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多重检验网:一款为生命科学研究人员设计的多重假设检验校正工具。

MultipleTesting.com: A tool for life science researchers for multiple hypothesis testing correction.

机构信息

Department of Bioinformatics, Semmelweis University, Budapest, Hungary.

Research Centre for Natural Sciences, Cancer Biomarker Research Group, Institute of Enzymology, Budapest, Hungary.

出版信息

PLoS One. 2021 Jun 9;16(6):e0245824. doi: 10.1371/journal.pone.0245824. eCollection 2021.

Abstract

Scientists from nearly all disciplines face the problem of simultaneously evaluating many hypotheses. Conducting multiple comparisons increases the likelihood that a non-negligible proportion of associations will be false positives, clouding real discoveries. Drawing valid conclusions require taking into account the number of performed statistical tests and adjusting the statistical confidence measures. Several strategies exist to overcome the problem of multiple hypothesis testing. We aim to summarize critical statistical concepts and widely used correction approaches while also draw attention to frequently misinterpreted notions of statistical inference. We provide a step-by-step description of each multiple-testing correction method with clear examples and present an easy-to-follow guide for selecting the most suitable correction technique. To facilitate multiple-testing corrections, we developed a fully automated solution not requiring programming skills or the use of a command line. Our registration free online tool is available at www.multipletesting.com and compiles the five most frequently used adjustment tools, including the Bonferroni, the Holm (step-down), the Hochberg (step-up) corrections, allows to calculate False Discovery Rates (FDR) and q-values. The current summary provides a much needed practical synthesis of basic statistical concepts regarding multiple hypothesis testing in a comprehensible language with well-illustrated examples. The web tool will fill the gap for life science researchers by providing a user-friendly substitute for command-line alternatives.

摘要

来自几乎所有学科的科学家都面临着同时评估许多假说的问题。进行多次比较会增加相当比例的关联是假阳性的可能性,从而掩盖真正的发现。得出有效的结论需要考虑进行的统计检验数量,并调整统计置信度度量。有几种策略可以克服多重假设检验的问题。我们旨在总结关键的统计概念和广泛使用的校正方法,同时提请注意经常被误解的统计推断概念。我们提供了每个多重检验校正方法的分步描述,包括清晰的示例,并提供了一个易于遵循的指南,用于选择最合适的校正技术。为了方便多重检验校正,我们开发了一个完全自动化的解决方案,不需要编程技能或使用命令行。我们的免注册在线工具可在 www.multipletesting.com 上使用,它汇集了五个最常用的调整工具,包括 Bonferroni、Holm(逐步下降)、Hochberg(逐步上升)校正,允许计算 False Discovery Rates (FDR) 和 q 值。目前的摘要以易于理解的语言和有充分说明的示例,为多重假设检验的基本统计概念提供了急需的实用综合。该网络工具将为生命科学研究人员填补空白,为命令行替代方案提供用户友好的替代品。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f372/8189492/d63a9e0f79d4/pone.0245824.g001.jpg

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