Comparison of different statistical approaches for urinary peptide biomarker detection in the context of coronary artery disease
 作者： Eleanor Stanley,  Eleni Ioanna Delatola,  Esther Nkuipou-Kenfack,  William Spooner,  Walter Kolch,  Joost P. Schanstra,  Harald Mischak,  Thomas Koeck 作者单位： 1Eagle Genomics Ltd, The Biodata Innovation Centre, Wellcome Genome Campus2University College Dublin3Mosaiques Diagnostics GmbH4Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institute of Cardiovascular and Metabolic Disease5Université Toulouse III Paul-Sabatier6University of Glasgow 刊名： BMC Bioinformatics, 2016, Vol.17 (1) 来源数据库： Springer Nature Journal DOI： 10.1186/s12859-016-1390-1 关键词： Statistical proteome analysis;  Biomarker detection;  Classifier modelling; 原始语种摘要： Abstract(#br) Background(#br)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.(#br) Results(#br)Computing the discovery sub-cohorts comprising $${\scriptscriptstyle \raisebox{1ex}{2}\!\left/ \!\raisebox{-1ex}{3}\right.}$$ 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.(#br) Conclusion(#br)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.

• coronary　冠状的
• urinary　尿的
• artery　动脉
• biomarker　生物标志化合物
• peptide
• detection　探测
• different　不相同的
• statistical　统计的
• context　菌髓
• disease