Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
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Abstract
Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. We have developed "scone"- a flexible framework for assessing performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes trade-offs and ranks large numbers of normalization methods by panel performance. The method is implemented in the open-source Bioconductor R software package scone. We show that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets. scone can be downloaded at http://bioconductor.org/packages/scone/.
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Non-linear normalization methods can account for more complex unwanted variation.
“Non‐linear normalization methods, which can account for more complex unwanted variation, also exist (Cole et al, 2019).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Cole et al, 2019
Non-linear normalization methods have been shown to outperform global scaling methods especially in situations with strong batch effects.
“Non‐linear normalization methods have been shown to outperform global scaling methods especially in situations with strong batch effects (Cole et al, 2019).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Cole et al, 2019
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- Authors
- Cole, M. B., Risso, D., Wagner, A., DeTomaso, D., Ngai, J., Purdom, E., Dudoit, S., & Yosef, N.
- Affiliations
- University of California, Berkeley (US)
- University of Padua (IT)
- Corresponding author
- Cole, Michael B.
- Risso, Davide
- Yosef, Nir
- ORCID
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 8
- Issue
- 4
- Pages
- 315-328.e8
- ISSN
- 2405-4712 (ISSN-L)
- PMC
- PMC6544759
- OpenAlex
- W2951444558
- Cited by
- 173
- References
- 62
- Citations by year
- 2026: 6 · 2025: 18 · 2024: 22 · 2023: 20 · 2022: 26 · 2021: 29 · 2020: 23 · 2019: 22 · 2018: 7
- FWCI
- 8.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by-nc-nd)
- Funders
- National Institutes of Health (U01 MH105979, U19 MH114830)
- Chan Zuckerberg Initiative
- Silicon Valley Community Foundation
- Ministero dell’Istruzione, dell’Università e della Ricerca
- National Institute of Dental and Craniofacial Research (F31 DE025176)
- 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, normalization, performance evaluation, measurement bias, scones, R/Bioconductor package
- MeSH terms
- RNA-Seq, Calibration, Data Interpretation, Statistical, Software
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