GOATEA: gene set enrichment analysis in R with shiny interactive visualizations

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics and Proteomics

Background:

• High-throughput technologies generate complex biological data requiring sophisticated analysis.
• Existing enrichment tools often lack user-friendliness, advanced features like multi-contrast comparison, and integrated gene/network context.
• The Geneset Ordinal Association Test (GOAT) is a statistical method for evaluating differential expression in gene or protein sets.

Purpose of the Study:

• To develop an accessible and comprehensive tool for pathway enrichment analysis.
• To extend the GOAT algorithm with interactive visualization and multi-contrast capabilities.
• To integrate gene-level and network-based context for bottom-up pathway analysis and hypothesis generation.

Main Methods:

• Development of GOATEA, an R/Shiny application implementing and extending the GOAT algorithm.
• Integration of interactive visualization, multi-contrast comparison, and gene/network context.
• Application to the Colameo dataset with paired transcriptomic and proteomic data for multi-omics analysis.

Main Results:

• GOATEA enables independent and integrated analysis of transcriptomic and proteomic data.
• Demonstrated capability for multi-omics analysis and simultaneous comparison of multiple contrasts.
• Identified shared genes and emphasized protein-protein interactions for hypothesis generation through integrated network context.

Conclusions:

• GOATEA streamlines enrichment analysis with a user-friendly interface and interactive visualizations.
• Facilitates exploratory analysis and hypothesis generation for researchers of all technical backgrounds.
• Available as an open-source tool with comprehensive documentation and usage vignettes.