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Uncertainty Estimation in Models of Multivariate Trait Evolution on Given Phylogenies
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
2024 (English)Licentiate thesis, comprehensive summary (Other academic)Alternative title
Osäkerhetsuppskattning i modeller av multivariat dragevolution på givna fylogenier (Swedish)
Abstract [en]

Phylogenetic comparative methods are a set of statistical methods that model the evolutionary history of species, especially in the context where one has data on certain traits of related extant species that have evolved over a phylogenetic tree in accordance to an underlying stochastic process. 

This thesis presents a Hessian-based closed-form asymptotic confidence region that covers a wide family of Gaussian continuous-trait evolution models; the result has been implemented in an R package. Also, some analyses have been done on the simpler Brownian Motion and Ornstein-Uhlenbeck process cases; and this leads to novel exact confidence regions for the Brownian Motion’s parameters and a closed-form ’partial’ unbiased estimator for the Ornstein-Uhlenbeck process’ varaince-covariance matrix when other parameters are given. 

The thesis contains two papers. Paper I is an applied work that uses discrete-trait speciation and extinction model to investigate early spread of COVID-19; it shows that it is possible to detect statistical signals of inter-continental spread of the virus from a very noisy world-wide phylogeny. Paper II is a more mathematical work that derived the closed-form formulae for the Hessian matrix of a wide family of Gaussian-process-based multivariate continuous-trait PCM models; accompanying with the Paper I have developed an R package called glinvci, publicly available on The Comprehensive R Archive Network (CRAN), that can compute Hessian-based confidence regions for these models while at the same time allowing users to have missing data and multiple evolutionary regimes. 

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2024. , p. 43
Series
Faculty of Arts and Sciences thesis, ISSN 1401-4637 ; 134
Keywords [en]
Phylogenetic comparative methods, Branching stochastic processes
Keywords [sv]
Fylogenetiska jämförande metoder, Förgrenade stokastiska processer
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-201907DOI: 10.3384/9789180755900ISBN: 9789180755894 (print)ISBN: 9789180755900 (electronic)OAI: oai:DiVA.org:liu-201907DiVA, id: diva2:1847102
Presentation
2024-04-23, Ada Lovelace, B-building, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Note

Funding: Vetenskapsrådet [Grant 2017-04951] and STIMA.

2024-04-05: Series have been corrected in the e-version

Available from: 2024-03-26 Created: 2024-03-26 Last updated: 2024-04-05Bibliographically approved
List of papers
1. Summarizing Global SARS-CoV-2 Geographical Spread by Phylogenetic Multitype Branching Models
Open this publication in new window or tab >>Summarizing Global SARS-CoV-2 Geographical Spread by Phylogenetic Multitype Branching Models
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2022 (English)In: COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS AND BIOSTATISTICS, CIBB 2021, SPRINGER INTERNATIONAL PUBLISHING AG , 2022, Vol. 13483, p. 170-184Conference paper, Published paper (Refereed)
Abstract [en]

Using available phylogeographical data of 3585 SARS–CoV–2 genomes we attempt at providing a global picture of the virus’s dynamics in terms of directly interpretable parameters. To this end we fit a hidden state multistate speciation and extinction model to a pre-estimated phylogenetic tree with information on the place of sampling of each strain. We find that even with such coarse–grained data the dominating transition rates exhibit weak similarities with the most popular, continent–level aggregated, airline passenger flight routes.

Place, publisher, year, edition, pages
SPRINGER INTERNATIONAL PUBLISHING AG, 2022
Series
Lecture Notes in Bioinformatics, ISSN 0302-9743
Keywords
COVID-19; Hidden Markov model; Phylogeography; State-dependent diversification
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:liu:diva-191763 (URN)10.1007/978-3-031-20837-9_14 (DOI)000895973300014 ()9783031208362 (ISBN)9783031208379 (ISBN)
Conference
17th International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB), ELECTR NETWORK, nov 15-17, 2021
Note

Funding Agencies|Vetenskapsradets [2017-04951]; ELLIIT Call C grant

Available from: 2023-02-13 Created: 2023-02-13 Last updated: 2024-03-26

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Kiang, Woodrow Hao Chi

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12341 of 4
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Output format
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