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      Data-driven hypothesis weighting increases detection power in genome-scale multiple testing

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      Publication date (Electronic):
      Journal: Nature methods

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          Abstract

          Hypothesis weighting improves the power of large-scale multiple testing. We describe a method that uses covariates independent of the p-values under the null hypothesis, but informative of each test’s power or prior probability of the null hypothesis. Independent hypothesis weighting (IHW) increases power while controlling the false discovery rate (FDR). IHW is a practical approach to discover associations in large datasets as encountered in genomics and high-throughput biology. Availability: www.bioconductor.org/packages/IHW

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

          Yoav Benjamini, Yosef Hochberg (1995)
          Article 0 comments Cited 23767 times – based on 0 reviews     Review now
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            Independent filtering increases detection power for high-throughput experiments.

            Richard Bourgon, Robert C Gentleman, Wolfgang Huber (2010)
            With high-dimensional data, variable-by-variable statistical testing is often used to select variables whose behavior differs across conditions. Such an approach requires adjustment for multiple testing, which can result in low statistical power. A two-stage approach that first filters variables by a criterion independent of the test statistic, and then only tests variables which pass the filter, can provide higher power. We show that use of some filter/test statistics pairs presented in the literature may, however, lead to loss of type I error control. We describe other pairs which avoid this problem. In an application to microarray data, we found that gene-by-gene filtering by overall variance followed by a t-test increased the number of discoveries by 50%. We also show that this particular statistic pair induces a lower bound on fold-change among the set of discoveries. Independent filtering-using filter/test pairs that are independent under the null hypothesis but correlated under the alternative-is a general approach that can substantially increase the efficiency of experiments.
            Article 0 comments Cited 326 times – based on 0 reviews     Review now
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              Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach

              John D. Storey, Jonathan Taylor, David Siegmund (2004)
              Article 0 comments Cited 299 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 101215604
                Journal ID (pubmed-jr-id): 32338
                Journal ID (nlm-ta): Nat Methods
                Journal ID (iso-abbrev): Nat. Methods
                Title: Nature methods
                ISSN (Print): 1548-7091
                ISSN (Electronic): 1548-7105
                Publication date Nihms-submitted: 8 June 2016
                Publication date (Electronic): 30 May 2016
                Publication date (Print): July 2016
                Publication date PMC-release: 30 November 2016
                Volume: 13
                Issue: 7
                Pages: 577-580
                Affiliations
                European Molecular Biology Laboratory, Heidelberg, Germany
                Author notes
                [1 ]Corresponding Author: whuber@ 123456embl.de
                Article
                Manuscript ID: EMS68345
                DOI: 10.1038/nmeth.3885
                PMC ID: 4930141
                PubMed ID: 27240256
                SO-VID: 6f41d9a3-cd1b-40b7-891f-74a2acf9082c
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                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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                ScienceOpen disciplines: Life sciences

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