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A backward procedure for change-point detection with applications to copy number variation detection

Authors
Jun Shin, SeungWu, YichaoHao, Ning
Issue Date
9월-2020
Publisher
WILEY
Keywords
Backward detection; copy number variation; mean change-point model; multiple change points; Short signal
Citation
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, v.48, no.3, pp.366 - 385
Indexed
SCIE
SCOPUS
Journal Title
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE
Volume
48
Number
3
Start Page
366
End Page
385
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/53258
DOI
10.1002/cjs.11535
ISSN
0319-5724
Abstract
Change-point detection regains much attention recently for analyzing array or sequencing data for copy number variation (CNV) detection. In such applications, the true signals are typically very short and buried in the long data sequence, which makes it challenging to identify the variations efficiently and accurately. In this article, we propose a new change-point detection method, a backward procedure, which is not only fast and simple enough to exploit high-dimensional data but also performs very well for detecting short signals. Although motivated by CNV detection, the backward procedure is generally applicable to assorted change-point problems that arise in a variety of scientific applications. It is illustrated by both simulated and real CNV data that the backward detection has clear advantages over other competing methods, especially when the true signal is short. The Canadian Journal of Statistics; 2020 (c) 2020 Statistical Society of Canada
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