Non Parametric Differential Network Analysis: A Tool for Unveiling Specific Molecular Signatures.

Abstract The rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential Network Analysis (DNA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DNA algorithms scrutinize alterations in interaction patterns derived from experimental data. 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. 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. Author summary This paper explores a novel technique for Differential Network Analysis (DNA) that considers sex-based variations. DNA compares biological networks under different conditions, like healthy vs. diseased states. Our method tackles the limitations of traditional DNA approaches, which often assume specific data distributions. We propose a non-parametric DNA methodology that integrates sex differences and identifies differential edges between networks. This approach utilizes data on gene expression levels and sex to construct a more accurate picture of the molecular mechanisms underlying diseases, particularly those exhibiting sex-dependent variations. Our method paves the way for a deeper understanding of how sex and age influence disease processes at the molecular level.