Phenotypic Mapping of Metabolic Profiles Using Self-Organizing Maps of High-Dimensional Mass Spectrometry Data
Phenotypic Mapping of Metabolic Profiles Using Self-Organizing Maps of High-Dimensional Mass Spectrometry Data
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DOI：
10.1021/ac5010794
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发表时间：
2014-07-01
影响因子：
7.4
通讯作者：
McLean, John A.
中科院分区：
文献类型：
--
作者：
Goodwin, Cody R.;Sherrod, Stacy D.;McLean, John A.
代谢系统由内在相互联系的代谢前体、中间体和产物组成。非靶向代谢组学数据的分析通常通过使用比较统计学或基于多变量统计分析的方法来进行;然而，每种福尔斯都不能代表代谢扰动的相关性质。在这里，我们描述了一个互补的方法，使用数据驱动的方法，基于自组织映射算法的大型代谢物库存的分析。该工作流程允许通过代谢热图的完形比较进行无监督聚类以及随后的相关特征的优先级排序。我们详细描述了这种方法，包括传统的代谢组学方法的比较，并证明了这种方法的应用，以分析长期暴露于可卡因在大鼠血清中的代谢影响。
A metabolic system is composed of inherently interconnected metabolic precursors, intermediates, and products. The analysis of untargeted metabolomics data has conventionally been performed through the use of comparative statistics or multivariate statistical analysis-based approaches; however, each falls short in representing the related nature of metabolic perturbations. Herein, we describe a complementary method for the analysis of large metabolite inventories using a data-driven approach based upon a self-organizing map algorithm. This workflow allows for the unsupervised clustering, and subsequent prioritization of, correlated features through Gestalt comparisons of metabolic heat maps. We describe this methodology in detail, including a comparison to conventional metabolomics approaches, and demonstrate the application of this method to the analysis of the metabolic repercussions of prolonged cocaine exposure in rat sera profiles.