Computational Analysis of Lifespan Experiment Reproducibility.
Computational Analysis of Lifespan Experiment Reproducibility.
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DOI：
10.3389/fgene.2017.00092
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发表时间：
2017
影响因子：
3.7
通讯作者：
Miller DL
中科院分区：
文献类型：
--
作者：
Petrascheck M;Miller DL
独立的再现性对于科学知识的产生至关重要。优化实验方案以确保重现性是科学工作的一个重要方面。与平均寿命的固有变异性相比，遗传或药理学寿命延长通常很小，即使在相同条件下饲养的等基因群体中也是如此。这种可变性使得小但真实的影响的可重复检测具有实验挑战性。在这项研究中，我们的目的是确定C。在没有方法学错误或环境或遗传背景影响的情况下，在理想条件下测量线虫的寿命。为了实现这一点，我们生成了C的参数化模型。根据从5，026只野生型N2动物收集的数据，计算了线虫的寿命。我们使用这个模型来预测不同的实验实践、效应量、动物数量以及不同的生存曲线“形状”如何影响重现真实的寿命效应的能力。我们发现，重现真实的但效果很小的可能性非常低，需要比通常使用的更多的动物。我们的研究结果表明，许多寿命研究的动力不足，以检测报告的变化，因此，随机变化本身可以解释许多失败的重现寿命的结果。作为补救措施，我们提供了检测功效表，可用作指导方针，以计划具有统计功效的实验，从而可靠地检测寿命中的真实的变化，并限制虚假的假阳性结果。这些考虑因素将改善设计寿命实验的最佳实践，以提高重现性。
Independent reproducibility is essential to the generation of scientific knowledge. Optimizing experimental protocols to ensure reproducibility is an important aspect of scientific work. Genetic or pharmacological lifespan extensions are generally small compared to the inherent variability in mean lifespan even in isogenic populations housed under identical conditions. This variability makes reproducible detection of small but real effects experimentally challenging. In this study, we aimed to determine the reproducibility of C. elegans lifespan measurements under ideal conditions, in the absence of methodological errors or environmental or genetic background influences. To accomplish this, we generated a parametric model of C. elegans lifespan based on data collected from 5,026 wild-type N2 animals. We use this model to predict how different experimental practices, effect sizes, number of animals, and how different “shapes” of survival curves affect the ability to reproduce real longevity effects. We find that the chances of reproducing real but small effects are exceedingly low and would require substantially more animals than are commonly used. Our results indicate that many lifespan studies are underpowered to detect reported changes and that, as a consequence, stochastic variation alone can account for many failures to reproduce longevity results. As a remedy, we provide power of detection tables that can be used as guidelines to plan experiments with statistical power to reliably detect real changes in lifespan and limit spurious false positive results. These considerations will improve best-practices in designing lifespan experiment to increase reproducibility.