Scan statistics on Poisson random fields with applications in genomics
Nancy R. Zhang, Benjamin Yakir, Li C. Xia, David Siegmund
The Annals of Applied Statistics · 2016 · Volume 10 · Issue 2
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.
Keywords
change-point detection, next-generation sequencing, Poisson processes, scan statistics, structural variation
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