Abstract
In recent years, many algorithmic strategies have been developed to exploit single-cell mutational profiles generated via sequencing experiments of cancer samples and return reliable models of cancer evolution. Here, we introduce the COB-tree algorithm, which summarizes the solutions explored by state-of-the-art methods for clonal tree inference, to return a unique consensus optimum branching tree. The method proves to be highly effective in detecting pairwise temporal relations between genomic events, as demonstrated by extensive tests on simulated datasets. We also provide a new method to visualize and quantitatively inspect the solution space of the inference methods, via Principal Coordinate Analysis. Finally, the application of our method to a single-cell dataset of patient-derived melanoma xenografts shows significant differences between the COB-tree solution and the maximum likelihood ones.
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Acknowledgments
This work was supported by a Bicocca 2020 Starting Grant and Google Cloud Academic Research Grant to DR and FA. Partial support is also granted by the CRUK/AIRC Accelerator Award #22790 “Single-cell Cancer Evolution in the Clinic”. The funders had no role in the design and conduct of the study, analysis, and interpretation of the data, preparation of the manuscript, and decision to submit the manuscript for publication.
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Maspero, D., Angaroni, F., Patruno, L., Ramazzotti, D., Posada, D., Graudenzi, A. (2023). Exploring the Solution Space of Cancer Evolution Inference Frameworks for Single-Cell Sequencing Data. In: De Stefano, C., Fontanella, F., Vanneschi, L. (eds) Artificial Life and Evolutionary Computation. WIVACE 2022. Communications in Computer and Information Science, vol 1780. Springer, Cham. https://doi.org/10.1007/978-3-031-31183-3_6
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