MODA

MODA performs differential analysis of gene co-expression networks from transcriptomic data to identify condition-specific and conserved modules and associate them with biological processes.

Key Features:

• R package implementation: Implemented as an R package for analysis of transcriptomic gene expression data.
• Condition-specific gene co-expression networks: Estimates and constructs gene co-expression networks tailored to specific experimental conditions.
• Differential subnetwork identification: Identifies differentially expressed subnetworks and classifies modules as conserved or condition-specific.
• Biological process association: Links identified modules to relevant biological processes to aid functional interpretation.
• Dataset support: Applied to both synthetic datasets and real-world transcriptomic data, including Daphnia magna under environmental stresses.

Scientific Applications:

• Daphnia magna environmental stress analysis: Reveals condition-specific genetic responses in Daphnia magna exposed to environmental stresses.
• Synthetic data validation: Demonstrates method behavior and validation on synthetic gene expression datasets.
• Gene regulatory mechanism exploration: Identifies conserved and condition-specific modules to investigate gene regulatory mechanisms.
• Environmental genomics, disease research, and evolutionary biology: Supports module-level analyses across conditions in these research domains.

Methodology:

Implemented as an R package, MODA estimates and constructs condition-specific gene co-expression networks from transcriptomic data, identifies differentially expressed subnetworks and classifies modules as conserved or condition-specific, and associates modules with biological processes; it has been applied to synthetic data and Daphnia magna expression under environmental stresses.