2018/09/29 by Toby Dylan Hocking, Guillem Rigaill, Hocking, Toby Dylan +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Gene expression and cancer classification #Genetic Associations and Epidemiology
paper · doi:10.48550/arxiv.1810.00117
openalex publication_date 2018/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We describe a new algorithm and R package for peak detection in genomic data sets using constrained changepoint algorithms. These detect changes from background to peak regions by imposing the constraint that the mean should alternately increase then decrease. An existing algorithm for this problem exists, and gives state-of-the-art accuracy results, but it is computationally expensive when the number of changes is large. We propose the GFPOP algorithm that jointly estimates the number of peaks and their locations by minimizing a cost function which consists of a data fitting term and a penalty for each changepoint. Empirically this algorithm has a cost that is O(N log(N)) for analysing data of length N. We also propose a sequential search algorithm that 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 that our disk-based implementation in the PeakSegDisk R package can be used to quickly compute constrained optimal models with many changepoints, which are needed to analyze typical genomic data sets that have tens of millions of observations.