DiDyNet: a robust framework for differential dynamic network inference from longitudinal multi-omics data

Understanding disease dynamics from longitudinal multi-omics is hindered by traditional approaches that focus on univariate trajectories and static networks while ignoring temporal evolution. We developed DiDyNet, a framework for identifying phenotype-specific temporal molecular networks by defining dynamic coupling as coordinated molecular trajectories. DiDyNet operates through four steps: (i) two-dimensional variance-based filtering to prioritize dynamic features; (ii) quantification of subject-specific coordination using Dynamic Time Warping to accommodate asynchrony; (iii) statistical testing for differential dynamic couplings; and (iv) linear mixed model-based post-hoc refinement to distinguish genuine coordinated dynamics from stochastic noise.

Simulation studies showed that DiDyNet significantly outperformed static summary statistics, including the mean, median, and difference, which cannot capture dynamic signals. Dynamic Time Warping-based quantification also demonstrated greater robustness than Euclidean distance, correlation-based distance, and constrained alignment methods under temporal misalignment and signal sparsity. Application to an insulin resistance cohort identified a coordinated cross-omics network linking systemic inflammation with intracellular stress responses.

Source code is freely available at https://github.com/bioinfoliu/DiDyNet.