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深思熟虑的实验设计如何在组学时代助力生物学家。

How thoughtful experimental design can empower biologists in the omics era.

作者信息

Wagner Maggie R, Kleiner Manuel

机构信息

Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, KS, USA.

Kansas Biological Survey & Center for Ecological Research, Lawrence, KS, USA.

出版信息

Nat Commun. 2025 Aug 6;16(1):7263. doi: 10.1038/s41467-025-62616-x.

DOI:10.1038/s41467-025-62616-x
PMID:40769965
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12328601/
Abstract

The modern biology toolbox continues to evolve, as cutting-edge molecular techniques complement some classic approaches and replace others. However, statistical literacy and experimental design remain critical to the success of any empirical research, regardless of which methods are used to collect data. This Perspective highlights common experimental design pitfalls and explains how to avoid them. We discuss principles of experimental design that are relevant for all biology research, along with special considerations for projects using -omics approaches. Established best practices for optimizing sample size, randomizing treatments, including positive and negative controls, and reducing noise (e.g., blocking and pooling) can empower researchers to conduct experiments that become useful contributions to the scientific record, even if they generate negative results. They also reduce the risk of introducing bias, drawing incorrect conclusions, or wasting effort and resources on experiments with low chances of success. Although experimental design strategies are often covered in undergraduate- and graduate-level courses and in textbooks, here we provide a succinct overview and highlight their relevance to modern biology research. This Perspective can be used in training of early-career scientists and as a refresher for seasoned scientists.

摘要

随着前沿分子技术补充了一些经典方法并取代了其他方法,现代生物学工具箱不断发展。然而,统计素养和实验设计对于任何实证研究的成功仍然至关重要,无论使用何种方法收集数据。这篇观点文章强调了常见的实验设计陷阱,并解释了如何避免这些陷阱。我们讨论了与所有生物学研究相关的实验设计原则,以及使用组学方法的项目的特殊注意事项。优化样本量、随机分配处理、设置阳性和阴性对照以及减少噪声(例如区组设计和合并)的既定最佳实践,即使实验产生负面结果,也能使研究人员进行对科学记录有价值贡献的实验。它们还降低了引入偏差、得出错误结论或在成功几率低的实验上浪费精力和资源的风险。尽管实验设计策略通常在本科和研究生课程以及教科书中有所涵盖,但在这里我们提供一个简要概述,并强调它们与现代生物学研究的相关性。这篇观点文章可用于早期职业科学家的培训,并作为经验丰富的科学家的复习资料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bff/12328601/0ccfba67a774/41467_2025_62616_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bff/12328601/dbc87f786ace/41467_2025_62616_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bff/12328601/c8d432669d55/41467_2025_62616_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bff/12328601/0ccfba67a774/41467_2025_62616_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bff/12328601/dbc87f786ace/41467_2025_62616_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bff/12328601/c8d432669d55/41467_2025_62616_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bff/12328601/0ccfba67a774/41467_2025_62616_Fig3_HTML.jpg

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