NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm
NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm
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DOI：
10.1021/ac7025964
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发表时间：
2008-05-15
影响因子：
7.4
通讯作者：
Van Criekinge, Wim
中科院分区：
文献类型：
--
作者：
De Meyer, Tim;Sinnaeve, Davy;Van Criekinge, Wim
与所有组学技术一样，代谢组学需要新的数据处理方法。由于光谱尺寸较大，基于 NMR 的代谢组学中的标准方法意味着将光谱划分为大小相等的区域，从而简化了后续数据分析。然而，缺点是信息丢失和峰移引起的伪影的出现。这里提出了一种新的分箱算法，即自适应智能分箱（AI-Binning），它在很大程度上规避了这些问题。 AI-Binning 递归地识别现有箱中的箱边缘，仅需要最少的用户输入，并避免使用任意参数或参考光谱。 AI-Binning 的性能通过来自 Asklepios 研究的 40 名高血压受试者和 40 名匹配的正常血压受试者的血清光谱得到证明。高血压是一种主要的心血管危险因素，具有复杂的生物化学特征，并且在大多数情况下其来源未知。与标准分箱相比，分箱算法改进了高血压状态的分类，并有利于相关代谢物的识别。此外，由于很大程度上避免了噪声变量的出现，AI-Binned 光谱可以进行单位方差缩放。这使得能够检测相关的低强度代谢物。这些结果证明了 AI-Binning 的强大功能，并表明 α-1 酸性糖蛋白和胆碱生物化学与高血压有关。
As with every -omics technology, metabolomics requires new methodologies for data processing. Due to the large spectral size, a standard approach in NMR-based metabolomics implies the division of spectra into equally sized bins, thereby simplifying subsequent data analysis. Yet, disadvantages are the loss of information and the occurrence of artifacts caused by peak shifts. Here, a new binning algorithm, Adaptive Intelligent Binning (AI-Binning), which largely circumvents these problems, is presented. AI-Binning recursively identifies bin edges in existing bins, requires only minimal user input, and avoids the use of arbitrary parameters or reference spectra. The performance of AI-Binning is demonstrated using serum spectra from 40 hypertensive and 40 matched normotensive subjects from the Asklepios study. Hypertension is a major cardiovascular risk factor characterized by a complex biochemistry and, in most cases, an unknown origin. The binning algorithm resulted in an improved classification of hypertensive status compared with that of standard binning and facilitated the identification of relevant metabolites. Moreover, since the occurrence of noise variables is largely avoided, AI-Binned spectra can be unit-variance scaled. This enables the detection of relevant, low-intensity metabolites. These results demonstrate the power of AI-Binning and suggest the involvement of alpha-1 acid glycoproteins and choline biochemistry in hypertension.