Finding high posterior density phylogenies by systematically extending a directed acyclic graph

TL;DR

This paper introduces methods to efficiently identify high posterior density phylogenies by extending a directed acyclic graph structure, offering a systematic alternative to traditional MCMC approaches in Bayesian phylogenetics.

Contribution

It develops and compares two novel methods for systematically exploring tree space using sDAGs, improving the efficiency and effectiveness over random walk methods.

Findings

One method successfully recovers high posterior density trees.

Aggregating trees into sDAGs is faster and yields more probable trees.

The simpler sDAG aggregation method outperforms more complex strategies.

Abstract

Bayesian phylogenetics typically estimates a posterior distribution, or aspects thereof, using Markov chain Monte Carlo methods. These methods integrate over tree space by applying local rearrangements to move a tree through its space as a random walk. Previous work explored the possibility of replacing this random walk with a systematic search, but was quickly overwhelmed by the large number of probable trees in the posterior distribution. In this paper we develop methods to sidestep this problem using a recently introduced structure called the subsplit directed acyclic graph (sDAG). This structure can represent many trees at once, and local rearrangements of trees translate to methods of enlarging the sDAG. Here we propose two methods of introducing, ranking, and selecting local rearrangements on sDAGs to produce a collection of trees with high posterior density. One of these methods successfully recovers the set of high posterior density trees across a range of data sets. However, we find that a simpler strategy of aggregating trees into an sDAG in fact is computationally faster and returns a higher fraction of probable trees.