DNLC

DNLC detects changes in local expression consistency in genome-scale biological networks to identify subnetworks and genes whose interactions differ between clinical conditions.

Key Features:

• Local Expression Consistency Detection: Employs the Local Moran's I statistic to identify subnetworks exhibiting significant changes in local expression consistency between clinical conditions.
• Module Selection: Selects specific genes and network modules from existing networks where local expression consistency shifts across conditions.
• Simulation Validation: Validated using simulations that detect artificially induced changes in local consistency.
• Application to Real Datasets: Applied to publicly available expression datasets to uncover novel genes and network modules with biological relevance.
• Complementary Analysis: Focuses on changes in local consistency as a complement to traditional differential expression analyses.

Scientific Applications:

• Disease mechanism analysis: Identifies condition-specific subnetworks and genes that may underlie disease-related regulatory changes.
• Developmental biology: Detects modules whose local interactions change across developmental stages or contexts.
• Network-level regulatory discovery: Reveals regulatory modules and interaction changes not apparent from differential expression alone.

Methodology:

Uses the Local Moran's I statistic to assess local expression consistency in biological networks, performs gene and module selection from existing networks, and has been validated by simulations and applied to public expression datasets.