Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis.
Random-effects meta-analysis of effect sizes as a unified framework for gene set analysis.
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DOI：
10.1371/journal.pcbi.1010278
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发表时间：
2022-10
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
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基因集分析 (GSA) 仍然是基因组规模研究中的常见步骤，因为它可以揭示从单个基因获得的结果中不明显的见解。 GSA应用了许多不同的计算工具，可能对不同类型的信号敏感；然而，大多数方法隐含地测试了感兴趣的基因组中的基因之间某些实验条件的影响分布是否存在差异。我们开发了一个统一的 GSA 框架，首先拟合效应大小分布，然后测试基因集之间这些分布的差异。这些差异可能在于受到干扰的基因的比例，或者影响的符号或大小。受统计荟萃分析的启发，我们通过减少具有较大不确定性的基因对分布参数估计的影响来考虑效应大小估计的不确定性。我们通过模拟和实际数据的应用证明，与现有方法相比，这种方法在性能方面提供了显着的提升。此外，所进行的统计测试是根据效应大小来定义的，而不是根据先前测量这些变化的统计测试的结果来定义的，这会提高可解释性并提高对样本量变化的鲁棒性。基因集分析的作用是识别基因组学实验中受到干扰的基因组。有许多工具可用于此任务，但它们并不都测试相同类型的更改。在这里，我们提出了一种进行基因集分析的新方法，首先计算出基因集中群体效应的分布，然后将该分布与其他基因中的等效分布进行比较。通过现有的基因集分析工具进行的测试可以与这些群体效应分布的不同比较相关。基因集分析的统一框架提供了更明确的零假设，用于测试基因集对实验条件的不同类型的反应。这些结果更容易解释，因为可以直观地比较群体效应分布，从而表明基因组之间的实验效果有何不同。
Gene set analysis (GSA) remains a common step in genome-scale studies because it can reveal insights that are not apparent from results obtained for individual genes. Many different computational tools are applied for GSA, which may be sensitive to different types of signals; however, most methods implicitly test whether there are differences in the distribution of the effect of some experimental condition between genes in gene sets of interest. We have developed a unifying framework for GSA that first fits effect size distributions, and then tests for differences in these distributions between gene sets. These differences can be in the proportions of genes that are perturbed or in the sign or size of the effects. Inspired by statistical meta-analysis, we take into account the uncertainty in effect size estimates by reducing the influence of genes with greater uncertainty on the estimation of distribution parameters. We demonstrate, using simulation and by application to real data, that this approach provides significant gains in performance over existing methods. Furthermore, the statistical tests carried out are defined in terms of effect sizes, rather than the results of prior statistical tests measuring these changes, which leads to improved interpretability and greater robustness to variation in sample sizes. The role of gene set analysis is to identify groups of genes that are perturbed in a genomics experiment. There are many tools available for this task and they do not all test for the same types of changes. Here we propose a new way to carry out gene set analysis that involves first working out the distribution of the group effect in the gene set and then comparing this distribution to the equivalent distribution in other genes. Tests performed by existing tools for gene set analysis can be related to different comparisons in these distributions of group effects. A unified framework for gene set analysis provides for more explicit null hypotheses against which to test sets of genes for different types of responses to the experimental conditions. These results are more interpretable, because the group effect distributions can be compared visually, providing an indication of how the experimental effect differs between the gene sets.
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影响因子：
3
作者：
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通讯作者：
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影响因子：
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Kusalik, Anthony J.
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