Single-cell RNA-seq denoising using a deep count autoencoder
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Abstract
Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNA-seq data are needed. We propose a deep count autoencoder network (DCA) to denoise scRNA-seq datasets. DCA takes the count distribution, overdispersion and sparsity of the data into account using a negative binomial noise model with or without zero-inflation, and nonlinear gene-gene dependencies are captured. Our method scales linearly with the number of cells and can, therefore, be applied to datasets of millions of cells. We demonstrate that DCA denoising improves a diverse set of typical scRNA-seq data analyses using simulated and real datasets. DCA outperforms existing methods for data imputation in quality and speed, enhancing biological discovery.
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Statistical and deep-learning methods designed for single-cell RNA-seq data often utilize a negative binomial (or zero-inflated negative binomial) error model.
“Importantly, statistical and deep-learning methods designed for single-cell RNA-seq data often utilize a negative binomial (or zero-inflated negative binomial) error model [10, 15].”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [15]
Performing expression recovery has been shown to improve the estimation of gene-gene correlations.
“Performing expression recovery has been shown to improve the estimation of gene–gene correlations (van Dijk et al, 2018; Eraslan et al, 2019).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Eraslan et al, 2019
First applications of deep learning to scRNA-seq are starting to emerge from dimensionality reduction to denoising.
“First applications to scRNA‐seq are starting to emerge from dimensionality reduction to denoising (e.g. scVis: Ding et al, 2018; scGen: preprint: Lotfollahi et al, 2018; DCA: Eraslan et al, 2019).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Eraslan et al, 2019
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Self-citationNicheformer: a foundation model for single-cell and spatial omics
Tejada-Lapuerta, A., Schaar, A. C., Gutgesell, R., Palla, G., Halle, L., Minaeva, M., Vornholz, L., Dony, L., Drummer, F., Richter, T., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02814-z · Cited by 100 · see citing works
Self-citationIsolating salient variations of interest in single-cell data with contrastiveVI
Weinberger, E., Lin, C., & Lee, S.-I. (2023) · Nature Methods · doi:10.1038/s41592-023-01955-3 · Cited by 66 · see citing works
Graph representation learning for single-cell biology
Hetzel, L., Fischer, D. S., Günnemann, S., & Theis, F. J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.05.008 · Cited by 55 · see citing works
Self-citationDoncevic, D., & Herrmann, C. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad387 · Cited by 51 · see citing works
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CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks
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Generative modeling of single-cell gene expression for dose-dependent chemical perturbations
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Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing
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Advancing biological understanding of cellular senescence with computational multiomics
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Sfaira accelerates data and model reuse in single cell genomics
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Self-citationSingle-cell and spatial detection of senescent cells using DeepScence
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Expression quantitative trait locus studies in the era of single-cell omics
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Computational challenges of cell cycle analysis using single cell transcriptomics
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- Authors
- Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S., & Theis, F. J.
- Affiliations
- Helmholtz Munich (DE)
- Corresponding author
- Theis, Fabian J.
- Venue
- Nature Communications
- Published
- 2019-01-23
- Volume
- 10
- Issue
- 1
- Pages
- 390-390
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6344535
- OpenAlex
- W2951381561
- Cited by
- 1,084
- References
- 60
- Citations by year
- 2026: 132 · 2025: 165 · 2024: 147 · 2023: 153 · 2022: 150 · 2021: 148 · 2020: 119 · 2019: 63 · 2018: 7
- FWCI
- 39.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- mediaTUM (Technical University of Munich) (submittedVersion, other-oa)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion, cc-by)
- Funders
- European Commission (753039, HORIZON2020)
- Deutsche Forschungsgemeinschaft (ZT-I-0007, Subproject A17)
- 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
- single-cell RNA sequencing, data denoising, overdispersion, excess zeros, count data, gene expression imputation, data sparsity
- MeSH terms
- Animals, Blood Cells, Gene Expression Regulation, Leukocytes, Mononuclear, Phenotype, RNA, Models, Statistical, Caenorhabditis elegans, Sequence Analysis, RNA, Computational Biology, RNA, Small Cytoplasmic, Gene Expression Profiling, Single-Cell Analysis
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