Markov chain Monte Carlo for the reconstruction of lineage trees from single-cell DNA data
Tutor / Supervisor
Lagergren, Jens
Hellgren Kotaleski, Jeanette
Casanelles, Marta
Student
Balcázar Castell, Iris
Document type
Bachelor thesis
Date
2019
rights
Open Access
Publisher
Universitat Politècnica de Catalunya
School
UPCommons
Abstract
The purpose of this study is to infer evolutionary trees through the Markov chain Monte Carlo algorithm (MCMC) based on whole genome single-cell DNA sequencing data.
By using MCMC we obtain likely tree structure samples according to the cells' somatic point mutations in our data. This probabilistic framework takes into consideration the errors caused by the current technology such as amplification errors, sequencing errors and allelic dropouts.
We investigated whether using this technique is reasonable given this biological scope. Most of the results give interesting conclusions that improve the previous results on the same Site Pair Model and therefore we conclude that using MCMC is reasonable. Though, since the model is based on probabilities and the algorithm randomizes decisions the best results are not always guaranteed. One needs to be aware that a decent amount of data in the data set is an important requisite to predict accurate tree structures. Furthermore, the computational time for this process is significantly high and can not be computed on regular laptops for large and realistic data sets. This is acceptable since for this type of research speed is not a strict requi
Entitat col·laboradora
Kungliga Tekniska högskolan
Kungliga Tekniska högskolan
Location
Participating teacher
- Lagergren, Jens
- Hellgren Kotaleski, Jeanette
- Casanelles, Marta