Pooling across cells to normalize single-cell RNA sequencing data with many zero counts
Open accessAbstract
Normalization of single-cell RNA sequencing data is necessary to eliminate cell-specific biases prior to downstream analyses. However, this is not straightforward for noisy single-cell data where many counts are zero. We present a novel approach where expression values are summed across pools of cells, and the summed values are used for normalization. Pool-based size factors are then deconvolved to yield cell-based factors. Our deconvolution approach outperforms existing methods for accurate normalization of cell-specific biases in simulated data. Similar behavior is observed in real data, where deconvolution improves the relevance of results of downstream analyses.
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Pseudobulk analysis methods are among the best-performing methods for differential state analysis and for making sample-level comparisons from single-cell data, and help to overcome some of the inherent sparsity in scRNA-seq data.
“A recent comparative analysis found that pseudobulk analysis methods are among the best-performing methods for differential state analysis and for making sample-level comparisons from single-cell data, and help to overcome some of the inherent sparsity in scRNA-seq data19,89.”
From Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain (2022) · cites this paper as [89]
The Background says pooling counts across cells reduces problematic zeroes, supporting the sparsity point. But the paper evaluates pooled-count normalization and deconvolution, not pseudobulk methods for differential state analysis or sample-level comparisons; it does not establish that those methods are among the best-performing. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAggregation of cell-type-specific expression values within an individual by sum or mean is a suggested approach to account for within-individual correlation.
“However, one suggested approach to account for within-individual correlation is the aggregation of cell-type-specific expression values within an individual by using either a sum or a mean11–13.”
From A practical solution to pseudoreplication bias in single-cell studies (2021) · cites this paper as [11]
In Results and discussion, under “Summation and deconvolution with linear equations” (page unavailable), the paper sums expression across pools of cells and uses a mean across all cells as a reference. It does not describe aggregating cell-type-specific values within an individual to account for within-individual correlation; its stated purpose is normalization despite many zero counts. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMethod-specific genes are unlikely to remain method specific at higher expression levels, and their impact on conclusions drawn from scRNA-seq data is rather limited.
“This suggests that they are unlikely to remain method specific at higher expression levels and that their impact on conclusions drawn from scRNAseq data is rather limited (Lun et al., 2016).”
From Comparative Analysis of Single-Cell RNA Sequencing Methods (2017) · cites this paper as Lun et al., 2016
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- Authors
- L. Lun, A. T., Bach, K., & Marioni, J. C.
- Affiliations
- European Bioinformatics Institute (GB)
- Corresponding author
- Marioni, John C.
- Venue
- Genome Biology
- Published
- 2016-04-27
- Volume
- 17
- Issue
- 1
- Pages
- 75-75
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC4848819
- OpenAlex
- W2343956310
- Cited by
- 1,108
- References
- 27
- Citations by year
- 2026: 58 · 2025: 95 · 2024: 129 · 2023: 135 · 2022: 134 · 2021: 149 · 2020: 146 · 2019: 102 · 2018: 93 · 2017: 49 · 2016: 17
- FWCI
- 45.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Genome biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Funders
- Cancer Research UK (GB) (SW73)
- 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 normalization, zero counts, size factor, deconvolution
- MeSH terms
- Algorithms, Animals, Calibration, Humans, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, Signal-To-Noise Ratio
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