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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Background: Host 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. 

Results: In 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) 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. 

Conclusions: Our 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.

Original languageEnglish
Article number546
Number of pages20
JournalBMC Bioinformatics
Issue number1
Publication statusPublished - 10 Nov 2021

Keywords / Materials (for Non-textual outputs)

  • classification
  • host population
  • non-structured
  • phylogenetic tree
  • simulation
  • structured
  • tree statistics


Dive into the research topics of 'Employing phylogenetic tree shape statistics to resolve the underlying host population structure'. Together they form a unique fingerprint.

Cite this