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Robust Regression Analysis of Copy Number Variation Data based on a Univariate Score

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  • Glen A Satten
  • Andrew S Allen
  • Morna Ikeda
  • Jennifer G Mulle
  • Stephen T Warren

Abstract

Motivation: The discovery that copy number variants (CNVs) are widespread in the human genome has motivated development of numerous algorithms that attempt to detect CNVs from intensity data. However, all approaches are plagued by high false discovery rates. Further, because CNVs are characterized by two dimensions (length and intensity) it is unclear how to order called CNVs to prioritize experimental validation. Results: We developed a univariate score that correlates with the likelihood that a CNV is true. This score can be used to order CNV calls in such a way that calls having larger scores are more likely to overlap a true CNV. We developed cnv.beast, a computationally efficient algorithm for calling CNVs that uses robust backward elimination regression to keep CNV calls with scores that exceed a user-defined threshold. Using an independent dataset that was measured using a different platform, we validated our score and showed that our approach performed better than six other currently-available methods. Availability: cnv.beast is available at http://www.duke.edu/~asallen/Software.html.

Suggested Citation

  • Glen A Satten & Andrew S Allen & Morna Ikeda & Jennifer G Mulle & Stephen T Warren, 2014. "Robust Regression Analysis of Copy Number Variation Data based on a Univariate Score," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-8, February.
    • Handle: RePEc:plo:pone00:0086272
    DOI: 10.1371/journal.pone.0086272
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    References listed on IDEAS

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    1. Jeng, X. Jessie & Cai, T. Tony & Li, Hongzhe, 2010. "Optimal Sparse Segment Identification With Application in Copy Number Variation Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1156-1166.
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    Cited by:

    1. Ekele Alih & Hong Choon Ong, 2015. "Cluster-based multivariate outlier identification and re-weighted regression in linear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 938-955, May.

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