SoupX removes ambient RNA contamination from droplet based single-cell RNA sequencing data
Open accessPreprint — published as 10.1093/gigascience/giaa151 · Paper page
Abstract
Background: Droplet based single-cell RNA sequence analyses assume all acquired RNAs are endogenous to cells. However, any cell free RNAs contained within the input solution are also captured by these assays. This sequencing of cell free RNA constitutes a background contamination that confounds the biological interpretation of single-cell transcriptomic data. Results: We demonstrate that contamination from this ‘soup’ of cell free RNAs is ubiquitous, with experiment-specific variations in composition and magnitude. We present a method, SoupX, for quantifying the extent of the contamination and estimating ‘background corrected’ cell expression profiles that seamlessly integrate with existing downstream analysis tools. Applying this method to several datasets using multiple droplet sequencing technologies, we demonstrate that its application improves biological interpretation of otherwise misleading data, as well as improving quality control metrics. Conclusions: We present ‘SoupX’, a tool for removing ambient RNA contamination from droplet based single cell RNA sequencing experiments. This tool has broad applicability and its application can improve the biological utility of existing and future data sets. Key Points: The signal from droplet based single cell RNA sequencing is ubiquitously contaminated by capture of ambient mRNA. SoupX is a method to quantify the abundance of these ambient mRNAs and remove them. Correcting for ambient mRNA contamination improves biological interpretation.
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Ambient RNA shifts the observed allele fraction away from the underlying genotype allele fractions.
“The ambient RNA shifts the observed allele fraction away from the underlying genotype allele fractions9.”
From Souporcell: robust clustering of single-cell RNA-seq data by genotype without reference genotypes (2020) · cites this paper as [9]
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- Authors
- Young, M. D., & Behjati, S.
- Affiliations
- Wellcome Sanger Institute (GB)
- University of Cambridge (GB)
- Corresponding author
- Young, Matthew D
- Venue
- GigaScience
- Publisher
- openRxiv
- Published
- 2018-04-20
- Volume
- 9
- Issue
- 12
- ISSN
- 2047-217X (ISSN-L)
- PMC
- PMC7763177
- OpenAlex
- W3114078084
- Cited by
- 1,648
- References
- 0
- Citations by year
- 2026: 386 · 2025: 388 · 2024: 347 · 2023: 212 · 2022: 150 · 2021: 85 · 2020: 54 · 2019: 24 · 2018: 1
- FWCI
- 41.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- GigaScience (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (110104/Z/15/Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- cell-free RNA, background correction, single-cell transcriptomics, quality control, contamination quantification
- MeSH terms
- Base Sequence, RNA, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, Transcriptome
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