PropertyValue
    122 N2830e66678bc420292a141c976ecd533 rdf:first sg:person.0704576323.20
    123 rdf:rest Ne85a2aff327f4fe0bab8510bee0b23a1
    124 N4011e0449810491f9115b689ebdca421 rdf:first sg:person.0770101362.75
    125 rdf:rest Na7db90823ae14ee38f65db55d76e2eef
    126 N42b1327a36464d598f83a202ccc18c58 schema:name dimensions_id
    127 schema:value pub.1010819562
    128 rdf:type schema:PropertyValue
    129 N5750f039247d4e35ba3d3e7d8e90364c schema:volumeNumber 12
    130 rdf:type schema:PublicationVolume
    131 N77e305c4798e4ceb8d1cf6a3845fcc79 schema:name Springer Nature - SN SciGraph project
    132 rdf:type schema:Organization
    133 N84b6d382d00242118a4e6721addffe3a rdf:first sg:person.0605446667.25
    134 rdf:rest N2830e66678bc420292a141c976ecd533
    135 Na7533e92a02f492f972277a2d4edd31b rdf:first sg:person.01124432620.04
    136 rdf:rest Nf4f008a1dc8f4846b269f5d6bc4c51b1
    137 Na7db90823ae14ee38f65db55d76e2eef rdf:first sg:person.0650756633.82
    138 rdf:rest N0d2b5929a6e049b0a29b5a25aa3942a7
    139 Nacc1a31efafe44aba0d174b49caf8c4b rdf:first sg:person.0713030357.17
    140 rdf:rest N84b6d382d00242118a4e6721addffe3a
    141 Nb5ebf286c4e044c5bab9dfd0891d5c7e rdf:first sg:person.0707004771.27
    142 rdf:rest N4011e0449810491f9115b689ebdca421
    143 Nb9b9a7248b2c41f9aefa3cd69e4296b5 rdf:first sg:person.014575024362.22
    144 rdf:rest rdf:nil
    145 Ne85a2aff327f4fe0bab8510bee0b23a1 rdf:first sg:person.01366416351.05
    146 rdf:rest Nb9b9a7248b2c41f9aefa3cd69e4296b5
    147 Nf471e7703d6e4b31b8623528b69da091 schema:issueNumber 5
    148 rdf:type schema:PublicationIssue
    149 Nf4f008a1dc8f4846b269f5d6bc4c51b1 rdf:first sg:person.010104047604.94
    150 rdf:rest Nacc1a31efafe44aba0d174b49caf8c4b
    151 Nfc05bdea44184c05b6b4613cda0bf003 rdf:first sg:person.01314356234.66
    152 rdf:rest Na7533e92a02f492f972277a2d4edd31b
    153 Nfeb1ce18bf1b40c49282b435cff4557c schema:name doi
    154 schema:value 10.1007/s11306-016-1015-8
    155 rdf:type schema:PropertyValue
    156 anzsrc-for:03 schema:inDefinedTermSet anzsrc-for:
    157 schema:name Chemical Sciences
    158 rdf:type schema:DefinedTerm
    159 anzsrc-for:0301 schema:inDefinedTermSet anzsrc-for:
    160 schema:name Analytical Chemistry
    161 rdf:type schema:DefinedTerm
    162 sg:journal.1036887 schema:issn 1573-3882
    163 1573-3890
    164 schema:name Metabolomics
    165 schema:publisher Springer Nature
    166 rdf:type schema:Periodical
    167 sg:person.010104047604.94 schema:affiliation grid-institutes:grid.4818.5
    168 schema:familyName Keurentjes
    169 schema:givenName Joost J. B.
    170 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010104047604.94
    171 rdf:type schema:Person
    172 sg:person.01124432620.04 schema:affiliation grid-institutes:grid.9026.d
    173 schema:familyName Wijnker
    174 schema:givenName Erik
    175 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01124432620.04
    176 rdf:type schema:Person
    177 sg:person.01314356234.66 schema:affiliation grid-institutes:grid.419498.9
    178 schema:familyName Flood
    179 schema:givenName Pádraic J.
    180 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01314356234.66
    181 rdf:type schema:Person
    182 sg:person.01366416351.05 schema:affiliation grid-institutes:grid.4818.5
    183 schema:familyName Mumm
    184 schema:givenName Roland
    185 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01366416351.05
    186 rdf:type schema:Person
    187 sg:person.014575024362.22 schema:affiliation grid-institutes:grid.4818.5
    188 schema:familyName de Vos
    189 schema:givenName Ric C. H.
    190 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014575024362.22
    191 rdf:type schema:Person
    192 sg:person.0605446667.25 schema:affiliation grid-institutes:grid.4818.5
    193 schema:familyName van Eekelen
    194 schema:givenName Henriëtte D. L. M.
    195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0605446667.25
    196 rdf:type schema:Person
    197 sg:person.0607514543.46 schema:affiliation grid-institutes:grid.4818.5
    198 schema:familyName Kooke
    199 schema:givenName Rik
    200 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0607514543.46
    201 rdf:type schema:Person
    202 sg:person.0650756633.82 schema:affiliation grid-institutes:grid.4818.5
    203 schema:familyName van Eeuwijk
    204 schema:givenName Fred
    205 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0650756633.82
    206 rdf:type schema:Person
    207 sg:person.0704576323.20 schema:affiliation grid-institutes:grid.4818.5
    208 schema:familyName Hall
    209 schema:givenName Robert D.
    210 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0704576323.20
    211 rdf:type schema:Person
    212 sg:person.0707004771.27 schema:affiliation grid-institutes:grid.4818.5
    213 schema:familyName Wehrens
    214 schema:givenName Ron
    215 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0707004771.27
    216 rdf:type schema:Person
    217 sg:person.0713030357.17 schema:affiliation grid-institutes:grid.4818.5
    218 schema:familyName Lommen
    219 schema:givenName Arjen
    220 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0713030357.17
    221 rdf:type schema:Person
    222 sg:person.0770101362.75 schema:affiliation grid-institutes:grid.4818.5
    223 schema:familyName Hageman
    224 schema:givenName Jos. A.
    225 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0770101362.75
    226 rdf:type schema:Person
    227 sg:pub.10.1007/978-0-387-77318-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018506708
    228 https://doi.org/10.1007/978-0-387-77318-6
    229 rdf:type schema:CreativeWork
    230 sg:pub.10.1007/978-1-4757-1904-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031639131
    231 https://doi.org/10.1007/978-1-4757-1904-8
    232 rdf:type schema:CreativeWork
    233 sg:pub.10.1007/978-1-61779-594-7_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052181974
    234 https://doi.org/10.1007/978-1-61779-594-7_6
    235 rdf:type schema:CreativeWork
    236 sg:pub.10.1007/978-3-642-17841-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026545920
    237 https://doi.org/10.1007/978-3-642-17841-2
    238 rdf:type schema:CreativeWork
    239 sg:pub.10.1007/s00216-013-6856-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035732243
    240 https://doi.org/10.1007/s00216-013-6856-7
    241 rdf:type schema:CreativeWork
    242 sg:pub.10.1007/s11306-011-0368-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042363301
    243 https://doi.org/10.1007/s11306-011-0368-2
    244 rdf:type schema:CreativeWork
    245 sg:pub.10.1007/s11306-012-0434-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002880572
    246 https://doi.org/10.1007/s11306-012-0434-4
    247 rdf:type schema:CreativeWork
    248 sg:pub.10.1007/s11306-014-0625-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003227544
    249 https://doi.org/10.1007/s11306-014-0625-2
    250 rdf:type schema:CreativeWork
    251 sg:pub.10.1007/s11306-014-0742-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1041306727
    252 https://doi.org/10.1007/s11306-014-0742-y
    253 rdf:type schema:CreativeWork
    254 sg:pub.10.1007/s11306-015-0925-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016892332
    255 https://doi.org/10.1007/s11306-015-0925-1
    256 rdf:type schema:CreativeWork
    257 sg:pub.10.1038/nbt.2931 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008162673
    258 https://doi.org/10.1038/nbt.2931
    259 rdf:type schema:CreativeWork
    260 sg:pub.10.1038/ng.1042 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044562954
    261 https://doi.org/10.1038/ng.1042
    262 rdf:type schema:CreativeWork
    263 sg:pub.10.1038/nprot.2007.95 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029308877
    264 https://doi.org/10.1038/nprot.2007.95
    265 rdf:type schema:CreativeWork
    266 sg:pub.10.1038/nprot.2011.335 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015396743
    267 https://doi.org/10.1038/nprot.2011.335
    268 rdf:type schema:CreativeWork
    269 sg:pub.10.1038/nrm3314 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023146162
    270 https://doi.org/10.1038/nrm3314
    271 rdf:type schema:CreativeWork
    272 sg:pub.10.1186/1471-2172-9-59 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007480674
    273 https://doi.org/10.1186/1471-2172-9-59
    274 rdf:type schema:CreativeWork
    275 grid-institutes:grid.419498.9 schema:alternateName Max Planck Institute For Plant Breeding Research, Cologne, Germany
    276 schema:name Horticulture and Production Physiology, Wageningen UR, Wageningen, The Netherlands
    277 Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands
    278 Max Planck Institute For Plant Breeding Research, Cologne, Germany
    279 rdf:type schema:Organization
    280 grid-institutes:grid.4818.5 schema:alternateName Biometris, Wageningen UR, Wageningen, The Netherlands
    281 Bioscience, Wageningen UR, Wageningen, The Netherlands
    282 Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands
    283 Laboratory of Plant Physiology, Wageningen UR, Wageningen, The Netherlands
    284 RIKILT, Wageningen UR, Wageningen, The Netherlands
    285 schema:name Biometris, Wageningen UR, Wageningen, The Netherlands
    286 Bioscience, Wageningen UR, Wageningen, The Netherlands
    287 Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands
    288 Laboratory of Plant Physiology, Wageningen UR, Wageningen, The Netherlands
    289 RIKILT, Wageningen UR, Wageningen, The Netherlands
    290 rdf:type schema:Organization
    291 grid-institutes:grid.9026.d schema:alternateName Developmental Biology, Hamburg University, Hamburg, Germany
    292 schema:name Developmental Biology, Hamburg University, Hamburg, Germany
    293 Laboratory of Genetics, Wageningen UR, Wageningen, The Netherlands
    294 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...