SCnorm: robust normalization of single-cell RNA-seq data
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Abstract
The normalization of RNA-seq data is essential for accurate downstream inference, but the assumptions upon which most normalization methods are based are not applicable in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of single-cell RNA-seq data.
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SCnorm utilizes quantile regression to treat distinct gene groups separately, but ignores zero values which predominantly characterize droplet-based scRNA-seq.
“Their method utilizes quantile regression to treat distinct gene groups separately, but ignores zero values which predominantly characterize droplet-based scRNA-seq.”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [6]
The Abstract describes quantile regression and separate normalization of groups of genes. ONLINE METHODS > SCnorm defines the regression input as non-zero expression counts, supporting the statement that zero values are excluded from that step. Our copy does not establish that zeros predominantly characterize droplet-based scRNA-seq specifically. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe authors of SCnorm reached similar conclusions in the normalization of non-UMI-based single-cell RNA-seq data.
“Indeed, the authors of SCnorm [6] reached similar conclusions in the normalization of non-UMI-based single-cell RNA-seq data.”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [6]
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- Authors
- Bacher, R., Chu, L.-F., Leng, N., Gasch, A. P., Thomson, J. A., Stewart, R. M., Newton, M., & Kendziorski, C.
- Affiliations
- University of Wisconsin–Madison (US)
- Morgridge Institute for Research (US)
- Corresponding author
- Kendziorski, Christina
- ORCID
- Venue
- Nature Methods
- Published
- 2017-04-17
- Volume
- 14
- Issue
- 6
- Pages
- 584-586
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC5473255
- OpenAlex
- W2605611918
- Cited by
- 282
- References
- 27
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 19 · 2023: 34 · 2022: 26 · 2021: 44 · 2020: 48 · 2019: 51 · 2018: 28 · 2017: 12
- FWCI
- 15.53
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (NIH U54, AI117924)
- National Heart, Lung, and Blood Institute (T32HL083806)
- National Institute of General Medical Sciences (R01GM102756, R01GM083989)
- National Institute of Allergy and Infectious Diseases (U54AI117924)
- U.S. National Library of Medicine (T32LM012413)
- 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, differential gene expression, gene expression normalization
- MeSH terms
- Algorithms, Data Interpretation, Statistical, Reference Values, RNA, Software, Sequence Analysis, RNA, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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