DDN2.0: R and Python packages for differential dependency network analysis of biological systems

Data-driven differential dependency network analysis identifies in a complex and often unknown overall molecular circuitry a network of differentially connected molecular entities (pairwise selective coupling or uncoupling depending on the specific phenotypes or experimental conditions). Such differential dependency networks are typically used to assist in the inference of potential key pathways. Based on our previously developed Differential Dependency Network (DDN) method, we report here the fully implemented R and Python software tool packages for public use. The DDN algorithm uses a fused Lasso model and block-wise coordinate descent to estimate both the common and differential edges of dependency networks. The identified DDN can help to provide plausible interpretation of data, gain new insight of disease biology, and generate novel hypotheses for further validation and investigations. Availability and ImplementationThe open-source R and Python codes are freely available for download at https://github.com/MintaYLu/DDN Contactyuewang@vt.edu, lyz66@vt.edu Supplementary informationSupplementary data are available upon request.