# GeDi: simplifying gene set distances for enhanced omics interpretation in R/Bioconductor

## TL;DR

GeDi is an R/Bioconductor package that simplifies gene set enrichment results by clustering them and integrating network data for better biological interpretation.

## Contribution

GeDi introduces a new method for aggregating gene sets using distance metrics and PPI data to reduce redundancy and enhance interpretation.

## Key findings

- GeDi clusters gene sets to reveal coherent biological themes from enrichment results.
- Integration of protein–protein interaction data improves biological context in functional analysis.
- The package is available as an R package and Shiny app for streamlined analysis and visualization.

## Abstract

Functional enrichment analysis is a standard component in many omics data analysis workflows, supported by a variety of methods and algorithms. However, despite their utility and wide application, these methods often return the results as an extensive and redundant list of gene sets, impeding interpretation and hypothesis generation. Moreover, network based information can provide additional biological context through functional interaction data, yet this is often overlooked by existing tools.

We developed GeDi, an R/Bioconductor package designed to streamline and standardize the interpretation of functional enrichment results. GeDi aggregates gene sets into biologically meaningful clusters using a suite of gene set distance metrics and clustering algorithms, aimed to reduce redundancy and improve clarity. GeDi also enables the integration of protein–protein interaction (PPI) data, through the implementation of a weighted distance metric, providing a richer biological context by capturing functional connectivity between pathways and their components. The package offers visualizations, aggregation, and automated reporting, and is available as both a stand-alone R-package and an interactive Shiny application.

GeDi facilitates clearer, faster interpretation of enrichment results by combining clustering and network context. Application to a public RNA-seq dataset revealed coherent biological themes, supporting both experimental and computational research. GeDi is freely available in the Bioconductor project under the MIT license (https://bioconductor.org/packages/GeDi), and a demo instance is accessible on the Shiny server (http://shiny.imbei.uni-mainz.de:3838/GeDi).

The online version contains supplementary material available at 10.1186/s12859-025-06335-6.