RadarOmics: Intuitive visualisation of multidimensional omics data in ecological, evolutionary, and developmental studies

Abstract

Interpreting high-dimensional omics datasets requires visualisation tools that reveal coordinated responses across multiple biological processes. Existing approaches such as heatmaps or enrichment plots typically present processes independently and struggle to convey system-level patterns as experimental complexity increases. We developed RadarOmics, an R package that integrates multidimensional omics data using multi-axis radar visualisations. RadarOmics performs dimensional reduction (based on scaling, Principal Component Analyses, or Linear Discriminant Analyses) for predefined biological processes to extract a representative value for each sample and process of interest. These values are displayed in a circular radar layout, enabling rapid identification of global trends, outliers, and trade-offs among biological functions. Using transcriptomic datasets from anemonefish metamorphosis and zebrafish chemical exposure assays, we show that radar-based visualisations can reveal coordinated molecular responses that are less readily captured with traditional visualisation outputs. RadarOmics provides a flexible and intuitive framework for summarising and interpreting biological variation in ecological, evolutionary, and developmental omics studies, offering a compact system-level overview of molecular behaviour across complex experimental designs.