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Title: doubletD: detecting doublets in single-cell DNA sequencing data
Abstract Motivation 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. Results 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. Availability and implementation https://github.com/elkebir-group/doubletD. Supplementary information Supplementary data are available at Bioinformatics online.  more » « less
Award ID(s):
1850502 2046488
PAR ID:
10289269
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Bioinformatics
Volume:
37
Issue:
Supplement_1
ISSN:
1367-4803
Page Range / eLocation ID:
i214 to i221
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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