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A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Computational Tools for Evaluating Phylogenetic and Hierarchical Clustering Trees.

John Chakerian¹, Susan Holmes²

¹Palantir Technologies.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America

|September 28, 2020

Summary

A new geometric distance metric for phylogenetic and hierarchical clustering trees enhances stability analysis. This method aids in evaluating tree construction influences and detecting data heterogeneity.

Keywords:

Bootstrap Hierarchical clustering Multidimensional scaling Negatively curved space Phylogenetic tree

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Area of Science:

Computational Biology
Data Science
Evolutionary Biology

Background:

Phylogenetic trees and hierarchical clustering trees are crucial in various fields.
Assessing the stability and generalizability of these tree estimates is essential.
Existing methods for tree comparison have limitations.

Purpose of the Study:

To implement and apply the geometric distance between trees (Billera, Holmes, and Vogtmann, 2001) for evaluating tree estimates.
To explore the application of this distance metric in representing collections of trees within a CAT(0) space.
To compare this representation with Euclidean approximations from multidimensional scaling techniques.

Main Methods:

Implementation of the geometric distance metric for trees.
Representation of tree collections as a tree within a CAT(0) space.
Comparison with classical and Kernel multidimensional scaling using distance matrices.

Main Results:

The geometric distance metric is applicable to both phylogenetic and hierarchical clustering trees.
A collection of trees can be naturally represented as a tree in the CAT(0) space.
The method provides new ways to evaluate the influence of data features (rows/columns) on tree construction.

Conclusions:

The geometric distance metric offers a robust framework for analyzing tree structures.
This approach facilitates the evaluation of tree stability and data influences.
The method aids in detecting heterogeneity within datasets, particularly in microarray analyses.