coGPS

coGPS performs gene set enrichment analysis to detect outlier genes in merged multi-study genomic datasets using p-value-based statistics.

Key Features:

• Gene Set Enrichment Analysis (GSEA): Performs GSEA on gene-level statistics to identify enriched gene sets associated with outlier behavior.
• P-value-based statistics: Calculates and analyzes per-gene p-values to determine significant deviations in merged datasets.
• Outlier gene detection: Focuses on identifying genes that exhibit outlier behavior across combined studies.
• Multi-study data integration: Operates on datasets merged from multiple studies to assess consistency and variability across experiments.
• R and Bioconductor integration: Implemented in R and interoperates with the Bioconductor ecosystem.

Scientific Applications:

• Outlier detection in genomic studies: Identifies potential biomarkers or novel targets by highlighting genes with significant deviations across datasets.
• Multi-study meta-analysis: Provides a systematic approach to assess consistency and variability when combining data from multiple studies.
• Disease and systems-level studies: Applicable to cancer genomics, transcriptomics, and systems biology to analyze gene function and regulation across conditions.

Methodology:

Calculates per-gene p-values on merged datasets and applies gene set enrichment analysis using R.