Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data

Camila P.E. de Souza; Mirela Andronescu; Tehmina Masud; Farhia Kabeer; Justina Biele; Emma Laks; Daniel Lai; Patricia Ye; Jazmine Brimhall; Beixi Wang; Edmund Su; Tony Hui; Qi Cao; Marcus Wong; Michelle Moksa; Richard A. Moore; Martin Hirst; Samuel Aparicio; Sohrab P. Shah

doi:10.1371/journal.pcbi.1008270

Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data

Camila P.E. de Souza, Mirela Andronescu, Tehmina Masud, Farhia Kabeer, Justina Biele, Emma Laks, Daniel Lai, Patricia Ye, Jazmine Brimhall, Beixi Wang, Edmund Su, Tony Hui, Qi Cao, Marcus Wong, Michelle Moksa, Richard A. Moore, Martin Hirst, Samuel Aparicio, Sohrab P. Shah

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18 Citations (Scopus)

Résumé

We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets, we show that Epiclomal outperforms non-probabilistic methods and can handle the inherent missing data characteristic that dominates single-cell CpG genome sequences. Using newly generated single-cell 5mCpG sequencing data, we show that Epiclomal discovers sub-clonal methylation patterns in aneuploid tumour genomes, thus defining epiclones that can match or transcend copy number-determined clonal lineages and opening up an important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.

Langue d'origine	English
Numéro d'article	1008270
Journal	PLoS Computational Biology
Volume	16
Numéro de publication	9
DOI	https://doi.org/10.1371/journal.pcbi.1008270
Statut de publication	Published - sept. 2020
Publié à l'externe	Oui

Note bibliographique

Funding Information:
We acknowledge generous funding support provided by the BC Cancer Foundation. S. A. is supported by grants from CIHR, Terry Fox Research Institute, Canadian Cancer Society Research Institute, and the Breast Cancer Research Foundation. S.P.S. is supported by CIHR, Terry Fox Research Institute (grant 1082) and the Canadian Cancer Society (grant 705636). C.P.E.d.S. is supported by the National Science and Engineering Research Council of Canada. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2020 P. E. de Souza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ASJC Scopus Subject Areas

Ecology, Evolution, Behavior and Systematics
Ecology
Modelling and Simulation
Molecular Biology
Genetics
Cellular and Molecular Neuroscience
Computational Theory and Mathematics

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de Souza, C. P. E., Andronescu, M., Masud, T., Kabeer, F., Biele, J., Laks, E., Lai, D., Ye, P., Brimhall, J., Wang, B., Su, E., Hui, T., Cao, Q., Wong, M., Moksa, M., Moore, R. A., Hirst, M., Aparicio, S., & Shah, S. P. (2020). Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data. PLoS Computational Biology, 16(9), Article 1008270. https://doi.org/10.1371/journal.pcbi.1008270

de Souza, CPE, Andronescu, M, Masud, T, Kabeer, F, Biele, J, Laks, E, Lai, D, Ye, P, Brimhall, J, Wang, B, Su, E, Hui, T, Cao, Q, Wong, M, Moksa, M, Moore, RA, Hirst, M, Aparicio, S & Shah, SP 2020, 'Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data', PLoS Computational Biology, vol. 16, n° 9, 1008270. https://doi.org/10.1371/journal.pcbi.1008270

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abstract = "We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets, we show that Epiclomal outperforms non-probabilistic methods and can handle the inherent missing data characteristic that dominates single-cell CpG genome sequences. Using newly generated single-cell 5mCpG sequencing data, we show that Epiclomal discovers sub-clonal methylation patterns in aneuploid tumour genomes, thus defining epiclones that can match or transcend copy number-determined clonal lineages and opening up an important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.",

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note = "Funding Information: We acknowledge generous funding support provided by the BC Cancer Foundation. S. A. is supported by grants from CIHR, Terry Fox Research Institute, Canadian Cancer Society Research Institute, and the Breast Cancer Research Foundation. S.P.S. is supported by CIHR, Terry Fox Research Institute (grant 1082) and the Canadian Cancer Society (grant 705636). C.P.E.d.S. is supported by the National Science and Engineering Research Council of Canada. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: {\textcopyright} 2020 P. E. de Souza et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",

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AU - Biele, Justina

AU - Laks, Emma

AU - Lai, Daniel

AU - Ye, Patricia

AU - Brimhall, Jazmine

AU - Wang, Beixi

AU - Su, Edmund

AU - Hui, Tony

AU - Cao, Qi

AU - Wong, Marcus

AU - Moksa, Michelle

AU - Moore, Richard A.

AU - Hirst, Martin

AU - Aparicio, Samuel

AU - Shah, Sohrab P.

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Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data

Résumé

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ASJC Scopus Subject Areas

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