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Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data
Camila P E de Souza1, Mirela Andronescu2,3, Tehmina Masud2,3
1Department of Statistical and Actuarial Sciences, University of Western Ontario, London, ON, Canada.
Plos Computational Biology
|September 23, 2020
Summary
Epiclomal is a new computational method that clusters single-cell DNA methylation data and fills in missing values. It identifies sub-clonal methylation patterns in tumors, offering new ways to analyze cancer evolution.
Area of Science:
- Computational Biology
- Genomics
- Cancer Research
Background:
- Single-cell DNA methylation data is sparse and contains many missing values.
- Existing clustering methods struggle with the high dimensionality and missing data in single-cell CpG datasets.
- Understanding clonal heterogeneity in tumors is crucial for cancer research.
Purpose of the Study:
- To develop a probabilistic clustering method for simultaneous clustering and imputation of sparse single-cell DNA methylation data.
- To evaluate the performance of the new method against existing approaches using synthetic and real datasets.
- To apply the method to discover novel sub-clonal methylation patterns in cancer genomes.
Main Methods:
- Developed Epiclomal, a probabilistic clustering method based on a hierarchical mixture model.
- Applied Epiclomal to synthetic and published single-cell CpG datasets.
- Utilized newly generated single-cell 5mCpG sequencing data from aneuploid tumor genomes.
Main Results:
- Epiclomal outperforms non-probabilistic methods in clustering and imputing sparse single-cell DNA methylation data.
- The method effectively handles the missing data characteristic of single-cell CpG sequences.
- Epiclomal identified sub-clonal methylation patterns ('epiclones') in aneuploid tumor genomes, revealing novel clonal structures.
Conclusions:
- Epiclomal provides a robust solution for analyzing sparse single-cell DNA methylation data, including imputation of missing values.
- The discovered epiclones offer a new dimension for clonal analysis in cancer, complementing copy number-based lineage tracing.
- This method advances the understanding of epigenetic heterogeneity and clonal evolution in cancer.
