clipper

clipper implements topological gene set analysis using a two-step empirical approach that exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path.

Key Features:

• Pathway Significance Testing: Selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies.
• Signal Path Identification: "Clips" the whole pathway to identify the signal paths having the greatest association with a specific phenotype.
• Graph Decomposition: Exploits graph decomposition theory to create a junction tree for pathway analysis.

Scientific Applications:

• Topological Gene Set Analysis: Analyzes gene sets while incorporating pathway topology information rather than treating pathways as simple gene lists.
• Phenotype Association Mapping: Identifies signal paths within pathways that have the greatest association with a specific phenotype.

Methodology:

clipper uses a two-step empirical approach: first, statistical tests on the means and the concentration matrices of graphs derived from pathway topologies select significant pathways; second, the whole pathway is "clipped" to identify signal paths with the greatest phenotype association. Graph decomposition theory creates a junction tree to reconstruct the most relevant signal path.