Oxylipin profile data analysis: Current methodologies, challenges, and future directions

Oxylipins are bioactive lipid mediators derived from the enzymatic and non-enzymatic oxidation of polyunsaturated fatty acids (PUFAs). They play diverse and potent roles in biological processes, with increasing relevance to a wide range of diseases and dietary interventions. While significant advances have been made in oxylipin profiling using targeted mass spectrometry-based techniques, challenges remain in the interpretation, integration, and visualization of these complex datasets. This review critically evaluates current methodologies, highlighting the application of classical statistical tests, regression models, and multivariate statistical methods (MSMs). MSMs, such as principal component analysis, partial least squares discriminant analysis, and non-metric multidimensional scaling, offer key advantages for exploring high-dimensional, collinear oxylipin datasets and are well-suited for both cross-sectional and time-course study designs. Newer approaches, including machine learning and deep learning models, are emerging as powerful tools but are currently constrained by data availability. We also review the integration of oxylipins into multi-omics frameworks and pathway-based analyses, highlighting opportunities and limitations in bioinformatics infrastructure. Finally, we propose future directions to enhance the biological interpretability of oxylipin data through pathway enrichment tools and incorporation into established omics platforms. Together, these developments will strengthen the understanding of oxylipin roles in systems biology and translational lipid research.