A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data.
A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data.
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一种新型的贝叶斯方法来量化临床变量并确定1H NMR替代数据中的光谱对应物。
DOI：
10.1186/1471-2105-8-s2-s8
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发表时间：
2007-05-03
影响因子：
3
通讯作者：
Ala-Korpela, Mika
中科院分区：
文献类型：
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作者：
Vehtari, Aki;Makinen, Ville-Petteri;Soininen, Pasi;Ingman, Petri;Makela, Sanna M;Savolainen, Markku J;Hannuksela, Minna L;Kaski, Kimmo;Ala-Korpela, Mika
代谢组学的一个关键挑战是揭示多维光谱数据与用于疾病风险评估和诊断的生化指标之间的定量联系。在这里，我们将重点放在通过血清1H核磁共振波谱对脂蛋白脂类进行临床相关性评估。贝叶斯方法，与生化动机，提出了一个真实的核磁共振代谢组学数据集的75个血清样本。通过超速离心法和特定的生化分析独立获得这些样本的脂蛋白脂质浓度。用马尔可夫链蒙特卡罗方法构建的贝叶斯模型具有很好的定量性能，对极低密度脂蛋白甘油三酯的预测R值为0.985，对中间脂蛋白胆固醇的预测R值为0.787，对低密度脂蛋白胆固醇的预测R值为0.943，对高密度脂蛋白胆固醇的预测R值为0.933。该模型产生了基于核的数据重新表述，其参数与1H核磁共振谱的众所周知的生化特征一致；特别是对于极低密度脂蛋白-甘油三酯和高密度脂蛋白-C，贝叶斯方法能够清楚地识别光谱中严重重叠的信息中最具特征的共振。对于IDL-C和LDL-C，所得到的模型核比VLDL-TG和HDLC的模型核更复杂，可能反映了1HNMR谱中IDL和LDL共振的严重重叠。系统地使用贝叶斯MCMC分析对计算要求很高。然而，高质量的量化和由此产生的模型的生化原理的结合预计将在代谢组学领域有用。
A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk assessment and diagnostics. Here we focus on clinically relevant estimation of lipoprotein lipids by 1H NMR spectroscopy of serum. A Bayesian methodology, with a biochemical motivation, is presented for a real 1H NMR metabonomics data set of 75 serum samples. Lipoprotein lipid concentrations were independently obtained for these samples via ultracentrifugation and specific biochemical assays. The Bayesian models were constructed by Markov chain Monte Carlo (MCMC) and they showed remarkably good quantitative performance, the predictive R-values being 0.985 for the very low density lipoprotein triglycerides (VLDL-TG), 0.787 for the intermediate, 0.943 for the low, and 0.933 for the high density lipoprotein cholesterol (IDL-C, LDL-C and HDL-C, respectively). The modelling produced a kernel-based reformulation of the data, the parameters of which coincided with the well-known biochemical characteristics of the 1H NMR spectra; particularly for VLDL-TG and HDL-C the Bayesian methodology was able to clearly identify the most characteristic resonances within the heavily overlapping information in the spectra. For IDL-C and LDL-C the resulting model kernels were more complex than those for VLDL-TG and HDL-C, probably reflecting the severe overlap of the IDL and LDL resonances in the 1H NMR spectra. The systematic use of Bayesian MCMC analysis is computationally demanding. Nevertheless, the combination of high-quality quantification and the biochemical rationale of the resulting models is expected to be useful in the field of metabonomics.