ESEA: Discovering the Dysregulated Pathways based on Edge Set Enrichment Analysis

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Pathway analysis is crucial for understanding biological mechanisms, cellular functions, and disease states.
• Existing methods primarily focus on gene expression changes, potentially overlooking alterations in biological relationships within pathways.

Purpose of the Study:

• To introduce Edge Set Enrichment Analysis (ESEA), a novel computational method for identifying dysregulated pathways.
• To investigate pathway dysregulation by analyzing changes in biological relationships in conjunction with gene expression data.

Main Methods:

• Developed Edge Set Enrichment Analysis (ESEA), a computational approach focusing on biological relationships.
• Validated ESEA using simulation studies and real-world datasets (p53 mutation, Type 2 diabetes, lung cancer).
• Compared ESEA performance against five other pathway enrichment analysis methods.

Main Results:

• ESEA effectively identifies dysregulated pathways underlying complex traits and human diseases.
• Simulation studies demonstrated ESEA's power and performance across various conditions.
• Real-world data analysis confirmed ESEA's capability in uncovering disease-associated pathways.

Conclusions:

• ESEA offers a powerful and novel approach to pathway analysis by incorporating dysregulated biological relationships.
• The freely available R-based tool supports analysis across seven public pathway databases.
• ESEA enhances the understanding of biological pathways in disease contexts.