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We describe a new algorithm and R package for peak detection in genomic datasets using constrained changepoint algorithms. These detect changes frombackground to peak regions by imposing the constraint that the mean shouldalternately increase then decrease. An existing algorithm for this problemexists, and gives state-of-the-art accuracy results, but it is computationallyexpensive when the number of changes is large. We propose the GFPOP algorithmthat jointly estimates the number of peaks and their locations by minimizing acost function which consists of a data fitting term and a penalty for eachchangepoint. Empirically this algorithm has a cost that is $O(N \log(N))$ foranalysing data of length $N$. We also propose a sequential search algorithmthat finds the best solution with $K$ segments in $O(\log(K)N \log(N))$ time,which is much faster than the previous $O(KN \log(N))$ algorithm. We show thatour disk-based implementation in the PeakSegDisk R package can be used toquickly compute constrained optimal models with many changepoints, which areneeded to analyze typical genomic data sets that have tens of millions ofobservations.

Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data