4 years ago

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

Wilco Ligterink, Hae-Won Uh, Fred van Eeuwijk, Jeanine-J. Houwing-Duistermaat, Henk W. M. Hilhorst, Julio Maia, Georgios Bartzis, Joris Deelen



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 \(\mathbf {X}_m\) .


Here, we use network analysis methods to recover a set of metabolite relationships, by finding metabolites sharing a similar relation to \(\mathbf {X}_m\) . Metabolite values are based on information coming from individuals’ \(\mathbf {X}_m\) status which might interact with other covariables.


Alternative to using the original metabolite values, the total information is decomposed by utilizing a linear regression model and the part relevant to \(\mathbf {X}_m\) 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.


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.


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.

Publisher URL: https://link.springer.com/article/10.1007/s11306-017-1263-2

DOI: 10.1007/s11306-017-1263-2

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.