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dc.contributor.author Enot, David P.
dc.contributor.author Lin, Wanchang
dc.contributor.author Beckmann, Manfred
dc.contributor.author Parker, David
dc.contributor.author Overy, David P.
dc.contributor.author Draper, John
dc.date.accessioned 2009-05-21T08:56:20Z
dc.date.available 2009-05-21T08:56:20Z
dc.date.issued 2008-02-23
dc.identifier.citation Enot , D P , Lin , W , Beckmann , M , Parker , D , Overy , D P & Draper , J 2008 , ' Preprocessing, classification modeling and feature selection using flow injection electrospray mass spectrometry metabolite fingerprint data ' Nature Protocols , vol 3 , pp. 446-470 . , 10.1038/nprot.2007.511 en
dc.identifier.issn 1750-2799
dc.identifier.other PURE: 101361
dc.identifier.other dspace: 2160/2272
dc.identifier.uri http://hdl.handle.net/2160/2272
dc.description Enot, D. P., Lin, W., Beckmann, M., Parker, D., Overy, D. P., Draper, J. (2008). Preprocessing, classification modeling and feature selection using flow injection electrospray mass spectrometry metabolite fingerprint data.  Nature Protocols, 3, (3), 446-470. IMPF: 04.17 RONO: 00 en
dc.description.abstract Metabolome analysis by flow injection electrospray mass spectrometry (FIE-MS) fingerprinting generates measurements relating to large numbers of m/z signals. Such data sets often exhibit high variance with a paucity of replicates, thus providing a challenge for data mining. We describe data preprocessing and modeling methods that have proved reliable in projects involving samples from a range of organisms. The protocols interact with software resources specifically for metabolomics provided in a Web-accessible data analysis package FIEmspro (http://users.aber.ac.uk/jhd) written in the R environment and requiring a moderate knowledge of R command-line usage. Specific emphasis is placed on describing the outcome of modeling experiments using FIE-MS data that require further preprocessing to improve quality. The salient features of both poor and robust (i.e., highly generalizable) multivariate models are outlined together with advice on validating classifiers and avoiding false discovery when seeking explanatory variables. en
dc.format.extent 25 en
dc.language.iso eng
dc.relation.ispartof Nature Protocols en
dc.title Preprocessing, classification modeling and feature selection using flow injection electrospray mass spectrometry metabolite fingerprint data en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1038/nprot.2007.511
dc.contributor.institution Institute of Biological, Environmental and Rural Sciences en
dc.description.status Peer reviewed en


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