🤖 AI Summary
This study addresses the challenges of high dimensionality and temporal dependence in modeling dynamic co-expression networks from longitudinal omics data by proposing a novel approach based on multivariate linear mixed-effects models. The method captures both fixed and random effects of molecular features and leverages correlations among random effects to characterize node dependencies, thereby constructing time-evolving co-expression networks. Two innovative penalized algorithms grounded in thresholded covariance estimation are introduced to substantially improve the accuracy of network structure inference. Simulation studies demonstrate that the proposed method outperforms existing approaches in terms of mean squared error and mean absolute error. Applied to the CARDIA cohort data, it successfully uncovers temporal evolution patterns of protein co-expression networks and their associations with longitudinal trajectories.
📝 Abstract
High-throughput sequencing technologies have enabled the collection of large-scale longitudinal -omics data, providing new opportunities for studying co-expression networks among molecular nodes such as genes and proteins. However, the high dimensionality and temporal dependence inherent in such data require specialized statistical methods. We propose a novel approach to infer dynamic co-expression networks among features over time (DCENt), where each node (feature) is modeled with a mixed-effects model, and dependencies among nodes are captured through correlated random effects. We develop two innovative penalized algorithms which harness the state of the art of threshold covariance estimators to estimate the random-effects covariance structure. Simulation studies show improved performance over existing approaches in terms of both mean square error and mean absolute error. We further apply the methods to data from the CARDIA study to investigate how the protein co-expression networks evolve over time as well as the association between protein trajectory patterns.