Multi Experiment Matrix

Multi Experiment Matrix aggregates hundreds of microarray gene expression datasets and applies rank aggregation to identify statistically significant gene co-expression patterns across species and platforms.

Key Features:

• Large-Scale Data Integration: Provides access to hundreds of publicly available gene expression datasets from diverse tissues, diseases, and experimental conditions, organized by species and microarray platform.
• Rank Aggregation Methodology: Uses rank aggregation to merge information from multiple datasets into a single global ordering while estimating the statistical significance of gene co-expression patterns.
• Automatic Detection and Visualization: Automatically detects and characterizes datasets with strong coexpression patterns and provides visualization to facilitate interpretation.
• Robust Rank Aggregation (RRA): Implements Robust Rank Aggregation to detect genes consistently ranked better than expected under the null hypothesis of uncorrelated inputs and assigns significance scores to genes.
• Parameter-Free and Outlier-Resistant: Employs a probabilistic RRA model that is parameter-free and resistant to outliers, noise, and errors.

Scientific Applications:

• Cross-condition co-expression discovery: Identify genes with consistent co-expression patterns across experimental setups and datasets.
• Disease and treatment impact analysis: Compare expression data from healthy and affected tissues to analyze the impact of diseases or treatments on gene expression.
• Evolutionary conservation of expression: Explore conservation of gene expression patterns across species using integrated cross-species datasets.

Methodology:

Aggregation of numerous microarray datasets categorized by species and platform; integration using rank aggregation to produce a unified global ordering with statistical significance estimation; automatic identification and visualization of significant coexpression patterns; application of the Robust Rank Aggregation probabilistic model to assign significance scores and reduce the influence of outliers, noise, and errors.