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A Super Scalable Algorithm for Short Segment Detection

Author

Listed:
  • Ning Hao

    (University of Arizona)

  • Yue Selena Niu

    (University of Arizona)

  • Feifei Xiao

    (University of South Carolina)

  • Heping Zhang

    (Yale School of Public Health)

Abstract

In many applications such as copy number variant (CNV) detection, the goal is to identify short segments on which the observations have different means or medians from the background. Those segments are usually short and hidden in a long sequence and hence are very challenging to find. We study a super scalable short segment (4S) detection algorithm in this paper. This nonparametric method clusters the locations where the observations exceed a threshold for segment detection. It is computationally efficient and does not rely on Gaussian noise assumption. Moreover, we develop a framework to assign significance levels for detected segments. We demonstrate the advantages of our proposed method by theoretical, simulation, and real data studies.

Suggested Citation

  • Ning Hao & Yue Selena Niu & Feifei Xiao & Heping Zhang, 2021. "A Super Scalable Algorithm for Short Segment Detection," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 18-33, April.
    • Handle: RePEc:spr:stabio:v:13:y:2021:i:1:d:10.1007_s12561-020-09278-z
    DOI: 10.1007/s12561-020-09278-z
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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.
    2. T. Tony Cai & X. Jessie Jeng & Hongzhe Li, 2012. "Robust detection and identification of sparse segments in ultrahigh dimensional data analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(5), pages 773-797, November.
    3. Klaus Frick & Axel Munk & Hannes Sieling, 2014. "Multiscale change point inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(3), pages 495-580, June.
    4. Fryzlewicz, Piotr, 2014. "Wild binary segmentation for multiple change-point detection," LSE Research Online Documents on Economics 57146, London School of Economics and Political Science, LSE Library.
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