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

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...

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Bibliographic Details
Published in:PLoS computational biology 2020-09, Vol.16 (9), p.e1008270-e1008270
Main Authors: P. E. de Souza, Camila, Andronescu, Mirela, Masud, Tehmina, Kabeer, Farhia, Biele, Justina, Laks, Emma, Lai, Daniel, Ye, Patricia, Brimhall, Jazmine, Wang, Beixi, Su, Edmund, Hui, Tony, Cao, Qi, Wong, Marcus, Moksa, Michelle, Moore, Richard A, Hirst, Martin, Aparicio, Samuel, Shah, Sohrab P
Format: Article
Language:English
Subjects:
Biology and life sciences
Breast cancer
Clustering
Copy number
CpG islands
Deoxyribonucleic acid
DNA
DNA methylation
DNA sequencing
Gene sequencing
Genomes
Laboratories
Medicine and Health Sciences
Methods
Methylation
Missing data
Observations
Physical Sciences
Probabilistic methods
Probabilistic models
Research and Analysis Methods
Software
Stem cells
Tumors
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Summary: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
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1008270