tvsfglasso: Time-varying scale-free graphical lasso for network estimation from time-series data

In high-dimensional gene co-expression network analysis, capturing the temporal changes of gene associations is crucial for unveiling dynamic regulatory mechanisms inherent in biological systems. Examining how these interactions change over time offers valuable insights into the developmental and adaptive processes that drive an organism's lifecycle. Moreover, incorporating structural prior information can substantially enhance the accuracy and interpretability of the estimated sparse dynamic gene network. Methods previously proposed in the literature cannot simultaneously model sparse time-varying co-expression network structure and have the power-law degree distribution. Additionally, there is a demand of time-efficient, memory-light software implementations and possibility to utilize repeated measures at each time-point (if available). In this paper, we introduce the time-varying scale-free graphical lasso (tvsfglasso), a novel scalable framework for estimating high-dimensional time-varying gene co-expression networks under the assumption that these networks simultaneously exhibit sparse and a scale-free structure. We utilize fast algorithms developed for the graphical lasso (glasso), which makes tvsfglasso a scalable tool for high-dimensional problems. We evaluate the performance of tvsfglasso using both simulated and real-world dynamic gene expression time series datasets, demonstrating its capability to detect temporal changes in gene associations. Our results highlight the potential of tvsfglasso to advance the understanding of dynamic gene networks, making this estimator useful for more accurate modeling of complex biological processes.