{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T08:50:52Z","timestamp":1772614252952,"version":"3.50.1"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T00:00:00Z","timestamp":1594598400000},"content-version":"vor","delay-in-days":12,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>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 (&amp;lt;0.5\u00d7 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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\u00d7. 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 \u22480.03\u00d7, 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 \u22480.5\u00d7, SBMClone shows that tumor cells are present in the post-treatment sample, contrary to published analysis of this dataset.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>SBMClone is available on the GitHub repository https:\/\/github.com\/raphael-group\/SBMClone.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa449","type":"journal-article","created":{"date-parts":[[2020,6,4]],"date-time":"2020-06-04T19:13:59Z","timestamp":1591298039000},"page":"i186-i193","source":"Crossref","is-referenced-by-count":24,"title":["Identifying tumor clones in sparse single-cell mutation data"],"prefix":"10.1093","volume":"36","author":[{"given":"Matthew A","family":"Myers","sequence":"first","affiliation":[{"name":"Department of Computer Science, Princeton University , Princeton, NJ 08544, USA"}]},{"given":"Simone","family":"Zaccaria","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Princeton University , Princeton, NJ 08544, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1274-048X","authenticated-orcid":false,"given":"Benjamin J","family":"Raphael","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Princeton University , Princeton, NJ 08544, USA"}]}],"member":"286","published-online":{"date-parts":[[2020,7,13]]},"reference":[{"key":"2024021913321818100_btaa449-B1","year":"2019"},{"key":"2024021913321818100_btaa449-B2","first-page":"6446","article-title":"Community detection and stochastic block models: recent developments","volume":"18","author":"Abbe","year":"2017","journal-title":"J. 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