GXNA

GXNA identifies high-scoring subnetworks from gene expression data using gene interaction networks to detect differentially expressed pathways and gene groups.

Key Features:

• Gene Interaction Network Construction: Builds gene interaction networks from prior biological knowledge (inspired by Ideker et al. (2002)).
• High-Scoring Subnetwork Identification: Applies scoring functions and algorithms to search for subnetworks with high relevance scores indicative of biological significance, with reported improvements in speed and accuracy over previous methods.
• Biological Interpretation: Produces subnetworks and scores that facilitate interpretation of gene expression patterns in the context of known interactions and pathways.
• Significance Adjustment: Assigns statistical significance to subnetworks and adjusts for multiple testing to control false discoveries.

Scientific Applications:

• Human cancer microarray analysis: Applied to human microarray datasets related to cancer to recover known pathways and suggest novel candidate pathways.
• Immune system microarray analysis: Applied to human immune system microarray datasets to retrieve biologically relevant pathways and potential new mechanisms.

Methodology:

Constructing a gene interaction network from prior biological knowledge; applying refined scoring functions and algorithms to identify high-scoring subnetworks; and assigning significance with multiple testing adjustment.