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      Bon-EV: an improved multiple testing procedure for controlling false discovery rates

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          Abstract

          Background

          Stability of multiple testing procedures, defined as the standard deviation of total number of discoveries, can be used as an indicator of variability of multiple testing procedures. Improving stability of multiple testing procedures can help to increase the consistency of findings from replicated experiments. Benjamini-Hochberg’s and Storey’s q-value procedures are two commonly used multiple testing procedures for controlling false discoveries in genomic studies. Storey’s q-value procedure has higher power and lower stability than Benjamini-Hochberg’s procedure. To improve upon the stability of Storey’s q-value procedure and maintain its high power in genomic data analysis, we propose a new multiple testing procedure, named Bon-EV, to control false discovery rate (FDR) based on Bonferroni’s approach.

          Results

          Simulation studies show that our proposed Bon-EV procedure can maintain the high power of the Storey’s q-value procedure and also result in better FDR control and higher stability than Storey’s q-value procedure for samples of large size(30 in each group) and medium size (15 in each group) for either independent, somewhat correlated, or highly correlated test statistics. When sample size is small (5 in each group), our proposed Bon-EV procedure has performance between the Benjamini-Hochberg procedure and the Storey’s q-value procedure. Examples using RNA-Seq data show that the Bon-EV procedure has higher stability than the Storey’s q-value procedure while maintaining equivalent power, and higher power than the Benjamini-Hochberg’s procedure.

          Conclusions

          For medium or large sample sizes, the Bon-EV procedure has improved FDR control and stability compared with the Storey’s q-value procedure and improved power compared with the Benjamini-Hochberg procedure. The Bon-EV multiple testing procedure is available as the BonEV package in R for download at https://CRAN.R-project.org/package=BonEV.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12859-016-1414-x) contains supplementary material, which is available to authorized users.

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          Most cited references11

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics

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              Six red flags for suspect work.

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                Author and article information

                Contributors
                dongmei_li@urmc.rochester.edu
                xzd482@yahoo.com
                Martin_Zand@URMC.Rochester.edu
                Thomas_Fogg@URMC.Rochester.edu
                Tim_Dye@URMC.Rochester.edu
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                3 January 2017
                3 January 2017
                2017
                : 18
                : 1
                Affiliations
                [1 ]Clinical and Translational Science Institute, School of Medicine and Dentistry, University of Rochester, 265 Crittenden Boulevard CU 420708, Rochester, 14642 NY USA
                [2 ]Goergen Institute for Data Science, University of Rochester, Computer Studies Building, Rochester, 14642 NY USA
                [3 ]Department of Obstetrics and Gynecology, University of Rochester, 500 Red Creek Drive Suite 220, Rochester, 14623 NY USA
                Author information
                http://orcid.org/0000-0001-9140-2483
                Article
                1414
                10.1186/s12859-016-1414-x
                5210267
                28049414
                f767302b-ea6b-460a-b9bd-d020afc8afb7
                © The Author(s) 2017

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 20 July 2016
                : 7 December 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100006108, National Center for Advancing Translational Sciences;
                Award ID: UL1 TR000042
                Funded by: FundRef http://dx.doi.org/10.13039/100000060, National Institute of Allergy and Infectious Diseases;
                Award ID: AI098112
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                Bioinformatics & Computational biology
                multiple testing procedure,fdr,power,stability,rna-seq
                Bioinformatics & Computational biology
                multiple testing procedure, fdr, power, stability, rna-seq

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