TRID

TRID analyzes gene expression and connectivity by integrating expression data with protein-protein interaction networks to identify disease-associated pathways.

Key Features:

• Dual Analysis Approach: Assesses both individual gene expression levels and gene connectivity to capture functional relationships that single-gene analyses may miss.
• TRID Weights and Network Integration: Integrates gene expression data with network analysis of protein-protein interaction (PPI) networks to calculate TRID weights used to evaluate genes and derive functional PPI modules.
• Pathway-level Consistency Detection: Detects consistent changes in biological pathways via functional PPI modules even when individual gene-level signals are heterogeneous across datasets.

Scientific Applications:

• Alzheimer’s disease hippocampus analysis: Applied to hippocampus tissue from three Alzheimer’s disease microarray datasets, revealing heterogeneity at the individual gene level but reproducible pathway-level changes in PPI modules.
• Neurodegenerative disorder investigation: Provides pathway-level insights that can be used to study molecular mechanisms in neurodegenerative disorders across heterogeneous datasets.

Methodology:

Calculates TRID weights by integrating gene expression data with network analysis of protein-protein interaction (PPI) networks to assess gene expression and connectivity and to identify functional PPI modules; analyses were demonstrated on microarray hippocampus datasets.