@article{weber_sashittal_el-kebir_2021,
title={doubletD: detecting doublets in single-cell DNA sequencing data},
volume={37},
DOI={10.1093/bioinformatics/btab266},
abstractNote={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.
},
number={Supplement_1},
publisher={Oxford University Press (OUP)},
author={Weber, Leah L and Sashittal, Palash and El-Kebir, Mohammed},
year={2021},
month={Jul}
}