{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T02:35:09Z","timestamp":1773282909917,"version":"3.50.1"},"reference-count":35,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF 1850502"],"award-info":[{"award-number":["CCF 1850502"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CCF 2046488"],"award-info":[{"award-number":["CCF 2046488"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>While single-cell DNA sequencing (scDNA-seq) has enabled the study of intratumor heterogeneity at an unprecedented resolution, current technologies are error-prone and often result in doublets where two or more cells are mistaken for a single cell. Not only do doublets confound downstream analyses, but the increase in doublet rate is also a major bottleneck preventing higher throughput with current single-cell technologies. Although doublet detection and removal are standard practice in scRNA-seq data analysis, options for scDNA-seq data are limited. Current methods attempt to detect doublets while also performing complex downstream analyses tasks, leading to decreased efficiency and\/or performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We present doubletD, the first standalone method for detecting doublets in scDNA-seq data. Underlying our method is a simple maximum likelihood approach with a closed-form solution. We demonstrate the performance of doubletD on simulated data as well as real datasets, outperforming current methods for downstream analysis of scDNA-seq data that jointly infer doublets as well as standalone approaches for doublet detection in scRNA-seq data. Incorporating doubletD in scDNA-seq analysis pipelines will reduce complexity and lead to more accurate results.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/elkebir-group\/doubletD.<\/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\/btab266","type":"journal-article","created":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T00:04:18Z","timestamp":1619136258000},"page":"i214-i221","source":"Crossref","is-referenced-by-count":23,"title":["doubletD: detecting doublets in single-cell DNA sequencing data"],"prefix":"10.1093","volume":"37","author":[{"given":"Leah L","family":"Weber","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Illinois at Urbana-Champaign , Urbama, IL 61801, USA"}]},{"given":"Palash","family":"Sashittal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Illinois at Urbana-Champaign , Urbama, IL 61801, USA"},{"name":"Department of Aerospace Engineering, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USA"}]},{"given":"Mohammed","family":"El-Kebir","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Illinois at Urbana-Champaign , Urbama, IL 61801, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,7,12]]},"reference":[{"key":"2023062410172285600_btab266-B1","doi-asserted-by":"crossref","first-page":"317","DOI":"10.3389\/fgene.2019.00317","article-title":"Single-cell RNA-seq technologies and related computational data analysis","volume":"10","author":"Chen","year":"2019","journal-title":"Front. 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