Change-Point Model on Nonhomogeneous Poisson Processes With Application in Copy Number Profiling by Next-Generation DNA Sequencing
inhomogeneous Poisson process
point-wise confidence interval
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.