Bijlsma Sabina, Bobeldijk Ivana, Verheij Elwin R, Ramaker Raymond, Kochhar Sunil, Macdonald Ian A, van Ommen Ben, Smilde Age K
Business Unit Analytical Sciences and Business Unit Physiological Sciences, TNO Quality of Life, P.O. Box 360, 3700 AJ Zeist, The Netherlands.
Anal Chem. 2006 Jan 15;78(2):567-74. doi: 10.1021/ac051495j.
A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up- or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies.
对肥胖和瘦受试者单次摄入高脂肪测试餐后四个时间点采集的600份血浆样本进行了一项大型代谢组学研究。所有样本均通过液相色谱-质谱联用（LC-MS）脂质组学方法进行代谢谱分析。为处理这个大型数据集，开发了一种结合多种成熟统计方法的实用方法，以便在结合较大生物变异的情况下检测代谢谱中的微小差异。此类代谢组学研究需要仔细的分析和统计方案。该策略包括数据预处理、数据分析和统计模型验证。经过几个数据预处理步骤后，使用偏最小二乘判别分析（PLS-DA）来寻找生物标志物。为了对发现的生物标志物进行统计学验证，通过置换检验、生物标志物模型和无信息模型对PLS-DA模型进行了验证。使用潜在生物标志物的单变量图来了解上调或下调情况。所提出的策略被证明适用于处理大规模人类代谢组学研究。
