In this paper, we introduce Gridsemble, a data-driven selective ensembling algorithm for estimating local false discovery rates (fdr) in large-scale multiple hypothesis testing. Existing methods for estimating fdr often yield different conclusions, yet the unobservable nature of fdr values prevents the use of traditional model selection. There is limited guidance on choosing a method for a given dataset, making this an arbitrary decision in practice. Gridsemble circumvents this challenge by ensembling a subset of methods with weights based on their estimated performances, which are computed on synthetic datasets generated to mimic the observed data while including ground truth. We demonstrate through simulation studies and an experimental application that this method outperforms three popular R software packages with their default parameter values$\unicode{x2014}$common choices given the current landscape. While our applications are in the context of high throughput transcriptomics, we emphasize that Gridsemble is applicable to any use of large-scale multiple hypothesis testing, an approach that is utilized in many fields. We believe that Gridsemble will be a useful tool for computing reliable estimates of fdr and for improving replicability in the presence of multiple hypotheses by eliminating the need for an arbitrary choice of method. Gridsemble is implemented in an open-source R software package available on GitHub at jennalandy/gridsemblefdr.

翻译：本文提出Gridsemble——一种数据驱动的选择性集成算法，用于大规模多重假设检验中的局部错误发现率（fdr）估计。现有fdr估计方法常得出不同结论，但由于fdr值不可观测，传统模型选择方法无法适用。针对给定数据集的方法选择缺乏指导，实践中常沦为随意决策。Gridsemble通过集成一组子方法，依据其在模拟数据（模仿观测数据并包含真实标签）上计算的估计性能分配权重，有效规避了这一难题。模拟研究与实验应用表明，本方法在默认参数设置下（当前研究格局中的常见选择）优于三种主流R软件包。尽管本文应用场景为高通量转录组学，但需强调Gridsemble可适用于任何使用大规模多重假设检验的领域（该方法已被众多学科采用）。我们相信Gridsemble能通过消除方法选择的随意性，为fdr提供可靠估计并提升多重假设情境下的可重复性。该算法已在开源R软件包中实现，代码托管于GitHub仓库jennalandy/gridsemblefdr。