  The increasing quantity of multi-omics data, such as methylomic and
transcriptomic profiles, collected on the same specimen, or even on the same
cell, provide a unique opportunity to explore the complex interactions that
define cell phenotype and govern cellular responses to perturbations. We
propose a network approach based on Gaussian Graphical Models (GGMs) that
facilitates the joint analysis of paired omics data. This method, called DRAGON
(Determining Regulatory Associations using Graphical models on multi-Omic
Networks), calibrates its parameters to achieve an optimal trade-off between
the network's complexity and estimation accuracy, while explicitly accounting
for the characteristics of each of the assessed omics layers. In simulation
studies, we show that DRAGON adapts to edge-density and feature-size
differences between the omics layers, which improves model inference and the
recovery of associations compared to state-of-the-art network inference using
GGMs. Next, we verified this performance gain on paired single-cell data, where
the transcriptome was assayed together with selected proteins. Finally, we used
DRAGON to study joint transcriptome-methylome data of breast cancer specimens.
In this context, we provide examples of how DRAGON can identify key molecular
mechanisms. For instance, we show how co-expression can be explained by
co-methylation and how DRAGON gives hints about the spatial organization of the
genome. DRAGON is available as open-source code in python through the Network
Zoo package (netZooPy v0.8; netzoo.github.io).
