Markov chain Monte Carlo for the reconstruction of lineage trees from single-cell DNA data
Abstract: The purpose of this study is to infer evolutionary trees through the Markovchain Monte Carlo algorithm (MCMC) [1] 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 [2] 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 dataset 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 requirement: it is worth waiting more for a given execution if the obtained results are more interestingor more accurate. Finally, we propose some further improvements for this strategy that could potentially obtain even better results in terms of accuracy and speed.
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