TY - JOUR T1 - Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data JF - bioRxiv DO - 10.1101/414482 SP - 414482 AU - Camila P.E. de Souza AU - Mirela Andronescu AU - Tehmina Masud AU - Farhia Kabeer AU - Justina Biele AU - Emma Laks AU - Daniel Lai AU - Patricia Ye AU - Jazmine Brimhall AU - Beixi Wang AU - Edmund Su AU - Tony Hui AU - Qi Cao AU - Marcus Wong AU - Michelle Moksa AU - Richard A. Moore AU - Martin Hirst AU - Samuel Aparicio AU - Sohrab P. Shah Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/02/21/414482.abstract N2 - 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 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. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal. ER -