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dc.contributor.authorDe Livera, AM
dc.contributor.authorDias, DA
dc.contributor.authorDe Souza, D
dc.contributor.authorRupasinghe, T
dc.contributor.authorPyke, J
dc.contributor.authorTull, D
dc.contributor.authorRoessner, U
dc.contributor.authorMcConville, M
dc.contributor.authorSpeed, TP
dc.date.available2014-05-22T07:35:07Z
dc.date.issued2012-12-18
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000312429800040&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1c
dc.identifier.citationDe Livera, A. M., Dias, D. A., De Souza, D., Rupasinghe, T., Pyke, J., Tull, D., Roessner, U., McConville, M. & Speed, T. P. (2012). Normalizing and Integrating Metabolomics Data. ANALYTICAL CHEMISTRY, 84 (24), pp.10768-10776. https://doi.org/10.1021/ac302748b.
dc.identifier.issn0003-2700
dc.identifier.urihttp://hdl.handle.net/11343/32958
dc.descriptionC1 - Journal Articles Refereed
dc.description.abstractMetabolomics research often requires the use of multiple analytical platforms, batches of samples, and laboratories, any of which can introduce a component of unwanted variation. In addition, every experiment is subject to within-platform and other experimental variation, which often includes unwanted biological variation. Such variation must be removed in order to focus on the biological information of interest. We present a broadly applicable method for the removal of unwanted variation arising from various sources for the identification of differentially abundant metabolites and, hence, for the systematic integration of data on the same quantities from different sources. We illustrate the versatility and the performance of the approach in four applications, and we show that it has several advantages over the existing normalization methods.
dc.languageEnglish
dc.publisherAMER CHEMICAL SOC
dc.subjectStatistics not elsewhere classified; Biological Sciences not elsewhere classified; Expanding Knowledge in the Chemical Sciences; Expanding Knowledge in the Biological Sciences
dc.titleNormalizing and Integrating Metabolomics Data
dc.typeJournal Article
dc.identifier.doi10.1021/ac302748b
melbourne.peerreviewPeer Reviewed
melbourne.affiliationThe University of Melbourne
melbourne.affiliation.departmentBotany
melbourne.source.titleANALYTICAL CHEMISTRY
melbourne.source.volume84
melbourne.source.issue24
melbourne.source.pages10768-10776
melbourne.publicationid185705
melbourne.elementsid491862
melbourne.contributor.authorDias, Daniel
melbourne.contributor.authorMcConville, Malcolm
melbourne.contributor.authorRupasinghe, Thusitha
melbourne.contributor.authorTull, Dedreia
melbourne.contributor.authorRoessner, Ute
melbourne.contributor.authorde Livera, Alysha
melbourne.contributor.authorde Souza, David
melbourne.contributor.authorPyke, James
dc.identifier.eissn1520-6882
melbourne.accessrightsThis item is currently not available from this repository


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