Scan statistics on Poisson random fields with applications in genomics

Nancy R. Zhang, Benjamin Yakir, Li C. Xia, David Siegmund

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

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 DNAsequencing. 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.

Original languageEnglish (US)
Pages (from-to)726-755
Number of pages30
JournalAnnals of Applied Statistics
Volume10
Issue number2
DOIs
StatePublished - Jun 2016

Keywords

  • Change-point detection
  • Nextgeneration sequencing
  • Poisson processes
  • Scan statistics
  • Structural variation

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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