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Journal Article

Estimation of metabolite networks with regard to a specific covariable: applications to plant and human data


Deelen,  J.
Deelen – Genetics and Biomarkers of Human Ageing, Research Groups, Max Planck Institute for Biology of Ageing, Max Planck Society;

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Bartzis, G., Deelen, J., Maia, J., Ligterink, W., Hilhorst, H. W. M., Houwing-Duistermaat, J. J., et al. (2017). Estimation of metabolite networks with regard to a specific covariable: applications to plant and human data. Metabolomics, 13(11), 129. doi:10.1007/s11306-017-1263-2.

Cite as: https://hdl.handle.net/21.11116/0000-000B-55F2-4
INTRODUCTION: In systems biology, where a main goal is acquiring knowledge of biological systems, one of the challenges is inferring biochemical interactions from different molecular entities such as metabolites. In this area, the metabolome possesses a unique place for reflecting "true exposure" by being sensitive to variation coming from genetics, time, and environmental stimuli. While influenced by many different reactions, often the research interest needs to be focused on variation coming from a certain source, i.e. a certain covariable [Formula: see text]. OBJECTIVE: Here, we use network analysis methods to recover a set of metabolite relationships, by finding metabolites sharing a similar relation to [Formula: see text]. Metabolite values are based on information coming from individuals' [Formula: see text] status which might interact with other covariables. METHODS: Alternative to using the original metabolite values, the total information is decomposed by utilizing a linear regression model and the part relevant to [Formula: see text] is further used. For two datasets, two different network estimation methods are considered. The first is weighted gene co-expression network analysis based on correlation coefficients. The second method is graphical LASSO based on partial correlations. RESULTS: We observed that when using the parts related to the specific covariable of interest, resulting estimated networks display higher interconnectedness. Additionally, several groups of biologically associated metabolites (very large density lipoproteins, lipoproteins, etc.) were identified in the human data example. CONCLUSIONS: This work demonstrates how information on the study design can be incorporated to estimate metabolite networks. As a result, sets of interconnected metabolites can be clustered together with respect to their relation to a covariable of interest.