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doubletD: detecting doublets in single-cell DNA sequencing data
Author(s) -
Leah L. Weber,
Palash Sashittal,
Mohammed El-Kebir
Publication year - 2021
Publication title -
bioinformatics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab266
Subject(s) - bottleneck , computer science , downstream (manufacturing) , data mining , throughput , current (fluid) , pipeline (software) , algorithm , embedded system , telecommunications , programming language , operations management , electrical engineering , economics , wireless , engineering
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.

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