NEAT

NEAT performs network enrichment analysis by integrating gene-set enrichment with the relational context of directed, undirected, and partially directed gene networks using a hypergeometric null model to detect statistically significant enrichments while avoiding resampling and normality assumptions.

Key Features:

• Support for network types: Analyzes directed, undirected, and partially directed gene networks.
• Hypergeometric null model: Employs the hypergeometric distribution as the null model for network enrichment testing.
• Avoidance of resampling and normality assumptions: Does not rely on computationally intensive resampling methods or normality assumptions.
• Computational efficiency: Demonstrates substantially faster performance in simulations compared to resampling-based methods while maintaining or improving detection capacity for enrichments.
• Integration of gene-set and network context: Combines gene enrichment analysis with network relational information to identify functional associations.

Scientific Applications:

• Yeast gene-network analysis: Applied to yeast networks to test enrichment of the Environmental Stress Response (ESR) target gene set with Gene Ontology (GO Slim) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional gene sets.
• Inspection of functional gene-set associations: Enables examination of associations between different functional gene sets to elucidate biological process relationships.
• Functional relationship discovery: Used where understanding functional relationships between genes within networks is crucial.

Methodology:

Performs a statistical test based on the hypergeometric distribution to identify statistically significant enrichments within gene networks.