Laboratory for Bioinformatics and Computational Genomics, Department of Molecular Biotechnology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
Anal Bioanal Chem. 2010 Oct;398(4):1781-90. doi: 10.1007/s00216-010-4085-x. Epub 2010 Aug 17.
Proton nuclear magnetic resonance ((1)H-NMR)-based metabolomics enables the high-resolution and high-throughput assessment of a broad spectrum of metabolites in biofluids. Despite the straightforward character of the experimental methodology, the analysis of spectral profiles is rather complex, particularly due to the requirement of numerous data preprocessing steps. Here, we evaluate how several of the most common preprocessing procedures affect the subsequent univariate analyses of blood serum spectra, with a particular focus on how the standard methods perform compared to more advanced examples. Carr-Purcell-Meiboom-Gill 1D (1)H spectra were obtained for 240 serum samples from healthy subjects of the Asklepios study. We studied the impact of different preprocessing steps--integral (standard method) and probabilistic quotient normalization; no, equidistant (standard), and adaptive-intelligent binning; mean (standard) and maximum bin intensity data summation--on the resonance intensities of three different types of metabolites: triglycerides, glucose, and creatinine. The effects were evaluated by correlating the differently preprocessed NMR data with the independently measured metabolite concentrations. The analyses revealed that the standard methods performed inferiorly and that a combination of probabilistic quotient normalization after adaptive-intelligent binning and maximum intensity variable definition yielded the best overall results (triglycerides, R = 0.98; glucose, R = 0.76; creatinine, R = 0.70). Therefore, at least in the case of serum metabolomics, these or equivalent methods should be preferred above the standard preprocessing methods, particularly for univariate analyses. Additional optimization of the normalization procedure might further improve the analyses.
基于质子核磁共振（1H-NMR）的代谢组学能够高分辨率和高通量地评估生物流体中广泛的代谢物。尽管实验方法具有直接的特点，但光谱轮廓的分析相当复杂，特别是由于需要进行大量的数据预处理步骤。在这里，我们评估了几种最常见的预处理程序如何影响随后对血清光谱的单变量分析，特别关注标准方法与更先进的方法相比的表现。对 Asklepios 研究中 240 个健康受试者的血清样本进行了 1D（1）H 谱的 Carr-Purcell-Meiboom-Gill 采集。我们研究了不同预处理步骤——积分（标准方法）和概率商归一化；无、等距（标准）和自适应智能分箱；均值（标准）和最大分箱强度数据求和——对三种不同类型代谢物的共振强度的影响：甘油三酯、葡萄糖和肌酸酐。通过将不同预处理的 NMR 数据与独立测量的代谢物浓度进行相关来评估这些影响。分析表明，标准方法的表现较差，而自适应智能分箱后进行概率商归一化和最大强度变量定义的组合产生了最佳的整体结果（甘油三酯，R = 0.98；葡萄糖，R = 0.76；肌酸酐，R = 0.70）。因此，至少在血清代谢组学的情况下，这些或等效的方法应优先于标准预处理方法，特别是对于单变量分析。归一化程序的进一步优化可能会进一步改善分析。
