RAMClust: A Novel Feature Clustering Method Enables Spectral-Matching-Based Annotation for Metabolomics Data
RAMClust: A Novel Feature Clustering Method Enables Spectral-Matching-Based Annotation for Metabolomics Data
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DOI：
10.1021/ac501530d
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发表时间：
2014-07-15
影响因子：
7.4
通讯作者：
Prenni, J. E.
中科院分区：
文献类型：
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作者：
Broeckling, C. D.;Afsar, F. A.;Prenni, J. E.
代谢组数据经常使用色谱耦合质谱 (MS) 平台获取。对于此类数据集，数据分析的第一步依赖于特征检测，其中特征由质量和保留时间定义。虽然特征通常源自单一化合物，但质量信号谱更能更准确地表示给定代谢物的质谱信号。在这里，我们报告了一种新颖的特征分组方法，该方法以无监督的方式运行，将来自 MS 数据的信号分组到光谱中，而不依赖于源内现象的可预测性。我们还通过隐式合并不加区分的 MS/MS (idMS/MS) 数据解决了代谢组学中的基本瓶颈，即 MS 水平信号注释：对 MS 和 idMS/MS 数据进行特征检测，并根据 MS 和 idMS/MS 数据同时确定特征特征关系。这种方法有利于使用来自单个实验的源内 MS 和/或 idMS/MS 谱来识别代谢物，与单一特征测量相比减少定量分析变化，并减少新化合物不可预测现象的假阳性注释。该工具作为免费提供的 R 软件包发布，称为 RAMClustR，并且具有足够的通用性，可以对任何色谱光谱平台或特征查找软件中的特征进行分组。
Metabolomic data are frequently acquired using chromatographically coupled mass spectrometry (MS) platforms. For such datasets, the first step in data analysis relies on feature detection, where a feature is defined by a mass and retention time. While a feature typically is derived from a single compound, a spectrum of mass signals is more a more-accurate representation of the mass spectrometric signal for a given metabolite. Here, we report a novel feature grouping method that operates in an unsupervised manner to group signals from MS data into spectra without relying on predictability of the in-source phenomenon. We additionally address a fundamental bottleneck in metabolomics, annotation of MS level signals, by incorporating indiscriminant MS/MS (idMS/MS) data implicitly: feature detection is performed on both MS and idMS/MS data, and feature feature relationships are determined simultaneously from the MS and idMS/MS data. This approach facilitates identification of metabolites using in-source MS and/or idMS/MS spectra from a single experiment, reduces quantitative analytical variation compared to single-feature measures, and decreases false positive annotations of unpredictable phenomenon as novel compounds. This tool is released as a freely available R package, called RAMClustR, and is sufficiently versatile to group features from any chromatographic-spectrometric platform or feature-finding software.