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 language | English |
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Pages (from-to) | 726-755 |
Number of pages | 30 |
Journal | Annals of Applied Statistics |
Volume | 10 |
Issue number | 2 |
DOIs | |
State | Published - Jun 2016 |
Keywords
- Change-point detection
- Nextgeneration sequencing
- Poisson processes
- Scan statistics
- Structural variation
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Modelling and Simulation
- Statistics, Probability and Uncertainty