Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender
Published version of a preprint
Abstract
Droplet-based single-cell assays, including single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), generate considerable background noise counts, the hallmark of which is nonzero counts in cell-free droplets and off-target gene expression in unexpected cell types. Such systematic background noise can lead to batch effects and spurious differential gene expression results. Here we develop a deep generative model based on the phenomenology of noise generation in droplet-based assays. The proposed model accurately distinguishes cell-containing droplets from cell-free droplets, learns the background noise profile and provides noise-free quantification in an end-to-end fashion. We implement this approach in the scalable and robust open-source software package CellBender. Analysis of simulated data demonstrates that CellBender operates near the theoretically optimal denoising limit. Extensive evaluations using real datasets and experimental benchmarks highlight enhanced concordance between droplet-based single-cell data and established gene expression patterns, while the learned background noise profile provides evidence of degraded or uncaptured cell types.
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CellBender analysis showed a minimal number of cells in the narrow transition zone in the authors' datasets compared with the greater number of cells within the broader transition zone observed in published samples.
“Analysis using CellBender (Fleming et al., 2023) also contrasted the minimal number of cells in the narrow transition zone in our datasets with the greater number of cells within the broader transition zone observed in the published samples (Figure 2—figure supplement 1E).”
From Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity (2025) · cites this paper as Fleming et al., 2023
CellBender v0.3.0 was applied to remove counts from ambient/background RNA.
“CellBender v0.3.0 (Fleming et al., 2023) was applied to remove counts from ambient/background RNA.”
From Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity (2025) · cites this paper as Fleming et al., 2023
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Influential
- Authors
- Fleming, S. J., Chaffin, M. D., Arduini, A., Akkad, A.-D., Banks, E., Marioni, J. C., Philippakis, A. A., Ellinor, P. T., & Babadi, M.
- Affiliations
- Broad Institute (US)
- European Bioinformatics Institute (GB)
- Corresponding author
- Fleming, Stephen J.
- Babadi, Mehrtash
- ORCID
- Venue
- Nature Methods
- Published
- 2023-08-07
- Volume
- 20
- Issue
- 9
- Pages
- 1323-1335
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W4385638791
- Cited by
- 867
- References
- 12
- Citations by year
- 2026: 329 · 2025: 300 · 2024: 180 · 2023: 57 · 2022: 1
- FWCI
- 77.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- background noise suppression, deep generative models, single-cell RNA sequencing, single-nucleus RNA sequencing, CITE-seq, batch effects, noise reduction, unsupervised denoising
- MeSH terms
- RNA, Small Nuclear, Software, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis
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