Coefficient of variation, signal-to-noise ratio, and effects of normalization in validation of biomarkers from NMR-based metabonomics studies
Coefficient of variation, signal-to-noise ratio, and effects of normalization in validation of biomarkers from NMR-based metabonomics studies
复制标题
DOI：
10.1016/j.chemolab.2013.07.007
复制
发表时间：
2013-10-15
影响因子：
3.9
通讯作者：
Kennedy, Michael A.
中科院分区：
文献类型：
--
作者：
Wang, Bo;Goodpaster, Aaron M.;Kennedy, Michael A.
代谢组学研究的一个主要目标是基于健康和疾病患者群体之间代谢谱的差异发现人类疾病的生物标记物。生物标记物发现中最重要的挑战之一是验证，这隐含地取决于与测量技术相关的变异系数(CV)。本文研究了核磁共振波谱测量代谢物的变异系数与信噪比和归一化方法的关系。对在八个月内收集的五个合成尿样的一系列核磁共振谱中的核磁共振共振峰进行了CVS计算。在所有归一化方法中，信噪比与变异系数之间均呈负相关。与SNR>；150最高的峰值相比，SNR<；15的小峰的CV值往往更大(15%-30%)，而SNR>；150的峰值的CV值通常更小(5%-10%)。CV和SNR之间的逆关系大致服从log(10)依赖关系。与无归一化、归一化到总强度(NTI)或归一化到内标(NIS)相比，商归一化(QN)倾向于为较小的峰产生较小的CV，但对于数据中最强的峰产生较大的CV。因此，商归一化似乎是验证低浓度代谢物的最佳方法。对于总信号强度变化非常小的样本，NTI或NIS似乎优于QN。虽然CV和log(10)(SNR)之间的反向关系并不严格适用于所有代谢物，但浓度较低的代谢物可能需要更严格的验证才能作为潜在的生物标志物，因为它们往往具有较差的重复性。(C)2013爱思唯尔B.V.保留所有权利。
A primary goal of metabonomics research is biomarker discovery for human diseases based on differences in metabolic profiles between healthy and diseased patient populations. One of the most significant challenges in biomarker discovery is validation, which implicitly depends on the coefficient of variation (CV) associated with the measurement technique. This paper investigates how the CV of metabolite resonances measured by nuclear magnetic resonance spectroscopy (NMR) depends on signal-to-noise ratio (SNR) and normalization method. CVs were calculated for NMR resonance peaks in a series of NMR spectra of five synthetic urine samples collected over an eight-month period. An inverse correlation was detected between SNR and CV for all normalization methods. Small peaks with SNR < 15 tended to have larger CVs (15-30%) compared to peaks with the highest SNR > 150, which typically had smaller CVs (5-10%). The inverse relationship between CV and SNR roughly obeyed a log(10) dependence. Quotient normalization (QN) tended to produce smaller CVs for smaller peaks, but larger CVs for the strongest peaks in the data, compared to no normalization, normalization to total intensity (NTI) or normalization to an internal standard (NIS). Consequently, quotient normalization appears optimal for validating low concentration metabolites. NTI or NIS appear superior to QN for samples that have very small variation in total signal intensity. While the inverse relationship between CV and log(10)(SNR) did not strictly hold for all metabolites, weaker concentration metabolites will likely require more rigorous validation as potential biomarkers since they tend to have poorer reproducibility. (C) 2013 Elsevier B.V. All rights reserved.