Employing phylogenetic tree shape statistics to resolve the underlying host population structure

Pontiano Kaleebu,Andrew J Leigh Brown,John M Mango,Deogratius Ssemwanga,Nicholas Bbosa,Roberto A Saenz,Ronald Galiwango,Alfred Ssekagiri,Hassan W Kayondo,Samuel Mwalili,Grace Nabakooza,John M Kitayimbwa

doi:10.1186/s12859-021-04465-1

Abstract

BackgroundHost population structure is a key determinant of pathogen and infectious disease transmission patterns. Pathogen phylogenetic trees are useful tools to reveal the population structure underlying an epidemic. Determining whether a population is structured or not is useful in informing the type of phylogenetic methods to be used in a given study. We employ tree statistics derived from phylogenetic trees and machine learning classification techniques to reveal an underlying population structure.ResultsIn this paper, we simulate phylogenetic trees from both structured and non-structured host populations. We compute eight statistics for the simulated trees, which are: the number of cherries; Sackin, Colless and total cophenetic indices; ladder length; maximum depth; maximum width, and width-to-depth ratio. Based on the estimated tree statistics, we classify the simulated trees as from either a non-structured or a structured population using the decision tree (DT), K-nearest neighbor (KNN) and support vector machine (SVM). We incorporate the basic reproductive number (R_0) in our tree simulation procedure. Sensitivity analysis is done to investigate whether the classifiers are robust to different choice of model parameters and to size of trees. Cross-validated results for area under the curve (AUC) for receiver operating characteristic (ROC) curves yield mean values of over 0.9 for most of the classification models.ConclusionsOur classification procedure distinguishes well between trees from structured and non-structured populations using the classifiers, the two-sample Kolmogorov-Smirnov, Cucconi and Podgor-Gastwirth tests and the box plots. SVM models were more robust to changes in model parameters and tree size compared to KNN and DT classifiers. Our classification procedure was applied to real -world data and the structured population was revealed with high accuracy of 92.3% using SVM-polynomial classifier.

Highlights

Host population structure is a key determinant of pathogen and infectious disease transmission patterns
Distributions for tree statistics for non‐structured and structured populations For the two datasets generated, trees from a non-structured population had higher Colless and Sackin index values compared to trees from a structured population (Fig. 2 & Fig. 3)
Tree index values for cophenetic, maximum depth, maximum depth and width to depth ratio were slightly higher for a structured compared to a non-structured population (Fig. 2 & Fig. 3)

Summary

Introduction

Host population structure is a key determinant of pathogen and infectious disease transmission patterns. The tree topology is described by the branching patterns arising from events such as birth, death, migration and sampling among the populations being analysed [6]. The underlying structure of the host population can be determined from a tree that is reconstructed using genomes from randomly sampled individuals coupled with their demographic characteristics [17]. This is usually done by analysing the clustering and balance of taxa on the resultant tree

Methods

Results

Discussion

Conclusion