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

Stanley, E., Delatola, E. I., Nkuipou-Kenfack, E., Spooner, W., Kolch, W., Schanstra, J. P., Mischak, H. and Koeck, T. (2016) Comparison of different statistical approaches for urinary peptide biomarker detection in the context of coronary artery disease. BMC Bioinformatics, 17, 496. (doi: 10.1186/s12859-016-1390-1) (PMID:27923348) (PMCID:PMC5139137)

[img]
Preview
Text
123791.pdf - Published Version
Available under License Creative Commons Attribution.

1MB

Abstract

Background: 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 2/32/3 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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Mischak, Professor Harald
Authors: Stanley, E., Delatola, E. I., Nkuipou-Kenfack, E., Spooner, W., Kolch, W., Schanstra, J. P., Mischak, H., and Koeck, T.
College/School:College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
Journal Name:BMC Bioinformatics
Publisher:Biomed Central
ISSN:1471-2105
ISSN (Online):1471-2105
Copyright Holders:Copyright © 2016 The Authors
First Published:First published in BMC Bioinformatics 17: 496
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
593511SysVascChristian DellesEuropean Commission (EC)603288RI CARDIOVASCULAR & MEDICAL SCIENCES