doubletD: detecting doublets in single-cell DNA sequencing data

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. Contact: melkebir@illinois.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Automated summary

The study introduces a standalone method, doubletD, for detecting doublets in scDNA-seq data, achieving high performance through a simple maximum likelihood approach that outperforms current methods. Applying doubletD in scDNA-seq analysis pipelines can reduce complexity and improve result accuracy.