DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data

Abstract Background Advances in droplet-based single-cell RNA-sequencing (scRNA-seq) have dramatically increased throughput, allowing tens of thousands of cells to be routinely sequenced in a single experiment. In addition to cells, droplets capture cell-free “ambient” RNA predominantly caused by ly...

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Autores principales:	Walter Muskovic, Joseph E. Powell
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Biology (General) QH301-705.5 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/87314464a784446a98f63d2b1ca32ddb
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spelling	oai:doaj.org-article:87314464a784446a98f63d2b1ca32ddb2021-12-05T12:25:33ZDropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data10.1186/s13059-021-02547-01474-760Xhttps://doaj.org/article/87314464a784446a98f63d2b1ca32ddb2021-12-01T00:00:00Zhttps://doi.org/10.1186/s13059-021-02547-0https://doaj.org/toc/1474-760XAbstract Background Advances in droplet-based single-cell RNA-sequencing (scRNA-seq) have dramatically increased throughput, allowing tens of thousands of cells to be routinely sequenced in a single experiment. In addition to cells, droplets capture cell-free “ambient” RNA predominantly caused by lysis of cells during sample preparation. Samples with high ambient RNA concentration can create challenges in accurately distinguishing cell-containing droplets and droplets containing ambient RNA. Current methods to separate these groups often retain a significant number of droplets that do not contain cells or empty droplets. Additionally, there are currently no methods available to detect droplets containing damaged cells, which comprise partially lysed cells, the original source of the ambient RNA. Results Here, we describe DropletQC, a new method that is able to detect empty droplets, damaged, and intact cells, and accurately distinguish them from one another. This approach is based on a novel quality control metric, the nuclear fraction, which quantifies for each droplet the fraction of RNA originating from unspliced, nuclear pre-mRNA. We demonstrate how DropletQC provides a powerful extension to existing computational methods for identifying empty droplets such as EmptyDrops. Conclusions We implement DropletQC as an R package, which can be easily integrated into existing single-cell analysis workflows.Walter MuskovicJoseph E. PowellBMCarticleBiology (General)QH301-705.5GeneticsQH426-470ENGenome Biology, Vol 22, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5 Genetics QH426-470
spellingShingle	Biology (General) QH301-705.5 Genetics QH426-470 Walter Muskovic Joseph E. Powell DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data
description	Abstract Background Advances in droplet-based single-cell RNA-sequencing (scRNA-seq) have dramatically increased throughput, allowing tens of thousands of cells to be routinely sequenced in a single experiment. In addition to cells, droplets capture cell-free “ambient” RNA predominantly caused by lysis of cells during sample preparation. Samples with high ambient RNA concentration can create challenges in accurately distinguishing cell-containing droplets and droplets containing ambient RNA. Current methods to separate these groups often retain a significant number of droplets that do not contain cells or empty droplets. Additionally, there are currently no methods available to detect droplets containing damaged cells, which comprise partially lysed cells, the original source of the ambient RNA. Results Here, we describe DropletQC, a new method that is able to detect empty droplets, damaged, and intact cells, and accurately distinguish them from one another. This approach is based on a novel quality control metric, the nuclear fraction, which quantifies for each droplet the fraction of RNA originating from unspliced, nuclear pre-mRNA. We demonstrate how DropletQC provides a powerful extension to existing computational methods for identifying empty droplets such as EmptyDrops. Conclusions We implement DropletQC as an R package, which can be easily integrated into existing single-cell analysis workflows.
format	article
author	Walter Muskovic Joseph E. Powell
author_facet	Walter Muskovic Joseph E. Powell
author_sort	Walter Muskovic
title	DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data
title_short	DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data
title_full	DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data
title_fullStr	DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data
title_full_unstemmed	DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data
title_sort	dropletqc: improved identification of empty droplets and damaged cells in single-cell rna-seq data
publisher	BMC
publishDate	2021
url	https://doaj.org/article/87314464a784446a98f63d2b1ca32ddb
work_keys_str_mv	AT waltermuskovic dropletqcimprovedidentificationofemptydropletsanddamagedcellsinsinglecellrnaseqdata AT josephepowell dropletqcimprovedidentificationofemptydropletsanddamagedcellsinsinglecellrnaseqdata
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DropletQC: improved identification of empty droplets and damaged cells in single-cell RNA-seq data

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