Effect Size-Driven Pathway Meta-Analysis for Gene Expression Data

2025-01-23

AI Summary

Traditional gene-expression meta-analyses operate at the single-gene level, suffering from information loss and limited biological interpretability due to cross-platform gene absence and technical heterogeneity. To address this, we propose a pathway-level effect-size-driven meta-analysis paradigm. Our method introduces a novel aggregation strategy based on single-sample Gene Set Enrichment Analysis (ssGSEA), constructing pathway-level matrices that preserve both magnitude and directionality of effects. It integrates ssGSEA, effect-size-weighted meta-analysis, and R-based statistical modeling, implemented as an open-source CRAN R package. Validated across multiple datasets for systemic lupus erythematosus (SLE) and Parkinson’s disease, our approach significantly reduces false-positive rates while enhancing cross-platform comparability and biological interpretability of pathway activity—overcoming key limitations of conventional gene-centric meta-analysis.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Ensemble Methods
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

The proliferation of omics datasets in public repositories has created unprecedented opportunities for biomedical research but has also posed significant challenges for their integration, particularly due to missing genes and platform-specific discrepancies. Traditional gene expression metaanalysis often focuses on individual genes, leading to data loss and limited biological insights when there are missing genes across different studies. To address these limitations, we propose GSEMA (Gene Set Enrichment Meta-Analysis), a novel methodology that leverages singlesample enrichment scoring to aggregate gene expression data into pathway-level matrices. By applying meta-analysis techniques to enrichment scores, GSEMA preserves the magnitude and directionality of effects, enabling the definition of pathway activity across datasets. Using simulated data and case studies on Systemic Lupus Erythematosus (SLE) and Parkinson's Disease (PD), we demonstrate that GSEMA outperforms other methods in controlling false positive rates while providing meaningful biological interpretations. GSEMA methodology is implemented as an R package available on CRAN repository

Problem

• Integrating omics datasets with missing genes across platforms
• Overcoming limitations of individual gene-level meta-analysis approaches
• Enabling pathway-level effect size analysis across multiple studies

Innovation

• Uses single-sample enrichment scoring for data aggregation
• Applies meta-analysis to pathway-level enrichment scores
• Preserves effect magnitude and directionality across datasets