Scanning a Poisson Random Field for Local Signals

TL;DR

This paper introduces a likelihood-based framework for detecting local signals in genomic data modeled as Poisson random fields, addressing false positive control, over-dispersion, and applying it to DNA sequencing for insertions and deletions.

Contribution

It develops a novel statistical framework for scanning Poisson fields in genomics, including formulas for false positive and power, and demonstrates its application to real sequencing data.

Findings

Framework effectively detects insertions and deletions in sequencing data.

Proposed statistics outperform existing methods under current experimental designs.

Application on real data illustrates practical utility and accuracy.

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 for such scans, and derive formulas for false positive rate control and power calculations. The framework can also accommodate mixtures of Poisson processes to deal with over-dispersion. 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.