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

التفاصيل البيبلوغرافية
العنوان: Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data.
المؤلفون: 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
المصدر: PLoS Computational Biology, Vol 16, Iss 9, p e1008270 (2020)
بيانات النشر: Public Library of Science (PLoS), 2020.
سنة النشر: 2020
المجموعة: LCC:Biology (General)
مصطلحات موضوعية: Biology (General), QH301-705.5
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1553-734X
1553-7358
Relation: https://doaj.org/toc/1553-734X; https://doaj.org/toc/1553-7358
DOI: 10.1371/journal.pcbi.1008270
URL الوصول: https://doaj.org/article/95e95387e9fb465bb06950a35a44a9d9
رقم الأكسشن: edsdoj.95e95387e9fb465bb06950a35a44a9d9
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:1553734X
15537358
DOI:10.1371/journal.pcbi.1008270