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

BMC Bioinformatics

Research Article

Eleanor Stanley¹  William Spooner¹  Joost P. Schanstra²  Thomas Koeck³  Esther Nkuipou-Kenfack³  Harald Mischak⁴  Eleni Ioanna Delatola⁵  Walter Kolch⁶ 

[1] Eagle Genomics Ltd, The Biodata Innovation Centre, Wellcome Genome Campus, Hinxton, CB10 1DR, Cambridge, UK;Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institute of Cardiovascular and Metabolic Disease, Toulouse, France;Université Toulouse III Paul-Sabatier, Toulouse, France;Mosaiques Diagnostics GmbH, Hanover, Germany;Mosaiques Diagnostics GmbH, Hanover, Germany;Institute of Cardiovascular and Medical Sciences, University of Glasgow, G12 8TA, Glasgow, UK;Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland;Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland;Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland;School of Medicine and Medical Science, University College Dublin, Belfield, Dublin, Ireland;

关键词: Statistical proteome analysis;    Biomarker detection;    Classifier modelling;   

DOI  :  10.1186/s12859-016-1390-1

 received in 2016-09-02, accepted in 2016-11-29,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundWhen 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.ResultsComputing the discovery sub-cohorts comprising 23\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$ {\scriptscriptstyle \raisebox{1ex}{$2$}\!\left/ \!\raisebox{-1ex}{$3$}\right.} $$\end{document} 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.ConclusionIn 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.

【 授权许可】

CC BY   
© The Author(s). 2016

【 图 表 】

Fig. 7