Building alternative consensus trees and supertrees using k-means and Robinson and Foulds distance

TL;DR

This paper introduces a novel, efficient k-means based method utilizing Robinson and Foulds distance to infer multiple alternative consensus trees and supertrees, effectively capturing diverse evolutionary patterns in large gene datasets.

Contribution

It presents a new clustering approach for phylogenetic trees that improves speed and handles heterogeneous data, with adaptations of standard validity indices for tree clustering.

Findings

Successfully applied to SARS-CoV-2 and betacoronavirus datasets

Identified multiple evolutionary patterns with alternative supertrees

Faster than existing tree clustering techniques

Abstract

Each gene has its own evolutionary history which can substantially differ from the evolutionary histories of other genes. For example, some individual genes or operons can be affected by specific horizontal gene transfer and recombination events. Thus, the evolutionary history of each gene should be represented by its own phylogenetic tree which may display different evolutionary patterns from the species tree that accounts for the main patterns of vertical descent. The output of traditional consensus tree or supertree inference methods is a unique consensus tree or supertree. We describe a new efficient method for inferring multiple alternative consensus trees and supertrees to best represent the most important evolutionary patterns of a given set of gene phylogenies. We show how an adapted version of the popular k-means clustering algorithm, based on some interesting properties of the Robinson and Foulds distance, can be used to partition a given set of trees into one (for homogeneous data) or multiple (for heterogeneous data) cluster(s) of trees. Moreover, we adapt the popular Cali\'nski-Harabasz, Silhouette, Ball and Hall, and Gap cluster validity indices to tree clustering with k-means. A special attention is given to the relevant but very challenging problem of inferring alternative supertrees. The use of the Euclidean property of the objective function of the method makes it faster than the existing tree clustering techniques, and thus perfectly suitable for analyzing large evolutionary datasets. We apply the new method to discover alternative supertrees characterizing the main patterns of evolution of SARS-CoV-2 and genetically related betacoronaviruses.