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.

Molecular Characterization of Acquired Resistance to KRASG12C–EGFR Inhibition in Colorectal Cancer

“…Genotyping calls were further examined and corrected according to variant allele frequency. Potential doublets or multiplets characterized by the existence of 2 or more cells that are captured within a droplet and linked to a single barcode were identified using DoubletD ( 47 ) and further excluded from the analysis. For genotype clustering analysis of the five known variants ( KRAS G12C , NRAS G12D , APC H1490Lfs*17 , ERBB3 V104M , and APC Q879* ), cells were included when these five variants met the criteria of read depth (≥10) and genotyping quality (≥60; ref.…”

ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model

“…Considering that the cells and mutations in each cell are sequenced independently, the likelihood of observing the variant read count matrix Q for given total read count matrix R and mutation matrix A can be written as follows. We model the observed variant read counts q i,j using a beta-binomial, similar to previous work [33, 43, 44]. The “Methods” section provides the details about the read count model.…”

CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data

“…As expected, and as shown in previous studies (e.g., [ 26 , 28 ]), cell doublets can be detrimental for single-cell phylogenetic reconstruction. When doublets are likely to occur in the data, specific scDNA-seq doublet detection methods might be used to remove suspicious cells [ 71 ].…”