Multidimensional scaling for phylogenetics

Author: 
Date created: 
2019-04-11
Identifier: 
etd20260
Keywords: 
Classical multidimensional scaling
Bayesian multidimensional scaling
Sequential Monte Carlo
Particle Markov Chain Monte Carlo
Steiner tree
Phylogenetic tree
Abstract: 

We study a novel approach to determine the phylogenetic tree based on multidimensional scaling and Euclidean Steiner minimum tree. Pairwise sequence alignment method is implemented to align the objects such as DNA sequences and then some evolutionary models are applied to get the estimated distance matrix. Given the distance matrix, multidimensional scaling is widely used to reconstruct the map which has coordinates of the data points in a lower-dimensional space while preserves the distance. We employ both Classical multidimensional scaling and Bayesian multidimensional scaling on the distance matrix to obtain the coordinates of the objects. Based on the coordinates, the Euclidean Steiner minimum tree could be constructed and served as a candidate for the phylogenetic tree. The result from the simulation study indicates that the use of the Euclidean Steiner minimum tree as a phylogenetic tree is feasible.

Document type: 
Graduating extended essay / Research project
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Supervisor(s): 
Liangliang Wang
Department: 
Science: Department of Statistics and Actuarial Science
Thesis type: 
(Project) M.Sc.
Statistics: