Show simple item record

dc.contributor.author David P. en_US
dc.contributor.author Wanchang en_US
dc.contributor.author Manfred en_US
dc.contributor.author David en_US
dc.contributor.author David P. en_US
dc.contributor.author John en_US
dc.date.accessioned 2009-05-21T08:56:20Z
dc.date.available 2009-05-21T08:56:20Z
dc.date.issued 2008-02-23 en_US
dc.identifier http://dx.doi.org/10.1038/nprot.2007.511 en_US
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_US
dc.identifier.other PURE: 101361 en_US
dc.identifier.other dspace: 2160/2272 en_US
dc.identifier.uri http://hdl.handle.net/2160/2272
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_US
dc.format.extent 25 en_US
dc.relation.ispartof Nature Protocols en_US
dc.title Preprocessing, classification modeling and feature selection using flow injection electrospray mass spectrometry metabolite fingerprint data en_US
dc.contributor.pbl Institute of Biological, Environmental and Rural Sciences en_US


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search Cadair


Advanced Search

Browse

Statistics