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

• Published in: Biometrics Vol. 77; no. 3; pp. 1037 - 1049
• Main Authors: Hyun, Sangwon, Lin, Kevin Z., G'Sell, Max, Tibshirani, Ryan J.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.09.2021
• Subjects: Abnormalities; Algorithms; Arrays; changepoint detection; comparative genomic hybridization; comparative genomic hybridization analysis; Copy number; copy number variation; genome; Hybridization; hypothesis tests; Importance sampling; Inference; Markov chain; Markov chains; post‐selection inference; Sampling; Segmentation; segmentation algorithms; uncertainty; Variation; comparative genomic hybridization analysis; post-selection inference; copy number variation; segmentation algorithms; changepoint detection; hypothesis tests
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.13422

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.