MAGE

MAGE performs meta-analysis and functional enrichment analysis of gene expression datasets to identify and annotate statistically significant biological terms across microarray and sequencing studies.

Key Features:

• Implementation: Python-based software package.
• Meta-analysis: Integrates established methodologies for conducting meta-analyses on gene expression studies.
• Bootstrap standard errors: Incorporates bootstrap standard errors for robust statistical evaluation.
• Multiple testing corrections: Applies corrections for multiple testing scenarios.
• Multiple outcomes: Supports meta-analysis of multiple outcomes.
• Probe-to-gene conversion: Converts probe data into gene identifiers for microarray or sequencing results.
• Functional enrichment: Conducts functional enrichment analysis on statistically significant enriched terms derived from the meta-analysis.
• Annotated outputs: Produces annotated results in various formats.

Scientific Applications:

• Cross-study differential expression: Aggregates results across studies to identify consistent differential expression signals.
• Enrichment analysis: Identifies enriched biological terms and pathways from meta-analytic results.
• Cross-platform interpretation: Aligns microarray or sequencing results to gene identifiers for comparison and integration.
• Multi-outcome assessment: Evaluates multiple outcomes across studies to support complex experimental designs.
• Annotation and reporting: Generates annotated result files in various formats to support biological interpretation and reporting.

Methodology:

Implemented in Python; integrates established meta-analysis methods; incorporates bootstrap standard errors; applies corrections for multiple testing; supports meta-analysis of multiple outcomes; converts probe data into gene identifiers; performs functional enrichment analysis on statistically significant enriched terms; outputs annotated results in various formats.