RT Journal Article
SR Electronic
T1 Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 414482
DO 10.1101/414482
A1 Camila P.E. de Souza
A1 Mirela Andronescu
A1 Tehmina Masud
A1 Farhia Kabeer
A1 Justina Biele
A1 Emma Laks
A1 Daniel Lai
A1 Patricia Ye
A1 Jazmine Brimhall
A1 Beixi Wang
A1 Edmund Su
A1 Tony Hui
A1 Qi Cao
A1 Marcus Wong
A1 Michelle Moksa
A1 Richard A. Moore
A1 Martin Hirst
A1 Samuel Aparicio
A1 Sohrab P. Shah
YR 2020
UL http://biorxiv.org/content/early/2020/02/14/414482.abstract
AB 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.