Multi Experiment Matrix (MEM)

Multi Experiment Matrix (MEM) performs gene expression similarity searches across aggregated microarray datasets to identify coexpression patterns and support comparative transcriptomics.

Key Features:

• Rank Aggregation: Aggregates rank data from multiple microarray datasets into a unified global ordering with simultaneous statistical significance estimation.
• Coexpression Pattern Detection and Visualization: Automatically detects, characterizes, and visualizes datasets that exhibit the strongest coexpression patterns.
• Gene Expression Similarity Searches: Performs similarity searches of gene expression profiles across numerous datasets to enable cross-study comparative analyses.
• ExpressView Gene Clustering and Visualization: Provides ExpressView gene clustering and graphical visualization to organize genes into clusters based on expression patterns.
• Differential Expression Search (DiffExp): Searches for differentially expressed genes between experimental conditions or biological states.

Scientific Applications:

• Systems Biology: Integrates multi-experiment expression data to support systems-level analyses of gene expression patterns.
• Functional Genomics: Identifies coexpressed genes across studies to aid discovery of gene functions and regulatory mechanisms.
• Comparative Transcriptomics: Enables cross-study comparison and meta-analysis of microarray expression data from repositories such as ArrayExpress and GEO.
• Visualization for Hypothesis Generation: Uses clustering and graphical outputs to assist interpretation of complex datasets and generation of testable hypotheses.

Methodology:

Integrates and analyzes microarray data from sources including ArrayExpress and GEO; aggregates rank data from multiple experiments using rank aggregation to produce a global ordering; applies statistical techniques to estimate significance of aggregated ranks and observed patterns; detects and visualizes coexpression patterns and performs gene clustering with ExpressView; conducts differential expression searches via DiffExp.