Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations.
Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations.
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DOI：
10.1038/s41540-017-0029-9
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发表时间：
2017
影响因子：
4
通讯作者：
Krumsiek J
中科院分区：
文献类型：
--
作者：
Do KT;Pietzner M;Rasp DJ;Friedrich N;Nauck M;Kocher T;Suhre K;Mook-Kanamori DO;Kastenmüller G;Krumsiek J
在复杂的多流体代谢组学数据中识别表型驱动的网络模块对统计分析和结果解释提出了相当大的挑战。对于只有很少关联的表型（“稀疏”效应），尤其是对于具有大量代谢物关联的表型（“密集”效应），情况就是如此。在这里，我们假设，检查数据在不同层次的分辨率，从代谢物的途径，将有助于解释模块的稀疏和密集的情况下。我们提出了一种方法的表型驱动的识别模块上的多流体网络的基础上非目标的代谢组学数据的血浆，尿液和唾液样本的德国波美拉尼亚健康研究（SHIP-TREND）的研究。我们使用高斯图形模型生成了一个分层的多流体代谢图，涵盖了代谢物和途径相关性。首先，这张地图有助于我们对液体内和液体间代谢的基本理解，可以作为一个有价值的可下载资源。其次，基于这张地图，我们提出了一种算法来识别与性别和胰岛素样生长因子I（IGF-I）等因素相关的调节模块，分别作为具有密集和稀疏关联的性状的例子。我们发现IGF-I在相当细粒度的代谢物水平上相关，而性别在途径水平上显示出良好的可解释性关联。我们的研究结果证实，一个整体的和可解释的观点与表型相关的代谢变化，只能得到如果从多个体液代谢分辨率的不同层被认为是。代谢包括各种器官和体液之间的复杂相互作用，这对代谢数据的分析提出了重大挑战。为了解决这个问题，来自Helmholtz Zentrum München的Jan Krumsiek及其同事使用来自1000人的血浆，尿液和唾液的代谢组学测量数据来统计重建人类代谢相互作用的地图。基于这一地图，一种新的方法，确定高度相关的生化模块，与给定的表型，进行了测试的性别和胰岛素样生长因子I（IGF-I）。所确定的模块提供了对代谢组学和表型之间相互作用的深入了解，这些相互作用超出了代谢组学常用的统计方法所能发现的范围。该方法是通用的，可以很容易地应用到新的数据集的其他同事从外地。
The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We generated a hierarchical, multifluid map of metabolism covering both metabolite and pathway associations using Gaussian graphical models. First, this map facilitates a fundamental understanding of metabolism within and across fluids for our study, and can serve as a valuable and downloadable resource. Second, based on this map, we then present an algorithm to identify regulated modules that associate with factors such as gender and insulin-like growth factor I (IGF-I) as examples of traits with dense and sparse associations, respectively. We found IGF-I to associate at the rather fine-grained metabolite level, while gender shows well-interpretable associations at pathway level. Our results confirm that a holistic and interpretable view of metabolic changes associated with a phenotype can only be obtained if different layers of metabolic resolution from multiple body fluids are considered. Metabolism consists of complex interactions across various organs and body fluids, which poses a substantial challenge for the analysis of metabolic data. To address this problem, Jan Krumsiek from Helmholtz Zentrum München and colleagues used metabolomics measurements of plasma, urine, and saliva from 1000 people to statistically reconstruct a map of interactions in human metabolism. Based on this map, a novel approach that identifies highly correlated biochemical modules that are associated with a given phenotype, was tested for gender and insulin-like growth factor I (IGF-I). The identified modules provided insights into the interaction between metabolome and phenotype that reach beyond what can be found by commonly used statistical approaches for metabolomics. The approach is generic and can be readily applied to new datasets by other colleagues from the field.
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影响因子：
14.9
作者：
Kanehisa M;Goto S;Sato Y;Furumichi M;Tanabe M
通讯作者：
Tanabe M
影响因子：
5.8
作者：
May, Ali;Brandt, Bernd W.;Abeln, Sanne
通讯作者：
Abeln, Sanne
影响因子：
7.4
作者：
Kim, Ji Won;Ryu, Sung Ha;Kim, Kyu-Bong
通讯作者：
Kim, Kyu-Bong
影响因子：
7.4
作者：
Aguirre GA;De Ita JR;de la Garza RG;Castilla-Cortazar I
通讯作者：
Castilla-Cortazar I
DOI：
10.1007/s11306-015-0829-0
发表时间：
2015
期刊：
Metabolomics : Official journal of the Metabolomic Society
影响因子：
--
作者：
Krumsiek J;Mittelstrass K;Do KT;Stückler F;Ried J;Adamski J;Peters A;Illig T;Kronenberg F;Friedrich N;Nauck M;Pietzner M;Mook-Kanamori DO;Suhre K;Gieger C;Grallert H;Theis FJ;Kastenmüller G
通讯作者：
Kastenmüller G