ArticleBioinformatics (Oxford, England)2021
doubletD: detecting doublets in single-cell DNA sequencing data.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Evolution and Co-occurrence of PI3K Pathway Gene Mutations in Endometrial Carcinoma Molecular Subtypes at the Single-Cell Level.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Article
- Advances and applications in single-cell and spatial genomics.Science China. Life sciences · 2025Review
- Mouse-Geneformer: A deep learning model for mouse single-cell transcriptome and its cross-species utility.PLoS genetics · 2025Article
- Application and research progress of single cell sequencing technology in leukemia.Frontiers in oncology · 2024Review
- Multi-omics in exploring the pathophysiology of diabetic retinopathy.Frontiers in cell and developmental biology · 2024Review
- Quartets enable statistically consistent estimation of cell lineage trees under an unbiased error and missingness model.Algorithms for molecular biology : AMB · 2023Article
- ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model.Genome biology · 2023Article
- Review
- Molecular Characterization of Acquired Resistance to KRASG12C-EGFR Inhibition in Colorectal Cancer.Cancer discovery · 2023Article
- ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model.bioRxiv : the preprint server for biology · 2023Article
- CNAViz: An interactive webtool for user-guided segmentation of tumor DNA sequencing data.PLoS computational biology · 2022Article
- CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data.Genome biology · 2022Article
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3 authors.
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Abstract
motivationWhile 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.
resultsWe 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.
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