Comparison of different statistical approaches for urinary peptide biomarker detection in the context of coronary artery disease
|Title:||Comparison of different statistical approaches for urinary peptide biomarker detection in the context of coronary artery disease||Authors:||Stanley, Eleanor
Delatola, Eleni Ioanna
|Permanent link:||http://hdl.handle.net/10197/9753||Date:||6-Dec-2016||Online since:||2019-04-01T09:18:13Z||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=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.||Funding Details:||European Commission - Seventh Framework Programme (FP7)||Type of material:||Journal Article||Publisher:||BioMed Central||Journal:||BMC Bioinformatics||Volume:||17||Issue:||1||Start page:||496||End page:||506||Copyright (published version):||2016 the Authors||Keywords:||Statistical proteome analysis; Biomarker detection; Classifier modelling||DOI:||10.1186/s12859-016-1390-1||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||SBI Research Collection|
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.