Comparison of different statistical approaches for urinary peptide biomarker detection in the context of coronary artery disease

DC FieldValueLanguage
dc.contributor.authorStanley, Eleanor-
dc.contributor.authorDelatola, Eleni Ioanna-
dc.contributor.authorNkuipou-Kenfack, Esther-
dc.contributor.authorKolch, Walter-
dc.contributor.authoret al.-
dc.date.accessioned2019-04-01T09:18:13Z-
dc.date.available2019-04-01T09:18:13Z-
dc.date.copyright2016 the Authorsen_US
dc.date.issued2016-12-06-
dc.identifier.citationBMC Bioinformaticsen_US
dc.identifier.urihttp://hdl.handle.net/10197/9753-
dc.description.abstractBackground: When combined with a clinical outcome variable, the size, complexity and nature of mass-spectrometry proteomics data impose great statistical challenges in the discovery of potential disease-associated biomarkers. The purpose of this study was thus to evaluate the effectiveness of different statistical methods applied for urinary proteomic biomarker discovery and different methods of classifier modelling in respect of the diagnosis of coronary artery disease in 197 study subjects and the prognostication of acute coronary syndromes in 368 study subjects. Results: Computing the discovery sub-cohorts comprising 2=3 of the study subjects based on the Wilcoxon rank sum test, t-score, cat-score, binary discriminant analysis and random forests provided largely different numbers (ranging from 2 to 398) of potential peptide biomarkers. Moreover, these biomarker patterns showed very little overlap limited to fragments of type I and III collagens as the common denominator. However, these differences in biomarker patterns did mostly not translate into significant differently performing diagnostic or prognostic classifiers modelled by support vector machine, diagonal discriminant analysis, linear discriminant analysis, binary discriminant analysis and random forest. This was even true when different biomarker patterns were combined into master-patterns. Conclusion: In conclusion, our study revealed a very considerable dependence of peptide biomarker discovery on statistical computing of urinary peptide profiles while the observed diagnostic and/or prognostic reliability of classifiers was widely independent of the modelling approach. This may however be due to the limited statistical power in classifier testing. Nonetheless, our study showed that urinary proteome analysis has the potential to provide valuable biomarkers for coronary artery disease mirroring especially alterations in the extracellular matrix. It further showed that for a comprehensive discovery of biomarkers and thus of pathological information, the results of different statistical methods may best be combined into a master pattern that then can be used for classifier modelling.en_US
dc.description.sponsorshipEuropean Commission - Seventh Framework Programme (FP7)en_US
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.subjectStatistical proteome analysisen_US
dc.subjectBiomarker detectionen_US
dc.subjectClassifier modellingen_US
dc.titleComparison of different statistical approaches for urinary peptide biomarker detection in the context of coronary artery diseaseen_US
dc.typeJournal Articleen_US
dc.statusPeer revieweden_US
dc.identifier.volume17en_US
dc.identifier.issue1en_US
dc.identifier.startpage496en_US
dc.identifier.endpage506en_US
dc.identifier.doi10.1186/s12859-016-1390-1-
dc.neeo.contributorStanley|Eleanor|aut|-
dc.neeo.contributorDelatola|Eleni Ioanna|aut|-
dc.neeo.contributorNkuipou-Kenfack|Esther|aut|-
dc.neeo.contributorKolch|Walter|aut|-
dc.neeo.contributoret al.||aut|-
dc.date.updated2017-12-05-
dc.identifier.grantid603228-
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
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