Automated convergence diagnostic for phylogenetic MCMC analyses

Abstract

AbstractConvergence assessment in Markov chain Monte Carlo (MCMC) based analyses is crucial but challenging, especially so in high dimensional and complex spaces such as the space of phylogenetic trees (treespace). Here we leverage recent advances in computational geometry of the treespace and introduce a method that combines classical statistical techniques and algorithms with geometric properties of the treespace to automatically evaluate and assess convergence of phylogenetic MCMC analyses. Our method systematically evaluates convergence across multiple MCMC chains and achieves high accuracy in detecting convergence of chains over the treespace. Furthermore, our approach is developed to allow for realtime convergence evaluation during the MCMC algorithm run, eliminating any of the chain post-processing steps that are currently required. Our tool therefore improves reliability and efficiency of MCMC based phylogenetic inference methods and makes analyses easier to reproduce and compare. We demonstrate the efficacy of our diagnostic via a well calibrated simulation study and provide examples of its performance on real data sets.The open source package for the phylogenetic inference framework BEAST2, called ASM, that implements these methods, making them accessible through a user-friendly GUI, is available fromhttps://github.com/rbouckaert/asm/. The open source Python package, called tetres, that provides an interface for these methods enabling their applications beyond BEAST2 can be accessed athttps://github.com/bioDS/tetres/.