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

Abstract 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 info...

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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Structured non-structured Host population Phylogenetic tree Simulation Tree statistics Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f921303b4ec8402c97778b1b59c8ee53
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spelling	oai:doaj.org-article:f921303b4ec8402c97778b1b59c8ee532021-11-14T12:13:09ZEmploying phylogenetic tree shape statistics to resolve the underlying host population structure10.1186/s12859-021-04465-11471-2105https://doaj.org/article/f921303b4ec8402c97778b1b59c8ee532021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04465-1https://doaj.org/toc/1471-2105Abstract 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 ( $$R_0$$ 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. 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 $$92.3\%$$ 92.3 % using SVM-polynomial classifier.Hassan W. KayondoAlfred SsekagiriGrace NabakoozaNicholas BbosaDeogratius SsemwangaPontiano KaleebuSamuel MwaliliJohn M. MangoAndrew J. Leigh BrownRoberto A. SaenzRonald GaliwangoJohn M. KitayimbwaBMCarticleStructurednon-structuredHost populationPhylogenetic treeSimulationTree statisticsComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-20 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Structured non-structured Host population Phylogenetic tree Simulation Tree statistics Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Structured non-structured Host population Phylogenetic tree Simulation Tree statistics Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 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 Employing phylogenetic tree shape statistics to resolve the underlying host population structure
description	Abstract 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 ( $$R_0$$ 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. 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 $$92.3\%$$ 92.3 % using SVM-polynomial classifier.
format	article
author	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
author_facet	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
author_sort	Hassan W. Kayondo
title	Employing phylogenetic tree shape statistics to resolve the underlying host population structure
title_short	Employing phylogenetic tree shape statistics to resolve the underlying host population structure
title_full	Employing phylogenetic tree shape statistics to resolve the underlying host population structure
title_fullStr	Employing phylogenetic tree shape statistics to resolve the underlying host population structure
title_full_unstemmed	Employing phylogenetic tree shape statistics to resolve the underlying host population structure
title_sort	employing phylogenetic tree shape statistics to resolve the underlying host population structure
publisher	BMC
publishDate	2021
url	https://doaj.org/article/f921303b4ec8402c97778b1b59c8ee53
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Employing phylogenetic tree shape statistics to resolve the underlying host population structure

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