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dc.contributor.authorKoekemoer, Gerhard
dc.contributor.authorDercksen, Marli
dc.contributor.authorAllison, James
dc.contributor.authorSantana, Leonard
dc.contributor.authorReinecke, Carolus J.
dc.date.accessioned2012-11-09T04:28:55Z
dc.date.available2012-11-09T04:28:55Z
dc.date.issued2012
dc.identifier.citationKoekemoer, G. et al. 2012. Concurrent class analysis identifies discriminatory variables from metabolomics data on isovaleric acidemia. Metabolomics, 8(Suppl 1):S17-S28. [https://doi.org/10.1007/s11306-011-0327-y]en_US
dc.identifier.issn1573-3882
dc.identifier.issn1573-3890 (Online)
dc.identifier.urihttp://hdl.handle.net/10394/7743
dc.identifier.urihttps://doi.org/10.1007/s11306-011-0327-y
dc.identifier.urihttps://link.springer.com/article/10.1007%2Fs11306-011-0327-y
dc.descriptionFrom the issue entitled "Special Issue: Data Analysis Methods for Metabolomics"
dc.description.abstractMetabolomics data are typically complex and high dimensional. Multivariate dimension-reducing techniques have thus been developed for analysing metabolomics data to disclose underlying relationships, with principal component analysis (PCA) as the technique mostly applied. Despite its widespread use in metabolomics, PCA has shortcomings that limit its applicability. Several approaches have been made to overcome these limitations and we describe an advanced disjoint PCA (DPCA) model, termed concurrent class analysis and abbreviated as CONCA. CONCA is a new model, and is unique in linking DPCA models to a traditional PCA model. This is accomplished by restructuring the input data matrix, applying DPCA group models to the restructured data, and combining the DPCA models in order to replicate a traditional PCA. We applied the CONCA model to a metabolomics data set on isovaleric acidaemia (IVA), a rare inherited metabolic disorder. The outcome showed that three of the variables with high discrimination value identified through the CONCA analysis are prominent organic acid biomarkers for IVA. Moreover, three further minor metabolites associated with the disease, and two as a consequence of treatment, were likewise identified as important discriminatory variables. The benefit of the CONCA model thus is its ability to disclose information concerning each individual group and to identify the variables important in discrimination (VIDs) which are also responsible for group separation.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectMetabolomicsen_US
dc.subjectbiomarker identificationen_US
dc.subjectvariables important in discriminationen_US
dc.subjectconcurrent class analysisen_US
dc.subjectisovaleric acidemiaen_US
dc.titleConcurrent class analysis identifies discriminatory variables from metabolomics data on isovaleric acidemiaen_US
dc.typeArticleen_US
dc.contributor.researchID11803371 - Santana, Leonard
dc.contributor.researchID11985682 - Allison, James Samuel
dc.contributor.researchID11998938 - Dercksen, Marli
dc.contributor.researchID10055037 - Reinecke, Carolus Johannes
dc.contributor.researchID10096353 - Koekemoer, Gerhard


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