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Post‐selection inference for changepoint detection algorithms with application to copy number variation data

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  • Sangwon Hyun
  • Kevin Z. Lin
  • Max G'Sell
  • Ryan J. Tibshirani

Abstract

Changepoint detection methods are used in many areas of science and engineering, for example, in the analysis of copy number variation data to detect abnormalities in copy numbers along the genome. Despite the broad array of available tools, methodology for quantifying our uncertainty in the strength (or the presence) of given changepoints post‐selection are lacking. Post‐selection inference offers a framework to fill this gap, but the most straightforward application of these methods results in low‐powered hypothesis tests and leaves open several important questions about practical usability. In this work, we carefully tailor post‐selection inference methods toward changepoint detection, focusing on copy number variation data. To accomplish this, we study commonly used changepoint algorithms: binary segmentation, as well as two of its most popular variants, wild and circular, and the fused lasso. We implement some of the latest developments in post‐selection inference theory, mainly auxiliary randomization. This improves the power, which requires implementations of Markov chain Monte Carlo algorithms (importance sampling and hit‐and‐run sampling) to carry out our tests. We also provide recommendations for improving practical useability, detailed simulations, and example analyses on array comparative genomic hybridization as well as sequencing data.

Suggested Citation

  • Sangwon Hyun & Kevin Z. Lin & Max G'Sell & Ryan J. Tibshirani, 2021. "Post‐selection inference for changepoint detection algorithms with application to copy number variation data," Biometrics, The International Biometric Society, vol. 77(3), pages 1037-1049, September.
  • Handle: RePEc:bla:biomet:v:77:y:2021:i:3:p:1037-1049
    DOI: 10.1111/biom.13422
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    References listed on IDEAS

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    1. Venkata Jandhyala & Stergios Fotopoulos & Ian MacNeill & Pengyu Liu, 2013. "Inference for single and multiple change-points in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 423-446, July.
    2. 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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    Cited by:

    1. Cho, Haeran & Kirch, Claudia, 2024. "Data segmentation algorithms: Univariate mean change and beyond," Econometrics and Statistics, Elsevier, vol. 30(C), pages 76-95.
    2. Cho, Haeran & Kirch, Claudia, 2022. "Bootstrap confidence intervals for multiple change points based on moving sum procedures," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    3. Jelle J Goeman & Aldo Solari, 2024. "On selection and conditioning in multiple testing and selective inference," Biometrika, Biometrika Trust, vol. 111(2), pages 393-416.

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