Quantify and control reproducibility in high-throughput experiments.

Quantify and control reproducibility in high-throughput experiments.
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DOI:
10.1038/s41592-020-00978-4
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发表时间:
2020-12
期刊:
影响因子:
48
通讯作者:
Wen X
Wen X
中科院分区:
生物学1区
文献类型:
--
作者:
Zhao Y;Sampson MG;Wen X

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我们提出了一套计算方法,称为INTRIGUE,以评估和控制高通量实验的重复性。我们的方法建立在可再现性的新定义之上,该定义强调当实验单位用符号效应大小估计进行评估时的方向性一致性(DC)。所提出的方法旨在i)评估多项研究的总体可重复性质量;以及ii)在单个实验单位的水平上评估重复性。我们通过模拟演示了所提出的方法在高通量实验中检测未观察到的批次效应。在转录组相关研究中评估重复性的应用中,我们说明了所提出的方法的多功能性:除了可重复性的质量控制外,它们还适用于研究真正的生物异质性。最后,我们讨论了所提出的可重复性度量的扩展以及在可重复性研究的其他重要领域(例如,发表偏见和概念复制)中的潜在应用。
We propose a set of computational methods, named INTRIGUE, to evaluate and control reproducibility in high-throughput experiments. Our approaches are built upon a novel definition of reproducibility, which emphasizes directional consistency (DC) when experimental units are assessed with signed effect size estimates. The proposed methods are designed to i) assess the overall reproducible quality of multiple studies; and ii) evaluate reproducibility at the level of individual experimental units. We demonstrate the proposed methods in detecting unobserved batch effects in high-throughput experiments via simulations. In an application of assessing reproducibility in transcriptome-wide association studies (TWAS), we illustrate the versatility of the proposed methods: in addition to reproducible quality control, they are also suited for investigating genuine biological heterogeneity. Finally, we discuss the extensions of the proposed reproducibility measures and potential applications in other vital areas of reproducible research (e.g., publication bias and conceptual replications).
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