.png){kind=icon}
Return
Effect Size-Driven Pathway Meta-Analysis for Gene Expression Data
.png){kind=icon}
OA
AI
DOI：10.1109/TCBBIO.2025.3635882.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 meta-analysis 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 single-sample 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.
Keywords:
Data integration
gene expression
meta-analysis
pathway analysis
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
0
Papers:
151
Citations:
0
Organization
Cited Papers
DExMA: An R Package for Performing Gene Expression Meta-Analysis with Missing Genes.png)
Mathematics
OAAI
MathematicsIF0
OAAIShareSaveShareSaveShareSaveOvercoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas dataNATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSave
View more