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A region-based multiple testing method for hypotheses ordered in space or time

Author

Listed:
  • Meijer Rosa J.

    (Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Postzone S5-P, P.O. Box 9604, 2300 RC Leiden, The Netherlands)

  • Krebs Thijmen J.P.

    (Faculty of Electrical Engineering, Mathematics and Information Technology, Delft University, P.O. Box 5031, 2600 GA Delft, The Netherlands)

  • Goeman Jelle J.

    (Section Biostatistics, Department for Health Evidence, Radboud University Medical Center, Postzone 133, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands)

Abstract

We present a multiple testing method for hypotheses that are ordered in space or time. Given such hypotheses, the elementary hypotheses as well as regions of consecutive hypotheses are of interest. These region hypotheses not only have intrinsic meaning but testing them also has the advantage that (potentially small) signals across a region are combined in one test. Because the expected number and length of potentially interesting regions are usually not available beforehand, we propose a method that tests all possible region hypotheses as well as all individual hypotheses in a single multiple testing procedure that controls the familywise error rate. We start at testing the global null-hypothesis and when this hypothesis can be rejected we continue with further specifying the exact location/locations of the effect present. The method is implemented in the R package cherry and is illustrated on a DNA copy number data set.

Suggested Citation

  • Meijer Rosa J. & Krebs Thijmen J.P. & Goeman Jelle J., 2015. "A region-based multiple testing method for hypotheses ordered in space or time," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(1), pages 1-19, February.
    • Handle: RePEc:bpj:sagmbi:v:14:y:2015:i:1:p:1-19:n:1
    DOI: 10.1515/sagmb-2013-0075
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    References listed on IDEAS

    as
    1. Goeman Jelle J. & Finos Livio, 2012. "The Inheritance Procedure: Multiple Testing of Tree-structured Hypotheses," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-18, January.
    2. Benjamini, Yoav & Heller, Ruth, 2007. "False Discovery Rates for Spatial Signals," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1272-1281, December.
    3. M. Perone Pacifico & C. Genovese & I. Verdinelli & L. Wasserman, 2004. "False Discovery Control for Random Fields," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1002-1014, December.
    4. Nicolai Meinshausen, 2008. "Hierarchical testing of variable importance," Biometrika, Biometrika Trust, vol. 95(2), pages 265-278.
    5. Westfall, Peter H. & Tobias, Randall D., 2007. "Multiple Testing of General Contrasts: Truncated Closure and the Extended ShafferRoyen Method," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 487-494, June.
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    Cited by:

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    2. Wiuf Carsten & Schaumburg-Müller Pallesen Jonatan & Foldager Leslie & Grove Jakob, 2016. "LandScape: a simple method to aggregate p-values and other stochastic variables without a priori grouping," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 349-361, August.
    3. Guillermo Durand & Gilles Blanchard & Pierre Neuvial & Etienne Roquain, 2020. "Post hoc false positive control for structured hypotheses," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1114-1148, December.

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