Analytic Properties of Statistical Total Correlation Spectroscopy Based Information Recovery in 1H NMR Metabolic Data Sets
Analytic Properties of Statistical Total Correlation Spectroscopy Based Information Recovery in 1H NMR Metabolic Data Sets
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DOI：
10.1021/ac801982h
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发表时间：
2009-03-15
影响因子：
7.4
通讯作者：
Ebbels, Timothy M. D.
中科院分区：
文献类型：
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作者：
Alves, Alexessander Couto;Rantalainen, Mattias;Ebbels, Timothy M. D.
共振峰的结构归属是核磁共振波谱学中的一个重要问题，而统计全相关谱（STOCSY）是一个有用的工具，可以帮助小分子在复杂混合物分析和代谢获益研究中完成这一过程。STOCSY提供分子内信息（描绘结构连接性），并在代谢研究中可以生成有关通路相关性的信息。为了进一步了解STOCSY的结构归属行为，我们分析了正常大鼠尿液样品1050 H-1 NMR谱的结构和非结构相关性的统计分布。我们发现，结构/非结构相关的分布是显着不同的（p < 10（-112））。从受试者工作特征曲线（ROC AUC）的曲线下面积，我们发现，结构相关性超过非结构相关性的概率AUC = 0.98。通过自举方法，我们证明了样本量的影响令人惊讶地小（例如，AUC = 0.97，样本量为50）。我们确定特定的签名在相关图中产生的小矩阵衍生的峰位置的变化，但发现它们对结构和非结构相关性的歧视的影响是可以忽略不计的大多数代谢物。需要r > 0.89的相关阈值以高概率（阳性预测值，PPV = 0.9）将两个峰分配给相同的代谢物，而对于r = 0.22，灵敏度和特异性等于93%。为了评估我们的研究结果的更广泛的适用性，我们分析了115模型毒素或生理应激处理的大鼠尿液的1H NMR光谱。在数据集中，我们发现获得PPV = 0.9所需的阈值没有显著差异，结构和非结构分布之间的重叠程度总是很小（中位AUC = 0.97）。STOCSY方法对于在不同生物条件和样本量下的结构表征是有效的，只要非结构关联产生的相关程度（例如，非平稳过程）是小的。本研究验证了使用的STOCSY方法在NAIR代谢分析研究中的信号的常规分配，并提供了实际的基准，研究人员可以解释的STOCSY分析的结果。
Structural assignment of resonances is an important problem in NMR spectroscopy, and statistical total correlation spectroscopy (STOCSY) is a useful tool aiding this process for small molecules in complex mixture analysis and metabolic profiting studies. STOCSY delivers intramolecular information (delineating structural connectivity) and in metabolism studies can generate information on pathway-related correlations. To understand further the behavior of STOCSY for structural assignment, we analyze the statistical distribution of structural and nonstructural correlations from 1050 H-1 NMR spectra of normal rat urine samples. We find that the distributions of structural/nonstructural correlations are significantly different (p < 10(-112)). From the area under the curve of the receiver operating characteristic (ROC AUC) we show that structural correlations exceed nonstructural correlations with probability AUC = 0.98. Through a bootstrap resampling approach, we demonstrate that sample size has a surprisingly small effect (e.g., AUC = 0.97 for a sample size of 50). We identify specific signatures in the correlation maps resulting from small matrix-derived variations in peak positions but find that their effect on discrimination of structural and nonstructural correlations is negligible for most metabolites. A correlation threshold of r > 0.89 is required to assign two peaks to the same metabolite with high probability (positive predictive value, PPV = 0.9), whereas sensitivity and specificity are equal at 93% for r = 0.22. To assess the wider applicability of our results, we analyze 1H NMR spectra of urine from rats treated with 115 model toxins or physiological stressors. Across the data sets, we find that the thresholds required to obtain PPV = 0.9 are not significantly different and the degree of overlap between the structural and nonstructural distributions is always small (median AUC = 0.97). The STOCSY method is effective for structural characterization under diverse biological conditions and sample sizes provided the degree of correlation resulting from nonstructural associations (e.g., from nonstationary processes) is small. This study validates the use of the STOCSY approach in the routine assignment of signals in NAIR metabolic profiling studies and provides practical benchmarks against which researchers can interpret the results of a STOCSY analysis.