DNEA

DNEA implements the Differential Network Enrichment Analysis algorithm for data-driven network analysis and enrichment testing of -omics data across two experimental conditions.

Key Features:

• Multi-omics Input Support: Accepts an m x n expression matrix for metabolomics, lipidomics, proteomics, and other -omics modalities.
• Joint Network Estimation: Jointly estimates biological network associations for each condition using the underlying correlation structure of the data to determine feature-feature interactions.
• Differential Network Construction: Builds differential networks from data containing two experimental groups.
• Enrichment Analysis: Identifies differentially enriched metabolic modules through data-driven enrichment analysis.
• Expanded Compound Scope: Supports construction of biological networks and enrichment testing for exogenous, secondary, and unknown compounds beyond well-annotated endogenous metabolites.
• Parallel Processing: Incorporates a parallel processing framework built into the most computationally extensive steps of the algorithm.
• Modular Package Structure: Provides a modular architecture for flexible analysis of large and complex datasets.

Scientific Applications:

• Metabolomics Network Analysis: Analyzes metabolomics data to define metabolite-metabolite interactions using partial correlations of experimentally determined metabolite measurements.
• Untargeted Metabolomics: Enables analysis of unknown compounds ubiquitous in untargeted metabolomics data that cannot be mapped to traditional metabolic pathways.
• Cross-condition Comparison: Performs data-driven enrichment analysis across two experimental conditions utilizing correlation structure.
• Large-scale Dataset Analysis: Analyzes large and complex metabolomics datasets with enhanced computational performance over predecessor tools.

Methodology:

The DNEA algorithm jointly estimates biological network associations of each condition from an expression matrix, utilizing the underlying correlation structure of the data to determine feature-feature interactions, followed by differential network construction and enrichment testing for metabolic modules.