NetworkAnalyst

NetworkAnalyst performs integrative analysis of gene expression data to support meta-analysis, network analysis, and visualization for identifying molecular signatures and elucidating biological processes.

Key Features:

• Integrative gene expression analysis: Applies advanced statistical methods to combine and analyze gene expression datasets.
• Meta-analysis of single or multiple datasets: Performs meta-analysis of single or multiple gene expression datasets while considering metadata parameters.
• Network analysis from gene lists: Constructs and analyzes networks derived from input gene lists.
• Protein-protein interaction (PPI) networks: Incorporates protein-protein interaction networks to provide biological context for gene sets.
• Heatmaps: Generates heatmaps to visualize expression patterns across genes and samples.
• Chord diagrams: Uses chord diagrams to visualize relationships among genes or modules.
• Statistical procedures: Integrates well-established statistical procedures alongside advanced statistical methods for analysis.
• Molecular signature identification: Identifies robust molecular signatures and links them to functional evidence and biological processes.

Scientific Applications:

• Molecular signature discovery: Detects robust gene expression signatures across individual or multiple studies.
• Functional contextualization: Contextualizes gene expression changes within biological processes using PPI networks and functional evidence.
• Comparative meta-analysis: Compares and integrates multiple gene expression datasets to identify consistent signals while accounting for metadata.
• Hypothesis generation: Facilitates interpretation and hypothesis generation from large, complex gene expression datasets.

Methodology:

Applies advanced statistical methods and well-established statistical procedures for meta-analysis of gene expression datasets, performs network analysis using protein-protein interaction networks, and employs visualizations including heatmaps and chord diagrams.