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Approaches to multiplicity issues in complex research in microarray analysis

Author

Listed:
  • Daniel Yekutieli
  • Anat Reiner‐Benaim
  • Yoav Benjamini
  • Gregory I. Elmer
  • Neri Kafkafi
  • Noah E. Letwin
  • Norman H. Lee

Abstract

The multiplicity problem is evident in the simplest form of statistical analysis of gene expression data – the identification of differentially expressed genes. In more complex analysis, the problem is compounded by the multiplicity of hypotheses per gene. Thus, in some cases, it may be necessary to consider testing millions of hypotheses. We present three general approaches for addressing multiplicity in large research problems. (a) Use the scalability of false discovery rate (FDR) controlling procedures; (b) apply FDR‐controlling procedures to a selected subset of hypotheses; (c) apply hierarchical FDR‐controlling procedures. We also offer a general framework for ensuring reproducible results in complex research, where a researcher faces more than just one large research problem. We demonstrate these approaches by analyzing the results of a complex experiment involving the study of gene expression levels in different brain regions across multiple mouse strains.

Suggested Citation

  • Daniel Yekutieli & Anat Reiner‐Benaim & Yoav Benjamini & Gregory I. Elmer & Neri Kafkafi & Noah E. Letwin & Norman H. Lee, 2006. "Approaches to multiplicity issues in complex research in microarray analysis," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 60(4), pages 414-437, November.
  • Handle: RePEc:bla:stanee:v:60:y:2006:i:4:p:414-437
    DOI: 10.1111/j.1467-9574.2006.00343.x
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    Cited by:

    1. 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 1-56, January.
    2. Rubin, Mark, 2021. "When to adjust alpha during multiple testing: A consideration of disjunction, conjunction, and individual testing," MetaArXiv tj6pm, Center for Open Science.
    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. Anat Reiner-Benaim, 2016. "Scan Statistic Tail Probability Assessment Based on Process Covariance and Window Size," Methodology and Computing in Applied Probability, Springer, vol. 18(3), pages 717-745, September.

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