GseaVis: An R Package for Enhanced Visualization of Gene Set Enrichment Analysis in Biomedicine

ABSTRACT Gene set enrichment analysis (GSEA) is a widely used computational method for determining whether predefined sets of genes show statistically significant concordant differences between two biological states. Despite its popularity, effective visualization of GSEA results remains challenging particularly for users seeking to extract meaningful insights without extensive programming knowledge. Although several tools are available for visualizing GSEA results, many lack the flexibility and customization options necessary for a comprehensive exploration of the data. For instance, the desktop GSEA software generates basic plots that are not publication ready and offer limited options for editing or modification. Users often encounter difficulties adjusting graphical parameters to achieve the desired level of customization or visual quality. Furthermore, traditional tools often fail to meet the demands of emerging analytical needs. For instance, they will lack the capability to effectively compare pathway activity levels across multiple experimental conditions. To bridge this gap, we introduce GseaVis, a user‐friendly R package specifically designed to simplify and enhance the visualization of GSEA results. GseaVis provides a variety of highly customizable and publication‐ready plots including enrichment plots, ranked gene heatmaps, and other forms of graphic visualizations of enriched gene sets. With its simple interface and flexibility, our tool significantly lowers the barrier for biologists and bioinformaticians to explore and present their GSEA data clearly and effectively. The GseaVis package is available on GitHub and is integrated with well‐established R libraries, allowing easy data manipulation and seamless integration into existing bioinformatics workflows. The GseaVis is publicly available via GitHub ( https://github.com/junjunlab/GseaVis ) for users’ access. A complete description of the usages can be found on the manuscript’s GitHub page ( https://junjunlab.github.io/gseavis‐manual/ ).