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Identifying tumor clones in sparse single-cell mutation data.

Author(s): Myers, Matthew A; Zaccaria, Simone; Raphael, Benjamin J

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Abstract: MOTIVATION:Recent single-cell DNA sequencing technologies enable whole-genome sequencing of hundreds to thousands of individual cells. However, these technologies have ultra-low sequencing coverage (<0.5× per cell) which has limited their use to the analysis of large copy-number aberrations (CNAs) in individual cells. While CNAs are useful markers in cancer studies, single-nucleotide mutations are equally important, both in cancer studies and in other applications. However, ultra-low coverage sequencing yields single-nucleotide mutation data that are too sparse for current single-cell analysis methods. RESULTS:We introduce SBMClone, a method to infer clusters of cells, or clones, that share groups of somatic single-nucleotide mutations. SBMClone uses a stochastic block model to overcome sparsity in ultra-low coverage single-cell sequencing data, and we show that SBMClone accurately infers the true clonal composition on simulated datasets with coverage at low as 0.2×. We applied SBMClone to single-cell whole-genome sequencing data from two breast cancer patients obtained using two different sequencing technologies. On the first patient, sequenced using the 10X Genomics CNV solution with sequencing coverage ≈0.03×, SBMClone recovers the major clonal composition when incorporating a small amount of additional information. On the second patient, where pre- and post-treatment tumor samples were sequenced using DOP-PCR with sequencing coverage ≈0.5×, SBMClone shows that tumor cells are present in the post-treatment sample, contrary to published analysis of this dataset. AVAILABILITY AND IMPLEMENTATION:SBMClone is available on the GitHub repository SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
Publication Date: Jul-2020
Citation: Myers, Matthew A, Zaccaria, Simone, Raphael, Benjamin J. (2020). Identifying tumor clones in sparse single-cell mutation data.. Bioinformatics (Oxford, England), 36 (Supplement_1), i186 - i193. doi:10.1093/bioinformatics/btaa449
DOI: doi:10.1093/bioinformatics/btaa449
ISSN: 1367-4803
EISSN: 1367-4811
Pages: i186 - i193
Type of Material: Journal Article
Journal/Proceeding Title: Bioinformatics (Oxford, England)
Version: Final published version. This is an open access article.

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