Network science inspires novel tree shape statistics

• Published in: PloS one 2021-12, Vol.16 (12), p.e0259877-e0259877
• Main Authors: Chindelevitch, Leonid, Hayati, Maryam, Poon, Art F Y, Colijn, Caroline
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Asymmetry; Biology and Life Sciences; Complex systems; Computational Biology - methods; Computer and Information Sciences; Computer simulation; Computer-generated environments; Data Interpretation, Statistical; Decision Trees; Dengue; Dengue - epidemiology; Dengue - virology; Dengue fever; Dengue Virus - classification; Diameters; Eigenvectors; Epidemiology; Extreme values; Fever; Genetic distance; Grants; HIV; HIV Infections - epidemiology; HIV Infections - virology; HIV-1 - classification; Human immunodeficiency virus; Humans; Infectious diseases; Influenza A; Learning algorithms; Machine learning; Measles; Measles - epidemiology; Measles - virology; Measles virus - classification; Medicine and Health Sciences; Methods; Molecular genetics; Pathogens; Phylogenetic trees; Phylogenetics; Phylogeny; Physical Sciences; Protein interaction; Social organization; Software; Statistics; Summaries; Vector-borne diseases; Viral diseases; Virus Diseases - epidemiology; Virus Diseases - virology; Viruses; Viruses - classification
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0259877

The shape of phylogenetic trees can be used to gain evolutionary insights. A tree's shape specifies the connectivity of a tree, while its branch lengths reflect either the time or genetic distance between branching events; well-known measures of tree shape include the Colless and Sackin imbalance, which describe the asymmetry of a tree. In other contexts, network science has become an important paradigm for describing structural features of networks and using them to understand complex systems, ranging from protein interactions to social systems. Network science is thus a potential source of many novel ways to characterize tree shape, as trees are also networks. Here, we tailor tools from network science, including diameter, average path length, and betweenness, closeness, and eigenvector centrality, to summarize phylogenetic tree shapes. We thereby propose tree shape summaries that are complementary to both asymmetry and the frequencies of small configurations. These new statistics can be computed in linear time and scale well to describe the shapes of large trees. We apply these statistics, alongside some conventional tree statistics, to phylogenetic trees from three very different viruses (HIV, dengue fever and measles), from the same virus in different epidemiological scenarios (influenza A and HIV) and from simulation models known to produce trees with different shapes. Using mutual information and supervised learning algorithms, we find that the statistics adapted from network science perform as well as or better than conventional statistics. We describe their distributions and prove some basic results about their extreme values in a tree. We conclude that network science-based tree shape summaries are a promising addition to the toolkit of tree shape features. All our shape summaries, as well as functions to select the most discriminating ones for two sets of trees, are freely available as an R package at http://github.com/Leonardini/treeCentrality.