Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data
Camila P. E. de Souza, Mirela Andronescu, Tehmina Masud, Farhia Kabeer, Justina Biele, Emma Laks, Daniel Lai, Patricia Ye, Jazmine Brimhall, Beixi Wang, Edmund Su, Tony Hui, Qi Cao, Marcus Wong, Michelle Moksa, Richard A. Moore, Martin Hirst, Samuel Aparício, Sohrab P. Shah
Abstract
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 https://github.com/shahcompbio/Epiclomal.