Combining bootstrap and uninformative variable elimination: Chemometric identification of metabonomic biomarkers by nonparametric analysis of discriminant partial least squares

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• 作者：Xiao-Ming Sun^a ; Xiao-Ping Yu^c ; Yun Liu^a ; ^b ; Lu Xu^c ; Duo-Long Di^a ; ^{didl@licp.cas.cn}
• 关键词：Metabonomics ; Biomarkers ; Discriminant partial least squares ; Bootstrap ; Uninformative variable elimination
• 刊名：Chemometrics and Intelligent Laboratory Systems
• 出版年：2012
• 期刊代码：21_01697439
• 类别：ch
• 出版时间：15 June, 2012
• 卷：115
• 期：Complete
• 页码：37-43
• 文件大小：723 K

摘要

Interpretation and mining of complex metabonomic data depend heavily on proper use of chemometric methods. Due to the 鈥渟mall n鈥?paradigm and the absence of sufficient information concerning distribution of data, the classical parametric methods based on known theoretical distributions are sometimes unsuitable or unreliable to treat such data. Therefore, nonparametric methods requiring no or very limited assumptions provide useful alternative tools in many practical applications. In this paper, a new discriminant partial least squares combined with bootstrap and uninformative variable elimination (DPLS-BS-UVE) method is proposed for biomarker discovery in metabonomics. The method was tested on two real chromatographic data sets containing plasma metabolic profilings for S180 and H22 tumor-bearing mice. A robust version of c_j was used as the cutoff criterion. The results of biomarker discovery were compared with those obtained using variable importance in the projection (VIP) as well as BS. It is demonstrated that similar results are obtained using the three methods and DPLS-BS-UVE could provide easy interpretation of raw data. When the resampling unit increases to 500, the results were not significantly affected. In conclusion, DPLS-BS-UVE is a reliable alternative method for biomarker discovery, especially when the sample size is small.