GeDi - Improving gene set distances accounting for network-based information

GeDi - Improving gene set distances accounting for network-based information

Author(s): Annekathrin Silvia Ludt,Federico Marini

Affiliation(s): Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

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Functional enrichment analysis, performed either via scripted analysis or with web-based tools, is one of the most frequently adopted steps in computational biology, especially when aiming to identify the systems level mechanisms captured by high-dimensional molecular datasets. However, the contextualization and evaluation of the results can be a complex and time-consuming task, as often these are provided in the form of large tables and their interpretation is additionally complicated by the redundancy in the features under inspection. In this short talk, we present GeDi, an R package containing a Shiny application for the interactive analysis, exploration and interpretation of functional enrichment results. GeDi is designed for the computation of distances between sets of genes, or 'genesets', while accounting for network-based information, provenient e.g. from large scale protein-protein interaction databases. Using a variety of similarity metrics, GeDi can identify and compare the functional similarity of sets of genes, enabling researchers to better understand the relationships between genes and their role in biological processes. GeDi also provides tools for visualizing and interpreting the results of these analyses, making it a valuable resource for researchers studying genomics and gene expression. In addition to the functionality provided with the package, we also developed a Shiny application which provides the users a well-arranged UI for the analysis of their data. Users can either upload their data and interactively analyze and explore it with the various visualization tools, including heatmaps, dendrograms, and other network visualizations. . These features make GeDi a powerful tool for researchers who want to explore and elucidate the functional relationships between sets of genes, and the potential implications for disease, drug discovery, and more.

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