DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks.

ABSTRACT: The increasing quantity of multi-omic data, such as methylomic and transcriptomic profiles collected on the same specimen or even on the same cell, provides 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 den

SUBMITTER: Shutta KH 

PROVIDER: S-EPMC9943674 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature