Statistics Papers
Document Type
Journal Article
Date of this Version
2016
Publication Source
The Annals of Applied Statistics
Volume
10
Issue
2
Start Page
726
Last Page
755
DOI
10.1214/15-AOAS892
Abstract
The detection of local genomic signals using high-throughput DNA sequencing data can be cast as a problem of scanning a Poisson random field for local changes in the rate of the process. We propose a likelihood-based framework for such scans, and derive formulas for false positive rate control and power calculations. The framework can also accommodate modified processes that involve overdispersion. As a specific, detailed example, we consider the detection of insertions and deletions by paired-end DNA-sequencing. We propose several statistics for this problem, compare their power under current experimental designs, and illustrate their application on an Illumina Platinum Genomes data set.
Copyright/Permission Statement
The original and published work is available at: https://projecteuclid.org/euclid.aoas/1469199891#abstract
Keywords
Scan statistics, Poisson processes, change-point detection, next-generation sequencing, structural variation
Recommended Citation
Zhang, N. R., Yakir, B., Xia, C. L., & Siegmund, D. O. (2016). Scanning a Poisson Random Field for Local Signals. The Annals of Applied Statistics, 10 (2), 726-755. http://dx.doi.org/10.1214/15-AOAS892
Date Posted: 27 November 2017
This document has been peer reviewed.