GSEAmining

GSEAmining clusters gene set collections based on the presence of the same genes in the leading edge (core) subset to help researchers make biological sense of Gene Set Enrichment Analysis outputs.

Key Features:

• Leading Edge Clustering: Clusters gene sets based on shared genes in the leading edge subsets that contribute most to the enrichment score.
• Term Visualization: Identifies and represents the most enriched terms in gene set names as wordclouds for each cluster.
• Gene Visualization: Identifies and represents the most enriched genes in the leading edge subsets as bar plots for each cluster.
• Directional Coloring: Displays positive and negative enrichments in different colors to distinguish biological processes or genes of interest.

Scientific Applications:

• GSEA Output Interpretation: Reduces redundancy and extracts meaningful biological information from large GSEA result sets, particularly for non-hierarchical gene set collections such as those in the Molecular Signatures Database.
• Biological Process Identification: Groups gene sets participating in similar biological processes through shared leading edge genes.

Methodology:

GSEAmining clusters gene set collections based on shared genes in the leading edge (core) subset, then identifies and visualizes the most enriched terms in gene set names and the most enriched genes in leading edge subsets for each cluster.