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Tim De Meyer Davy Sinnaeve Bjorn Van Gasse Ernst-R Rietzschel Marc L. De Buyzere Michel R. Langlois Sofie Bekaert José C. Martins Wim Van Criekinge 《Analytical and bioanalytical chemistry》2010,398(4):1781-1790
Proton nuclear magnetic resonance (1H-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 1H 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. 相似文献
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