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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Statistical methods for handling unwanted variation in metabolomics data</title><author><name sortKey="De Livera, Alysha M" sort="De Livera, Alysha M" uniqKey="De Livera A" first="Alysha M." last="De Livera">Alysha M. De Livera</name></author><author><name sortKey="Sysi Aho, Marko" sort="Sysi Aho, Marko" uniqKey="Sysi Aho M" first="Marko" last="Sysi-Aho">Marko Sysi-Aho</name></author><author><name sortKey="Jacob, Laurent" sort="Jacob, Laurent" uniqKey="Jacob L" first="Laurent" last="Jacob">Laurent Jacob</name></author><author><name sortKey="Gagnon Bartsch, Johann A" sort="Gagnon Bartsch, Johann A" uniqKey="Gagnon Bartsch J" first="Johann A." last="Gagnon-Bartsch">Johann A. Gagnon-Bartsch</name></author><author><name sortKey="Castillo, Sandra" sort="Castillo, Sandra" uniqKey="Castillo S" first="Sandra" last="Castillo">Sandra Castillo</name></author><author><name sortKey="Simpson, Julie A" sort="Simpson, Julie A" uniqKey="Simpson J" first="Julie A" last="Simpson">Julie A. Simpson</name></author><author><name sortKey="Speed, Terence P" sort="Speed, Terence P" uniqKey="Speed T" first="Terence P." last="Speed">Terence P. Speed</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">25692814</idno><idno type="pmc">4544854</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4544854</idno><idno type="RBID">PMC:4544854</idno><idno type="doi">10.1021/ac502439y</idno><date when="2015">2015</date><idno type="wicri:Area/Pmc/Corpus">001C92</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001C92</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Statistical methods for handling unwanted variation in metabolomics data</title><author><name sortKey="De Livera, Alysha M" sort="De Livera, Alysha M" uniqKey="De Livera A" first="Alysha M." last="De Livera">Alysha M. De Livera</name></author><author><name sortKey="Sysi Aho, Marko" sort="Sysi Aho, Marko" uniqKey="Sysi Aho M" first="Marko" last="Sysi-Aho">Marko Sysi-Aho</name></author><author><name sortKey="Jacob, Laurent" sort="Jacob, Laurent" uniqKey="Jacob L" first="Laurent" last="Jacob">Laurent Jacob</name></author><author><name sortKey="Gagnon Bartsch, Johann A" sort="Gagnon Bartsch, Johann A" uniqKey="Gagnon Bartsch J" first="Johann A." last="Gagnon-Bartsch">Johann A. Gagnon-Bartsch</name></author><author><name sortKey="Castillo, Sandra" sort="Castillo, Sandra" uniqKey="Castillo S" first="Sandra" last="Castillo">Sandra Castillo</name></author><author><name sortKey="Simpson, Julie A" sort="Simpson, Julie A" uniqKey="Simpson J" first="Julie A" last="Simpson">Julie A. Simpson</name></author><author><name sortKey="Speed, Terence P" sort="Speed, Terence P" uniqKey="Speed T" first="Terence P." last="Speed">Terence P. Speed</name></author></analytic><series><title level="j">Analytical chemistry</title><idno type="ISSN">0003-2700</idno><idno type="eISSN">1520-6882</idno><imprint><date when="2015">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">Metabolomics experiments are inevitably subject to a component of unwanted variation, due to factors such as batch effects, long runs of samples, and confounding biological variation. Although the removal of this unwanted variation is a vital step in the analysis of metabolomics data, it is considered a gray area in which there is a recognised need to develop a better understanding of the procedures and statistical methods required to achieve statistically relevant optimal biological outcomes. In this paper, we discuss the causes of unwanted variation in metabolomics experiments, review commonly used metabolomics approaches for handling this unwanted variation, and present a statistical approach for the removal of unwanted variation to obtain normalized metabolomics data. The advantages and performance of the approach relative to several widely-used metabolomics normalization approaches are illustrated through two metabolomics studies, and recommendations are provided for choosing and assessing the most suitable normalization method for a given metabolomics experiment. Software for the approach is made freely available online.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">0370536</journal-id><journal-id journal-id-type="pubmed-jr-id">519</journal-id><journal-id journal-id-type="nlm-ta">Anal Chem</journal-id><journal-id journal-id-type="iso-abbrev">Anal. Chem.</journal-id><journal-title-group><journal-title>Analytical chemistry</journal-title></journal-title-group><issn pub-type="ppub">0003-2700</issn><issn pub-type="epub">1520-6882</issn></journal-meta><article-meta><article-id pub-id-type="pmid">25692814</article-id><article-id pub-id-type="pmc">4544854</article-id><article-id pub-id-type="doi">10.1021/ac502439y</article-id><article-id pub-id-type="manuscript">NIHMS715285</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Statistical methods for handling unwanted variation in metabolomics data</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>De Livera</surname><given-names>Alysha M.</given-names></name><xref rid="FN1" ref-type="author-notes">†</xref><email>alyshad@unimelb.edu.au</email></contrib><contrib contrib-type="author"><name><surname>Sysi-Aho</surname><given-names>Marko</given-names></name><xref rid="FN2" ref-type="author-notes">‡</xref><xref rid="FN3" ref-type="author-notes">¶</xref></contrib><contrib contrib-type="author"><name><surname>Jacob</surname><given-names>Laurent</given-names></name><xref rid="FN4" ref-type="author-notes">§</xref></contrib><contrib contrib-type="author"><name><surname>Gagnon-Bartsch</surname><given-names>Johann A.</given-names></name><xref rid="FN5" ref-type="author-notes">||</xref></contrib><contrib contrib-type="author"><name><surname>Castillo</surname><given-names>Sandra</given-names></name><xref rid="FN3" ref-type="author-notes">¶</xref></contrib><contrib contrib-type="author"><name><surname>Simpson</surname><given-names>Julie A</given-names></name><xref rid="FN1" ref-type="author-notes">†</xref></contrib><contrib contrib-type="author"><name><surname>Speed</surname><given-names>Terence P.</given-names></name><xref rid="FN5" ref-type="author-notes">||</xref><xref rid="FN6" ref-type="author-notes">⊥</xref><xref rid="FN7" ref-type="author-notes">#</xref></contrib><aff id="A1">Biostatistics Unit, Centre for Epidemiology and Biostatistics, University of Melbourne, VIC 3800, Australia, Zora Biosciences Oy, FIN-02150 Espoo, Finland, VTT Technical Research Centre of Finland, Finland, Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, CNRS, INRA, UMR5558, Villeurbanne, France, Department of Statistics, University of California, Berkeley, USA, Bioinformatics Division, Walter and Eliza Hall Institute, and Department of Mathematics and Statistics, University of Melbourne, VIC 3800, Australia</aff></contrib-group><author-notes><fn id="FN1"><label>†</label><p>Biostatistics Unit, Centre for Epidemiology and Biostatistics, University of Melbourne, VIC 3800, Australia</p></fn><fn id="FN2"><label>‡</label><p>Zora Biosciences Oy, FIN-02150 Espoo, Finland</p></fn><fn id="FN3"><label>¶</label><p>VTT Technical Research Centre of Finland, Finland</p></fn><fn id="FN4"><label>§</label><p>Laboratoire de Biométrie et Biologie Evolutive, Université Lyon 1, CNRS, INRA, UMR5558, Villeurbanne, France</p></fn><fn id="FN5"><label>||</label><p>Department of Statistics, University of California, Berkeley, USA</p></fn><fn id="FN6"><label>⊥</label><p>Bioinformatics Division, Walter and Eliza Hall Institute</p></fn><fn id="FN7"><label>#</label><p>Department of Mathematics and Statistics, University of Melbourne, VIC 3800, Australia</p></fn></author-notes><pub-date pub-type="nihms-submitted"><day>15</day><month>8</month><year>2015</year></pub-date><pub-date pub-type="epub"><day>06</day><month>3</month><year>2015</year></pub-date><pub-date pub-type="ppub"><day>7</day><month>4</month><year>2015</year></pub-date><pub-date pub-type="pmc-release"><day>07</day><month>4</month><year>2016</year></pub-date><volume>87</volume><issue>7</issue><fpage>3606</fpage><lpage>3615</lpage><pmc-comment>elocation-id from pubmed: 10.1021/ac502439y</pmc-comment>
<abstract><p id="P1">Metabolomics experiments are inevitably subject to a component of unwanted variation, due to factors such as batch effects, long runs of samples, and confounding biological variation. Although the removal of this unwanted variation is a vital step in the analysis of metabolomics data, it is considered a gray area in which there is a recognised need to develop a better understanding of the procedures and statistical methods required to achieve statistically relevant optimal biological outcomes. In this paper, we discuss the causes of unwanted variation in metabolomics experiments, review commonly used metabolomics approaches for handling this unwanted variation, and present a statistical approach for the removal of unwanted variation to obtain normalized metabolomics data. The advantages and performance of the approach relative to several widely-used metabolomics normalization approaches are illustrated through two metabolomics studies, and recommendations are provided for choosing and assessing the most suitable normalization method for a given metabolomics experiment. Software for the approach is made freely available online.</p></abstract></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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