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

被引:103
作者
Menyhart, Otilia [1 ,2 ]
Weltz, Boglarka [2 ,3 ]
Gyorffy, Balazs [1 ,2 ,4 ]
机构
[1] Semmelweis Univ, Dept Bioinformat, Budapest, Hungary
[2] Inst Enzymol, Canc Biomarker Res Grp, Res Ctr Nat Sci, Budapest, Hungary
[3] A5 Genet Ltd, Und, Hungary
[4] Semmelweis Univ, Dept Pediat 2, Budapest, Hungary
关键词
FALSE DISCOVERY RATE; BONFERRONI; PROPORTION;
D O I
10.1371/journal.pone.0245824
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:12
相关论文
共 49 条
[41]   False discovery rates: a new deal [J].
Stephens, Matthew .
BIOSTATISTICS, 2017, 18 (02) :275-294
[42]   The positive false discovery rate:: A Bayesian interpretation and the q-value [J].
Storey, JD .
ANNALS OF STATISTICS, 2003, 31 (06) :2013-2035
[43]   A direct approach to false discovery rates [J].
Storey, JD .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2002, 64 :479-498
[44]   Statistical significance for genomewide studies [J].
Storey, JD ;
Tibshirani, R .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2003, 100 (16) :9440-9445
[45]  
Storey JD BA., 2020, QVALUE Q VALUE ESTIM
[46]   Advances in p-Value Based Multiple Test Procedures [J].
Tamhane, Ajit C. ;
Gou, Jiangtao .
JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 2018, 28 (01) :10-27
[47]   Implementing false discovery rate control: increasing your power [J].
Verhoeven, KJF ;
Simonsen, KL ;
McIntyre, LM .
OIKOS, 2005, 108 (03) :643-647
[48]   A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk [J].
Yurko, Ronald ;
G'Sell, Max ;
Roeder, Kathryn ;
Devlin, Bernie .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2020, 117 (26) :15028-15035
[49]   Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing [J].
Zhang, Martin J. ;
Xia, Fei ;
Zou, James .
NATURE COMMUNICATIONS, 2019, 10 (1)