iDDN: determining trans-omics network structure and rewiring with integrative differential dependency networks.

Motivation: Mapping the gene networks that drive disease progression allows identifying molecules that rectify the network by normalizing pivotal regulatory elements. Upon mechanistic validation, these upstream normalizers represent attractive targets for developing therapeutic interventions to prevent the initiation or interrupt the pathways of disease progression. Differential network analysis aims to detect significant rewiring of regulatory network structures under different conditions. With few exceptions, most existing tools are limited to inferring differential networks from single-omics data that could be incomplete and prone to collapse when trans-omics multifactorial regulatory mechanisms are involved. Results: We previously developed an efficient differential network analysis method-Differential Dependency Networks (DDN), that enables joint learning of common network structure and rewiring under different conditions. We now introduce the integrative DDN (iDDN) tool that extends this framework with biologically principled designs to make robust multi-omics differential network inferences. The comparative experimental evaluations on both realistic simulations and case studies show that iDDN can help biologists more accurately identify, in a study-specific and often unknown trans-omics regulatory circuitry, a network of differentially wired molecules potentially responsible for phenotypic transitions. Availability and implementation: The Python package of iDDN is available at https://github.com/cbil-vt/iDDN. A user's guide is provided at https://iddn.readthedocs.io/.