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The Inheritance Procedure: Multiple Testing of Tree-structured Hypotheses

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  • Goeman Jelle J.

    (Leiden University Medical Center)

  • Finos Livio

    (University of Padua)

Abstract

Hypotheses tests in bioinformatics can often be set in a tree structure in a very natural way, e.g. when tests are performed at probe, gene, and chromosome level. Exploiting this graph structure in a multiple testing procedure may result in a gain in power or increased interpretability of the results.We present the inheritance procedure, a method of familywise error control for hypotheses structured in a tree. The method starts testing at the top of the tree, following up on those branches in which it finds significant results, and following up on leaf nodes in the neighborhood of those leaves. The method is a uniform improvement over a recently proposed method by Meinshausen. The inheritance procedure has been implemented in the globaltest package which is available on www.bioconductor.org.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:1:n:11
    DOI: 10.1515/1544-6115.1554
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    References listed on IDEAS

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    1. Liang, Kun & Nettleton, Dan, 2010. "A Hidden Markov Model Approach to Testing Multiple Hypotheses on a Tree-Transformed Gene Ontology Graph," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1444-1454.
    2. Kim Kyung In & Roquain Etienne & van de Wiel Mark A, 2010. "Spatial Clustering of Array CGH Features in Combination with Hierarchical Multiple Testing," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-25, November.
    3. Nicolai Meinshausen, 2008. "Hierarchical testing of variable importance," Biometrika, Biometrika Trust, vol. 95(2), pages 265-278.
    4. Yekutieli, Daniel, 2008. "Hierarchical False Discovery RateControlling Methodology," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 309-316, March.
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    Cited by:

    1. Kwonsang Lee & Dylan S. Small & Paul R. Rosenbaum, 2018. "A powerful approach to the study of moderate effect modification in observational studies," Biometrics, The International Biometric Society, vol. 74(4), pages 1161-1170, December.
    2. Gilles R. Ducharme & Walid Al Akhras, 2016. "Tree based diagnostic procedures following a smooth test of goodness-of-fit," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(8), pages 971-989, November.
    3. 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.
    4. Antoine Bichat & Christophe Ambroise & Mahendra Mariadassou, 2022. "Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process," Computational Statistics, Springer, vol. 37(3), pages 995-1013, July.
    5. Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 1-40, March.

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