Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data.
Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data.
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DOI：
10.1186/1752-0509-5-21
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发表时间：
2011-01-31
影响因子：
--
通讯作者：
Theis FJ
中科院分区：
文献类型：
--
作者：
Krumsiek J;Suhre K;Illig T;Adamski J;Theis FJ
随着高通量靶向代谢分析技术的出现，如何解释和分析由此产生的大量数据的问题变得越来越重要。在这项工作中，我们解决了重建的代谢反应的横截面代谢组学数据，即不需要时间分辨的测量或特定的系统扰动。以前的研究主要集中在皮尔逊相关系数，但通常无法区分直接和间接的代谢相互作用。在我们的新方法中，我们提出了一个高斯图形模型（GGM），一个无向概率图形模型估计变量之间的条件依赖的应用。GGM基于偏相关系数，即以与所有其他代谢物的相关性为条件的成对Pearson相关系数。我们首先证明了一般的有效性的方法和它的优势，经常相关网络与计算机模拟的反应系统。然后，我们估计GGM的数据，从一个大的人群队列，涵盖1020空腹血清样本与151个定量代谢物。GGM比相关网络稀疏得多，显示出相对于代谢物类的模块化结构，并且对数据集中的样本的选择是稳定的。在人类脂肪酸代谢的例子中，我们第一次证明了高偏相关系数通常对应于已知的代谢反应。通过研究特定的高分代谢物对手动评估该特征，然后系统地在脂肪酸合成和降解的文献策划模型上进行评估。我们的方法检测到许多已知的反应沿着与可能的新途径的相互作用，代表候选人进行进一步的实验检查。总之，我们在血清数据中展示了细胞内途径的强有力的特征，并为从大规模代谢组学数据集无偏重建代谢反应提供了一个有价值的工具。
With the advent of high-throughput targeted metabolic profiling techniques, the question of how to interpret and analyze the resulting vast amount of data becomes more and more important. In this work we address the reconstruction of metabolic reactions from cross-sectional metabolomics data, that is without the requirement for time-resolved measurements or specific system perturbations. Previous studies in this area mainly focused on Pearson correlation coefficients, which however are generally incapable of distinguishing between direct and indirect metabolic interactions. In our new approach we propose the application of a Gaussian graphical model (GGM), an undirected probabilistic graphical model estimating the conditional dependence between variables. GGMs are based on partial correlation coefficients, that is pairwise Pearson correlation coefficients conditioned against the correlation with all other metabolites. We first demonstrate the general validity of the method and its advantages over regular correlation networks with computer-simulated reaction systems. Then we estimate a GGM on data from a large human population cohort, covering 1020 fasting blood serum samples with 151 quantified metabolites. The GGM is much sparser than the correlation network, shows a modular structure with respect to metabolite classes, and is stable to the choice of samples in the data set. On the example of human fatty acid metabolism, we demonstrate for the first time that high partial correlation coefficients generally correspond to known metabolic reactions. This feature is evaluated both manually by investigating specific pairs of high-scoring metabolites, and then systematically on a literature-curated model of fatty acid synthesis and degradation. Our method detects many known reactions along with possibly novel pathway interactions, representing candidates for further experimental examination. In summary, we demonstrate strong signatures of intracellular pathways in blood serum data, and provide a valuable tool for the unbiased reconstruction of metabolic reactions from large-scale metabolomics data sets.
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