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) (Herrington, et al. 2018; Zhang, et al., 2009; Zhang and Wang, 2010; Zhang, et al., 2016). 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 DDN2.0 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. To address the imbalanced sample group problem, we propose a sample-size normalized formulation to correct systematic bias. To address high computational complexity, we propose four strategies to accelerate DDN2.0 learning. The experimental results show that new DDN2.0+ learning speed with combined four accelerating strategies is hundreds of times faster than that of DDN2.0 algorithm on medium-sized data (Fu, 2019). To detect intra-omics and inter-omics network rewiring, we propose multiDDN using a multi-layer signaling model to integrate multi-omics data. The simulation study shows that the multiDDN method can achieve higher accuracy of detecting network rewiring (Fu, 2019).