Study

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

Study ID Alternative Stable ID Type
EGAS00001003504 Other

Study Description

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.

Study Datasets 11 datasets.

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Dataset ID Description Technology Samples
EGAD00001004812
40 samples; filetype=bam
Illumina HiSeq 2500 40
EGAD00001004813
48 samples; filetype=bam
Illumina HiSeq 2500 48
EGAD00001004814
50 samples; filetype=bam
Illumina HiSeq 2500 50
EGAD00001004815
45 samples; filetype=bam
Illumina HiSeq 2500 45
EGAD00001004816
61 samples; filetype=bam
Illumina HiSeq 2500 61
EGAD00001004817
68 samples; filetype=bam
Illumina HiSeq 2500 68
EGAD00001004818
71 samples; filetype=bam
Illumina HiSeq 2500 71
EGAD00001004819
48 samples; filetype=bam
Illumina HiSeq 2500 48
EGAD00001004820
52 samples; filetype=bam
Illumina HiSeq 2500 52
EGAD00001004821
60 samples; filetype=bam
Illumina HiSeq 2500 60
EGAD00001004822
55 samples; filetype=bam
Illumina HiSeq 2500 55

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