@article {de Souza414482, author = {de Souza, Camila P.E. and Andronescu, Mirela and Masud, Tehmina and Kabeer, Farhia and Biele, Justina and Laks, Emma and Lai, Daniel and Brimhall, Jazmine and Wang, Beixi and Su, Edmund and Hui, Tony and Cao, Qi and Wong, Marcus and Moksa, Michelle and Moore, Richard A. and Hirst, Martin and Aparicio, Samuel and Shah, Sorab P.}, title = {Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data}, elocation-id = {414482}, year = {2018}, doi = {10.1101/414482}, publisher = {Cold Spring Harbor Laboratory}, abstract = {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.}, URL = {https://www.biorxiv.org/content/early/2018/09/12/414482}, eprint = {https://www.biorxiv.org/content/early/2018/09/12/414482.full.pdf}, journal = {bioRxiv} }