Rxivist logo

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>.

Download data

  • Downloaded 955 times
  • Download rankings, all-time:
    • Site-wide: 10,194 out of 77,682
    • In genomics: 1,490 out of 5,090
  • Year to date:
    • Site-wide: 8,669 out of 77,682
  • Since beginning of last month:
    • Site-wide: 15,022 out of 77,682

Altmetric data


Downloads over time

Distribution of downloads per paper, site-wide


PanLingua

Sign up for the Rxivist weekly newsletter! (Click here for more details.)


News