标题： 基于效应量的基因表达数据通路元分析 显示英文标题

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

摘要： 公共存储库中组学数据集的激增为生物医学研究创造了前所未有的机遇，但也对其整合带来了重大挑战，特别是由于缺失基因和平台特异性差异。 传统的基因表达元分析通常关注单个基因，在不同研究中存在缺失基因时会导致数据丢失并限制生物学见解。 为解决这些局限性，我们提出了GSEMA（基因集富集元分析），这是一种新方法，利用单样本富集评分将基因表达数据汇总为通路级矩阵。 通过将元分析技术应用于富集分数，GSEMA保留了效应的大小和方向性，使得可以在数据集中定义通路活性。 使用模拟数据和系统性红斑狼疮（SLE）和帕金森病（PD）的案例研究，我们证明GSEMA在控制假阳性率方面优于其他方法，同时提供有意义的生物学解释。 GSEMA方法作为R包实现，可在CRAN存储库中获得。 显示英文摘要

摘要： 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