Characterization of data analysis methods for information recovery from metabolic 1H NMR spectra using artificial complex mixtures
Characterization of data analysis methods for information recovery from metabolic 1H NMR spectra using artificial complex mixtures
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DOI：
10.1007/s11306-012-0422-8
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发表时间：
2012-12-01
期刊：
影响因子：
3.6
通讯作者：
Ebbels, Timothy M. D.
中科院分区：
文献类型：
--
作者：
Alves, Alexessander C.;Li, Jia V.;Ebbels, Timothy M. D.
由于缺乏对确切样品组成的了解，基于H-1 NMR的代谢谱分析中的数据分析方法的评估受到阻碍。在本研究中，使用21种代谢物实施了一种人工复杂混合物设计，该设计包括两个人工定义的组，分别指定为正常组和疾病组，每组包含30个样本，这些代谢物的浓度通常在人尿液中发现，并且具有代谢物间相关性的真实分布。这些人工混合物通过H-1 NMR光谱进行分析，并用于评估区分两种条件的任务中的数据分析方法。当代谢物被单独量化时，火山图提供了一种很好的方法来跟踪条件之间变化的影响大小和意义。有趣的是，Welch t检验检测到一组类似的代谢物在定量和光谱数据中的类别之间变化，这表明使用错误发现率校正的H-1 NMR光谱的差分分析，考虑到倍数变化，是一种可靠的方法来检测复杂混合物研究中的差分代谢物。采用基于偏最小二乘（PLS）的多元回归方法进行判别分析。在定量和光谱数据中，最可靠的方法分别是PLS和RPLS线性和logistic回归。一个刀切为基础的变量选择策略进行了评估的量化和光谱数据和结果表明，它可能是可以提高传统的非线性偏最小二乘方法的准确性和灵敏度。我们的方法的一个关键改进包括客观标准，以选择与一种条件相关的显著信号，该条件提供了对所做发现的置信水平，这可以在代谢分析研究中实施。
The assessment of data analysis methods in H-1 NMR based metabolic profiling is hampered owing to a lack of knowledge of the exact sample composition. In this study, an artificial complex mixture design comprising two artificially defined groups designated normal and disease, each containing 30 samples, was implemented using 21 metabolites at concentrations typically found in human urine and having a realistic distribution of inter-metabolite correlations. These artificial mixtures were profiled by H-1 NMR spectroscopy and used to assess data analytical methods in the task of differentiating the two conditions. When metabolites were individually quantified, volcano plots provided an excellent method to track the effect size and significance of the change between conditions. Interestingly, the Welch t test detected a similar set of metabolites changing between classes in both quantified and spectral data, suggesting that differential analysis of H-1 NMR spectra using a false discovery rate correction, taking into account fold changes, is a reliable approach to detect differential metabolites in complex mixture studies. Various multivariate regression methods based on partial least squares (PLS) were applied in discriminant analysis mode. The most reliable methods in quantified and spectral H-1 NMR data were PLS and RPLS linear and logistic regression respectively. A jackknife based strategy for variable selection was assessed on both quantified and spectral data and results indicate that it may be possible to improve on the conventional Orthogonal-PLS methodology in terms of accuracy and sensitivity. A key improvement of our approach consists of objective criteria to select significant signals associated with a condition that provides a confidence level on the discoveries made, which can be implemented in metabolic profiling studies.