Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data.
PLoS Comput Biol
; 16(9): e1008270, 2020 09.
Article
em En
| MEDLINE
| ID: mdl-32966276
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Metilação de DNA
/
Análise de Célula Única
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article