Statistical total correlation spectroscopy:: An exploratory approach for latent biomarker identification from metabolic 1H NMR data sets
Statistical total correlation spectroscopy:: An exploratory approach for latent biomarker identification from metabolic 1H NMR data sets
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DOI：
10.1021/ac048630x
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发表时间：
2005-03-01
影响因子：
7.4
通讯作者：
Nicholson, J
中科院分区：
文献类型：
--
作者：
Cloarec, O;Dumas, ME;Nicholson, J
我们在这里描述的统计总相关光谱（STOCSY）分析方法的实施，以帮助识别潜在的生物标志物分子的代谢组学研究的基础上NMR光谱数据。STOCSY利用一组光谱（在这种情况下为H-1 NMR光谱）中强度变量的多重共线性来生成伪二维NMR光谱，其显示整个样品中各个峰的强度之间的相关性。该方法不限于从更标准的二维NMR光谱方法（例如TOCSY）推导出的通常的连接性。此外，由于生物学协变性，同一途径中涉及的两个或更多个分子也可以呈现高分子间相关性，甚至可以是单分子相关的。这种结合的STOCSY与监督模式识别，特别是正交投影的潜在结构判别分析（O-PLS-DA）提供了一个新的强大的框架分析代谢组学数据。在第一步骤中，O-PLS-DA提取与辨别相关的NMR谱的部分。然后将这些信息与STOCSY结果交叉组合，以帮助识别负责代谢变化的分子。为了说明该方法的适用性，它已被应用到114 NMR光谱的尿液从代谢组学研究的基础上管理的碳水化合物饮食的胰岛素抵抗模型的三种不同的小鼠品系（C57 BL/60 xjr，BAIB/cOxjr，和129 S6/SvEvOxjr），其中一系列的代谢物的生物学重要性，可以最终分配和确定通过使用的STOCSY方法。
We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case H-1 NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 114 NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/60xjr, BAIB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.