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Principal Component Analysis and the Locus of the Fréchet Mean in the Space of Phylogenetic Trees

Lookup NU author(s): Dr Tom Nye



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Most biological data are multidimensional, posing a major challenge to human comprehension and computational analysis. Principal component analysis is the most popular approach to rendering two- or three-dimensional representations of the major trends in such multidimensional data. The problem of multidimensionality is acute in the rapidly growing area of phylogenomics. Evolutionary relationships are represented by phylogenetic trees, and very typically a phylogenomic analysis results in a collection of such trees, one for each gene in the analysis. Principal component analysis offers a means of quantifying variation and summarizing a collection of phylogenies by dimensional reduction. However, the space of all possible phylogenies on a fixed set of species does not form a Euclidean vector space, so principal component analysis must be reformulated in the geometry of tree-space, which is a CAT(0) geodesic metric space. Previous work has focused on construction of the first principal component, or principal geodesic. Here we propose a geometric object which represents a $k$-th order principal component: the locus of the weighted Fr\'echet mean of $k+1$ points in tree-space, where the weights vary over the standard $k$-dimensional simplex. We establish basic properties of these objects, in particular that locally they generically have dimension $k$, and we propose an efficient algorithm for projection onto these surfaces. Combined with a stochastic optimization algorithm, this projection algorithm gives a procedure for constructing a principal component of arbitrary order in tree-space. Simulation studies confirm these algorithms perform well, and they are applied to data sets of Apicomplexa gene trees and the African coelacanth genome. The results enable visualizations of slices of tree-space, revealing structure within these complex data sets.

Publication metadata

Author(s): Nye TMW, Tang X, Weyenberg G, Yoshida R

Publication type: Article

Publication status: Published

Journal: Biometrika

Year: 2017

Volume: 104

Issue: 4

Pages: 901-922

Print publication date: 01/12/2017

Online publication date: 27/09/2017

Acceptance date: 17/08/2017

Date deposited: 02/10/2017

ISSN (print): 0006-3444

ISSN (electronic): 1464-3510

Publisher: Oxford University Press


DOI: 10.1093/biomet/asx047


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Funder referenceFunder name
Wellcome Trust