IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v11y2012i1n11.html
   My bibliography  Save this article

The Inheritance Procedure: Multiple Testing of Tree-structured Hypotheses

Author

Listed:
  • Goeman Jelle J.
  • Finos Livio

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/1544-6115.1554
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/1544-6115.1554?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Anders Bredahl Kock & David Preinerstorfer, 2021. "Superconsistency of Tests in High Dimensions," Papers 2106.03700, arXiv.org, revised Jan 2022.
    4. T. Tony Cai & Wenguang Sun, 2017. "Optimal screening and discovery of sparse signals with applications to multistage high throughput studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 197-223, January.
    5. Yoav Benjamini, 2010. "Discovering the false discovery rate," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 405-416, September.
    6. Rina Foygel Barber & Aaditya Ramdas, 2017. "The p-filter: multilayer false discovery rate control for grouped hypotheses," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1247-1268, September.
    7. Wang Xiaoming & Dinu Irina & Liu Wei & Yasui Yutaka, 2011. "Linear Combination Test for Hierarchical Gene Set Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-18, March.
    8. 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.
    9. Steven Phillips & Yuji Takeda & Archana Singh, 2012. "Visual Feature Integration Indicated by pHase-Locked Frontal-Parietal EEG Signals," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-9, March.
    10. Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 59-67, March.
    11. Qingyun Cai & Hock Peng Chan, 2017. "A Double Application of the Benjamini-Hochberg Procedure for Testing Batched Hypotheses," Methodology and Computing in Applied Probability, Springer, vol. 19(2), pages 429-443, June.
    12. Ferreira José A. & Berkhof Johannes & Souverein Olga & Zwinderman Koos, 2009. "A Multiple Testing Approach to High-Dimensional Association Studies with an Application to the Detection of Associations between Risk Factors of Heart Disease and Genetic Polymorphisms," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-58, January.
    13. Ting Wang & Jiancang Zhuang & Kazushige Obara & Hiroshi Tsuruoka, 2017. "Hidden Markov modelling of sparse time series from non-volcanic tremor observations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 691-715, August.
    14. Patrick K. Kimes & Yufeng Liu & David Neil Hayes & James Stephen Marron, 2017. "Statistical significance for hierarchical clustering," Biometrics, The International Biometric Society, vol. 73(3), pages 811-821, September.
    15. 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.
    16. Chang, Chiu-Lan & Cai, Qingyun, 2023. "Stock return anomalies identification during the Covid-19 with the application of a grouped multiple comparison procedure," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 168-183.
    17. Newton Michael A. & He Qiuling & Kendziorski Christina, 2012. "A Model-Based Analysis to Infer the Functional Content of a Gene List," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-27, January.
    18. 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.
    19. 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.
    20. Paulo C. Rodrigues & Vanda M. Lourenço, 2020. "Comments on: Hierarchical Inference for genome-wide association studies: a view on methodology with software by Paulo C. Rodrigues and Vanda M. Lourenço," Computational Statistics, Springer, vol. 35(1), pages 57-58, March.

    More about this item

    Keywords

    hierarchical testing; sequential testing;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sagmbi:v:11:y:2012:i:1:n:11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.