Contact: Luke W. Johnston, MSc, PhD Email: luke.johnston@rm.dk 0000000341692616 posters.lwjohnst.com/2020/ddanetwork 

Background: Metabolic data, especially from omics, is challenging to meaningfully analyze and visualize. Even more so when we’re interested in potential causal pathways. Our aim is to develop an algorithm and R package to estimate and visualize these pathways, in order to be used by the wider research community.
Methods: The NetCoupler algorithm estimates causal pathways between a network of metabolic variables and either 1) an “outcome” variable (i.e. influenced by the network), 2) an “exposure” variable (i.e. influences the network), or 3) both. The R package is being developed at github.com/NetCoupler and aims to be userfriendly.
The algorithm implementation follows these steps, visually shown in Figure 1:
Example of an analysis on the UK Biobank dataset using this algorithm is shown in Figure 2.
Conclusion: We hope the NetCoupler package will give researchers more tools to meaningfully analyze complex omic style data.