Folch-Fortuny Abel, Marques Rodolfo, Isidro Inês A, Oliveira Rui, Ferrer Alberto
Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universitat Politècnica de València, 46022 València, Spain.
Mol Biosyst. 2016 Mar;12(3):737-46. doi: 10.1039/c5mb00828j. Epub 2016 Feb 1.
Principal component analysis (PCA) has been widely applied in fluxomics to compress data into a few latent structures in order to simplify the identification of metabolic patterns. These latent structures lack a direct biological interpretation due to the intrinsic constraints associated with a PCA model. Here we introduce a new method that significantly improves the interpretability of the principal components with a direct link to metabolic pathways. This method, called principal elementary mode analysis (PEMA), establishes a bridge between a PCA-like model, aimed at explaining the maximum variance in flux data, and the set of elementary modes (EMs) of a metabolic network. It provides an easy way to identify metabolic patterns in large fluxomics datasets in terms of the simplest pathways of the organism metabolism. The results using a real metabolic model of Escherichia coli show the ability of PEMA to identify the EMs that generated the different simulated flux distributions. Actual flux data of E. coli and Pichia pastoris cultures confirm the results observed in the simulated study, providing a biologically meaningful model to explain flux data of both organisms in terms of the EM activation. The PEMA toolbox is freely available for non-commercial purposes on http://mseg.webs.upv.es.
主成分分析（PCA）已在通量组学中广泛应用，用于将数据压缩为几个潜在结构，以简化代谢模式的识别。由于与PCA模型相关的内在限制，这些潜在结构缺乏直接的生物学解释。在此，我们介绍一种新方法，该方法通过与代谢途径的直接联系显著提高了主成分的可解释性。这种方法称为主基本模式分析（PEMA），它在旨在解释通量数据中最大方差的类PCA模型与代谢网络的基本模式（EM）集之间架起了一座桥梁。它提供了一种简单的方法，可根据生物体代谢的最简单途径在大型通量组学数据集中识别代谢模式。使用大肠杆菌真实代谢模型的结果表明，PEMA能够识别产生不同模拟通量分布的EM。大肠杆菌和毕赤酵母培养物的实际通量数据证实了模拟研究中观察到的结果，提供了一个具有生物学意义的模型，可根据EM激活来解释两种生物体的通量数据。PEMA工具箱可在http://mseg.webs.upv.es上免费用于非商业目的。
