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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 infer their corresponding hidden methylation profiles. Using synthetic and published single-cell CpG datasets we show that Epiclomal outperforms non-probabilistic methods and is able to handle the inherent missing data feature which dominates single-cell CpG genome sequences. Using a recently published single-cell 5mCpG sequencing method (PBAL), we show that Epiclomal discovers sub-clonal patterns of methylation in aneuploid tumour genomes, thus defining epiclones. We show that epiclones may transcend copy number determined clonal lineages, thus opening this important form of clonal analysis in cancer.

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID Description Technology Samples
EGAD00001004812 Illumina HiSeq 2500 40
EGAD00001004813 Illumina HiSeq 2500 48
EGAD00001004814 Illumina HiSeq 2500 50
EGAD00001004815 Illumina HiSeq 2500 45
EGAD00001004816 Illumina HiSeq 2500 61
EGAD00001004817 Illumina HiSeq 2500 68
EGAD00001004818 Illumina HiSeq 2500 71
EGAD00001004819 Illumina HiSeq 2500 48
EGAD00001004820 Illumina HiSeq 2500 52
EGAD00001004821 Illumina HiSeq 2500 60
EGAD00001004822 Illumina HiSeq 2500 55
Publications Citations
Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data.
PLoS Comput Biol 16: 2020 e1008270