CePa

CePa performs pathway enrichment analysis by integrating network topology and node-based centrality measures to identify significant biological pathways.

Key Features:

• Implementation: Provided as an R package.
• Network-topology-based enrichment: Leverages pathway network topology to assess pathway significance.
• Node-centric analysis: Treats pathway nodes as the fundamental units of analysis to reflect the tendency of genes to function within complexes rather than in isolation.
• Multiple centralities: Uses multiple network centrality measures to assess node importance from different angles.
• Centrality integration: Integrates different centrality measures to capture diverse aspects of pathway significance.
• Enrichment methods: Implements both over-representation analysis and gene-set analysis procedures.
• Performance evaluation: Demonstrated high performance when evaluated with real-world data.

Scientific Applications:

• Pathway enrichment detection: Identifies biological pathways that are significantly enriched in a given dataset.
• Pathway function and interaction analysis: Supports analysis of pathway functions and interactions by incorporating network topology.
• Node/complex-level interpretation: Provides biologically relevant interpretation at the node or complex level rather than at the single-gene level.

Methodology:

Computes multiple network centrality measures for pathway nodes, integrates centrality measures to score nodes, and applies over-representation analysis and gene-set analysis procedures.