Change-point model on nonhomogeneous Poisson processes with application in copy number profiling by next-generation DNA sequencing

TL;DR

This paper introduces a flexible change-point model for inhomogeneous Poisson processes, specifically applied to DNA copy number profiling using next-generation sequencing data, with methods for model selection and confidence assessment.

Contribution

It develops a novel change-point model tailored for inhomogeneous Poisson processes in DNA sequencing, including new statistical tools for model selection and confidence interval estimation.

Findings

Effective segmentation of sequencing data demonstrated on simulated and real datasets.

The model accurately detects change-points in DNA copy number profiles.

Proposed methods outperform existing approaches in accuracy and confidence assessment.

Abstract

We propose a flexible change-point model for inhomogeneous Poisson Processes, which arise naturally from next-generation DNA sequencing, and derive score and generalized likelihood statistics for shifts in intensity functions. We construct a modified Bayesian information criterion (mBIC) to guide model selection, and point-wise approximate Bayesian confidence intervals for assessing the confidence in the segmentation. The model is applied to DNA Copy Number profiling with sequencing data and evaluated on simulated spike-in and real data sets.