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

TL;DR

This paper introduces GSEMA, a pathway-based meta-analysis method for gene expression data that improves integration across studies by focusing on pathway activity rather than individual genes, enhancing biological insights.

Contribution

GSEMA leverages single-sample enrichment scores for pathway-level meta-analysis, addressing missing gene issues and platform discrepancies in gene expression studies.

Findings

GSEMA outperforms existing methods in controlling false positives.

GSEMA provides meaningful biological interpretations in case studies.

GSEMA is available as an R package on CRAN.

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