  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 omics layers, improving model inference and edge recovery
compared to state-of-the-art methods. We further demonstrate in an analysis of
joint transcriptome - methylome data from TCGA breast cancer specimens that
DRAGON can identify key molecular mechanisms such as gene regulation via
promoter methylation. In particular, we identify Transcription Factor AP-2 Beta
(TFAP2B) as a potential multi-omic biomarker for basal-type breast cancer.
DRAGON is available as open-source code in Python through the Network Zoo
package (netZooPy v0.8; netzoo.github.io).
