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DoubletDecon: Cell-State Aware Removal of Single-Cell RNA-Seq Doublets

Erica A.K. DePasquale, Daniel J. Schnell, Íñigo Valiente-Alandí, Burns C. Blaxall, View ORCID ProfileH. Leighton Grimes, Harinder Singh, View ORCID ProfileNathan Salomonis
doi: https://doi.org/10.1101/364810
Erica A.K. DePasquale
1Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
2Department of Biomedical Informatics, University of Cincinnati, Cincinnati, OH
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Daniel J. Schnell
1Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
3Heart Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
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Íñigo Valiente-Alandí
3Heart Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
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Burns C. Blaxall
3Heart Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
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H. Leighton Grimes
4Division of Immunobiology and Center for Systems Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
5Bone Marrow Transplantation & Immune Deficiency, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
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Harinder Singh
4Division of Immunobiology and Center for Systems Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
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Nathan Salomonis
1Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
2Department of Biomedical Informatics, University of Cincinnati, Cincinnati, OH
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  • ORCID record for Nathan Salomonis
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ABSTRACT

Technologies and analytical methods for single-cell RNA sequencing (scRNA-Seq) have greatly advanced in recent years. While droplet- and well-based methods have significantly increased the isolation of cells for scRNA-Seq analysis, these technologies readily produce technical artifacts, such as doublet-cell and multiplet-cell captures. Doublets occurring between distinct cell-types can appear as hybrid scRNA-Seq profiles, but do not have distinct transcriptomes from individual cell states. Traditional approaches for detecting doublets, such as assessing the number of sequencing reads, fall short when different cell types with differing levels of transcriptional activity or library amplification efficiency are sequenced. We introduce DoubletDecon (https://github.com/EDePasquale/DoubletDecon). an approach that detects doublets with a combination of deconvolution analyses and the identification of unique cell-state gene expression. We demonstrate the ability of DoubletDecon to identify synthetic and cell-hashing cell singlets and doublets from scRNA-Seq datasets of varying cellular complexity. DoubletDecon is able to account for cell-cycle effects, and is compatible with diverse species and unsupervised population detection algorithms (e.g., ICGS, Seurat). We believe this approach has the potential to become a standard quality control step for the accurate delineation of cell states.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted July 08, 2018.
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DoubletDecon: Cell-State Aware Removal of Single-Cell RNA-Seq Doublets
Erica A.K. DePasquale, Daniel J. Schnell, Íñigo Valiente-Alandí, Burns C. Blaxall, H. Leighton Grimes, Harinder Singh, Nathan Salomonis
bioRxiv 364810; doi: https://doi.org/10.1101/364810
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DoubletDecon: Cell-State Aware Removal of Single-Cell RNA-Seq Doublets
Erica A.K. DePasquale, Daniel J. Schnell, Íñigo Valiente-Alandí, Burns C. Blaxall, H. Leighton Grimes, Harinder Singh, Nathan Salomonis
bioRxiv 364810; doi: https://doi.org/10.1101/364810

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