BACKGROUND: The rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential network analysis (DINA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DINA algorithms scrutinize alterations in interaction patterns derived from experimental data. RESULTS: Introducing a novel approach to differential network analysis, we incorporate differential gene expression based on sex and gender attributes. We hypothesize that gene expression can be accurately represented through non-Gaussian processes. Our methodology involves quantifying changes in non-parametric correlations among gene pairs and expression levels of individual genes. CONCLUSIONS: Applying our method to public expression datasets concerning diabetes mellitus and atherosclerosis in liver tissue, we identify gender-specific differential networks. Results underscore the biological relevance of our approach in uncovering meaningful molecular distinctions.

Many modern data sets require inference methods that can estimate the shared and individual-specific components of variability in collections of matrices that change over time. Promising methods have been developed to analyze these types of data in static cases, but only a few approaches are available for dynamic settings. To address this gap, we consider novel models and inference methods for pairs of matrices in which the columns correspond to multivariate observations at different time points. In order to characterize common and individual features, we propose a Bayesian dynamic factor modeling framework called Time Aligned Common and Individual Factor Analysis (TACIFA) that includes uncertainty in time alignment through an unknown warping function. We provide theoretical support for the proposed model, showing identifiability and posterior concentration. The structure enables efficient computation through a Hamiltonian Monte Carlo (HMC) algorithm. We show excellent performance in simulations, and illustrate the method through application to a social mimicry experiment.