s-SaRa: a stable and powerful algorithm for DNA copy number variation detection

Jia Shengji () and Shi Lei ()
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Jia Shengji: Shanghai Lixin University of Accounting and Finance
Shi Lei: Yunnan University of Finance and Economics

Statistical Papers, 2025, vol. 66, issue 6, No 15, 20 pages

Abstract: Abstract Identifying the number and exact locations of change-points is an important problem in DNA copy number variation detection. The screening and ranking algorithm (SaRa) serves as an efficient change-point detection tool with a computational complexity of O(n). Nevertheless, the performance of the estimated change-points by SaRa depends heavily on the choices of bandwidth and threshold parameters, necessitating meticulous tuning. Furthermore, SaRa’s inherently local nature limits its power and precision in detecting change-points. In this study, we introduce a refined version of SaRa, termed s-SaRa, specifically tailored to enhance the performance of change-point detection. Our approach initially leverages the SaRa algorithm to generate a pool of change-points candidates. Subsequently, a refined algorithm is proposed to improve the initial candidates and a multiple testing procedure is conducted to determine the optimal number of change-points. Simulation analyses underscore the stability (parameter-insensitivity) of s-SaRa, indicating its superior performance in a variety of conditions. Moreover, s-SaRa is found to be more powerful in detecting change-points with weak signals (small jump sizes). We demonstrate the practical efficacy of s-SaRa by applying it to both Coriel and SNP genotyping datasets.

Keywords: Change-point; Copy number variation; Ridge regression; Multiple testing; Screening and ranking algorithm (SaRa ) (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00362-025-01762-2

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