Normalizing and denoising protein expression data from droplet-based single cell profiling
Open accessPublished version of a preprint
Abstract
Multimodal single-cell profiling methods that measure protein expression with oligo-conjugated antibodies hold promise for comprehensive dissection of cellular heterogeneity, yet the resulting protein counts have substantial technical noise that can mask biological variations. Here we integrate experiments and computational analyses to reveal two major noise sources and develop a method called "dsb" (denoised and scaled by background) to normalize and denoise droplet-based protein expression data. We discover that protein-specific noise originates from unbound antibodies encapsulated during droplet generation; this noise can thus be accurately estimated and corrected by utilizing protein levels in empty droplets. We also find that isotype control antibodies and the background protein population average in each cell exhibit significant correlations across single cells, we thus use their shared variance to correct for cell-to-cell technical noise in each cell. We validate these findings by analyzing the performance of dsb in eight independent datasets spanning multiple technologies, including CITE-seq, ASAP-seq, and TEA-seq. Compared to existing normalization methods, our approach improves downstream analyses by better unmasking biologically meaningful cell populations. Our method is available as an open-source R package that interfaces easily with existing single cell software platforms such as Seurat, Bioconductor, and Scanpy and can be accessed at "dsb [ https://cran.r-project.org/package=dsb ]".
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DSB uses background droplets representing protein background noise to correct values in cells while removing cell-to-cell variation by combining isotype control levels with the specific background level of each cell.
“DSB uses background droplets that represent protein background noise to correct values in cells while removing cell-to-cell variation by combining isotype control levels with the specific background level of the respective cell.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [161]
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Mutation-dependent responses to sleep and exercise in clonal haematopoiesis
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Panpipes: a pipeline for multiomic single-cell and spatial transcriptomic data analysisPreprint
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- Authors
- Mulè, M. P., Martins, A. J., & Tsang, J. S.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Tsang, John S.
- Venue
- Nature Communications
- Published
- 2022-04-19
- Volume
- 13
- Issue
- 1
- Pages
- 2099-2099
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC9018908
- OpenAlex
- W4225731526
- Cited by
- 197
- References
- 65
- Citations by year
- 2026: 39 · 2025: 43 · 2024: 41 · 2023: 36 · 2022: 27 · 2021: 10 · 2020: 1
- FWCI
- 12.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, public-domain)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo
- Apollo (cc-by)
- Funders
- Johns Hopkins University
- National Institutes of Health
- National Institute of Allergy and Infectious Diseases (ZIAAI001152)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Engineering › Biomedical Engineering › Innovative Microfluidic and Catalytic Techniques Innovation
- Keywords
- data denoising, CITE-seq, cellular heterogeneity
- MeSH terms
- Software, Gene Expression Profiling, Single-Cell Analysis
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