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

10 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 languageAmerican English
Pages (from-to)726-755
Number of pages30
JournalAnnals of Applied Statistics
Volume10
Issue number2
DOIs
StatePublished - Jun 2016

Bibliographical note

Funding Information:
Supported in part by NSF Grant DMS 1043204. Supported in part by NIH R01 HG006137-01. Supported in part by the Sloan Foundation.

Publisher Copyright:
© Institute of Mathematical Statistics, 2016.

Keywords

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

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