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Genomic Outlier Detection in High-Throughput Data Analysis

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Book cover Statistical Methods for Microarray Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 972))

Abstract

In the analysis of high-throughput data, a very common goal is the detection of genes or of differential expression between two groups or classes. A recent finding from the scientific literature in prostate cancer demonstrates that by searching for a different pattern of differential expression, new candidate oncogenes might be found. In this chapter, we discuss the statistical problem, termed oncogene outlier detection, and discuss a variety of proposals to this problem. A statistical model in the multiclass situation is described; links with multiple testing concepts are established. Some new nonparametric procedures are described and compared to existing methods using simulation studies.

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Acknowledgments

The author would like to acknowledge the support of the Huck Institutes of Life Sciences at Penn State University and NIH Grant R01GM72007.

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Authors and Affiliations

  1. Departments of Statistics and Public Health Sciences, Penn State University, DuBios, PA, USA

    Debashis Ghosh

Authors
  1. Debashis Ghosh
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Corresponding author

Correspondence to Debashis Ghosh .

Editor information

Editors and Affiliations

  1. School of Medicine & Dentistry, Dept. Biostatistics & Computational, University of Rochester, Elmwood Ave. 601, Rochester, 14642, New York, USA

    Andrei Y. Yakovlev

  2. , Department of Probability and Statistics, Charles University, Sokolovska 83, Prague, 18675, Czech Republic

    Lev Klebanov

  3. State University of New York at Buffalo, Main St - 706 Kimball Tower 3435, Buffalo, 14214, New York, USA

    Daniel Gaile

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Ghosh, D. (2013). Genomic Outlier Detection in High-Throughput Data Analysis. In: Yakovlev, A., Klebanov, L., Gaile, D. (eds) Statistical Methods for Microarray Data Analysis. Methods in Molecular Biology, vol 972. Humana Press, New York, NY. https://doi.org/10.1007/978-1-60327-337-4_9

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  • DOI: https://doi.org/10.1007/978-1-60327-337-4_9

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-60327-336-7

  • Online ISBN: 978-1-60327-337-4

  • eBook Packages: Springer Protocols

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