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False discovery rates and copy number variation

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  • Bradley Efron
  • Nancy R. Zhang

Abstract

Copy number changes, the gains and losses of chromosome segments, are a common type of genetic variation among healthy individuals as well as an important feature in tumour genomes. Microarray technology enables us to simultaneously measure, with moderate accuracy, copy number variation at more than a million chromosome locations and for hundreds of subjects. This leads to massive data sets and complicated inference problems concerning which locations are more likely to vary. In this paper we consider a relatively simple false discovery rate approach to copy number analysis. More careful parametric change-point methods can then be focused on promising regions of the genome. Copyright 2011, Oxford University Press.

Suggested Citation

  • Bradley Efron & Nancy R. Zhang, 2011. "False discovery rates and copy number variation," Biometrika, Biometrika Trust, vol. 98(2), pages 251-271.
    • Handle: RePEc:oup:biomet:v:98:y:2011:i:2:p:251-271
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    File URL: http://hdl.handle.net/10.1093/biomet/asr018
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    Cited by:

    1. Wang Chamont & Gevertz Jana L., 2016. "Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 321-347, August.
    2. 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.
    3. Cai, Qingyun, 2018. "A scoring criterion for rejection of clustered p-values," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 180-189.
    4. 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.

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