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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 https://github.com/raphael-group/SBMClone. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.|
|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|
|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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