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Multiple Hypothesis Testing: A Methodological Overview

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Statistical Methods for Microarray Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 972))

Abstract

The process of screening for differentially expressed genes using microarray samples can usually be reduced to a large set of statistical hypothesis tests. In this situation, statistical issues arise which are not encountered in a single hypothesis test, related to the need to identify the specific hypotheses to be rejected, and to report an associated error. As in any complex testing problem, it is rarely the case that a single method is always to be preferred, leaving the analysts with the problem of selecting the most appropriate method for the particular task at hand. In this chapter, an introduction to current multiple testing methodology was presented, with the objective of clarifying the methodological issues involved, and hopefully providing the reader with some basis with which to compare and select methods.

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Author information

Authors and Affiliations

  1. Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA

    Anthony Almudevar

Authors
  1. Anthony Almudevar
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Correspondence to Anthony Almudevar .

Editor information

Editors and Affiliations

  1. School of Medicine & Dentistry, Dept. Biostatistics & Computational, University of Rochester, Elmwood Ave. 601, Rochester, 14642, New York, USA

    Andrei Y. Yakovlev

  2. , Department of Probability and Statistics, Charles University, Sokolovska 83, Prague, 18675, Czech Republic

    Lev Klebanov

  3. State University of New York at Buffalo, Main St - 706 Kimball Tower 3435, Buffalo, 14214, New York, USA

    Daniel Gaile

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Almudevar, A. (2013). Multiple Hypothesis Testing: A Methodological Overview. In: Yakovlev, A., Klebanov, L., Gaile, D. (eds) Statistical Methods for Microarray Data Analysis. Methods in Molecular Biology, vol 972. Humana Press, New York, NY. https://doi.org/10.1007/978-1-60327-337-4_3

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  • DOI: https://doi.org/10.1007/978-1-60327-337-4_3

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-60327-336-7

  • Online ISBN: 978-1-60327-337-4

  • eBook Packages: Springer Protocols

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