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

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

18 Citas (Scopus)

Resumen

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.

Idioma original	English
Número de artículo	1008270
Publicación	PLoS Computational Biology
Volumen	16
N.º	9
DOI	https://doi.org/10.1371/journal.pcbi.1008270
Estado	Published - sep. 2020
Publicado de forma externa	Sí

Nota bibliográfica

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

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

Acceder al documento

10.1371/journal.pcbi.1008270

Otros archivos y enlaces

Citar esto

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), Artículo 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.º 9, 1008270. https://doi.org/10.1371/journal.pcbi.1008270

@article{bb10007f5b61414dbf1fe56b7746c164,

title = "Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data",

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.",

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

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.",

year = "2020",

month = sep,

doi = "10.1371/journal.pcbi.1008270",

language = "English",

volume = "16",

journal = "PLoS Computational Biology",

issn = "1553-734X",

publisher = "Public Library of Science",

number = "9",

}

TY - JOUR

T1 - Epiclomal

T2 - Probabilistic clustering of sparse single-cell DNA methylation data

AU - de Souza, Camila P.E.

AU - Andronescu, Mirela

AU - Masud, Tehmina

AU - Kabeer, Farhia

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.

N1 - 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.

PY - 2020/9

Y1 - 2020/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85092259225&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85092259225&partnerID=8YFLogxK

U2 - 10.1371/journal.pcbi.1008270

DO - 10.1371/journal.pcbi.1008270

M3 - Article

C2 - 32966276

AN - SCOPUS:85092259225

SN - 1553-734X

VL - 16

JO - PLoS Computational Biology

JF - PLoS Computational Biology

IS - 9

M1 - 1008270

ER -

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

Resumen

Nota bibliográfica

ASJC Scopus Subject Areas

ODS de las Naciones Unidas

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto