Meta-analysis approaches to combine multiple gene set enrichment studies.
Meta-analysis approaches to combine multiple gene set enrichment studies.
复制标题
结合多个基因集富集研究的荟萃分析方法。
DOI：
10.1002/sim.7540
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发表时间：
2018
影响因子：
2
通讯作者：
Gazdar,Adi
中科院分区：
文献类型：
--
作者：
Lu,Wentao;Wang,Xinlei;Zhan,Xiaowei;Gazdar,Adi
在基因集富集分析（GSEA）领域，荟萃分析已被用于整合来自多个研究的信息，以提供个体生物医学研究的可靠总结，并提高检测涉及复杂人类疾病的必要基因集的能力。然而，现有的Meta - Analysis for Pathway Enrichment （MAPE）方法可能会受到功率损失的影响，因为(1)使用总汇总统计来组合组分研究的最终结果，(2)使用富集分数，其分布取决于集合大小。在本文中，我们采用了基于固定效应和随机效应（RE）模型的全基因组关联研究的meta分析方法来整合多个GSEA研究。我们进一步开发了一种混合策略，通过自适应测试来选择RE和FE模型，以实现更高的统计效率和灵活性。此外，提出了基于单侧Kolmogorov - Smirnov统计量的大小调整富集分数，以正式考虑测试多个基因集时不同的集大小。我们的方法往往比MAPE方法有更好的性能，可以应用于离散和连续表型。具体而言，自适应测试方法的性能在一般情况下似乎是最稳定的。
In the field of gene set enrichment analysis (GSEA), meta‐analysis has been used to integrate information from multiple studies to present a reliable summarization of the expanding volume of individual biomedical research, as well as improve the power of detecting essential gene sets involved in complex human diseases. However, existing methods, Meta‐Analysis for Pathway Enrichment (MAPE), may be subject to power loss because of (1) using gross summary statistics for combining end results from component studies and (2) using enrichment scores whose distributions depend on the set sizes. In this paper, we adapt meta‐analysis approaches recently developed for genome‐wide association studies, which are based on fixed effect and random effects (RE) models, to integrate multiple GSEA studies. We further develop a mixed strategy via adaptive testing for choosing RE versus FE models to achieve greater statistical efficiency as well as flexibility. In addition, a size‐adjusted enrichment score based on a one‐sided Kolmogorov‐Smirnov statistic is proposed to formally account for varying set sizes when testing multiple gene sets. Our methods tend to have much better performance than the MAPE methods and can be applied to both discrete and continuous phenotypes. Specifically, the performance of the adaptive testing method seems to be the most stable in general situations.