Accounting for technical noise in single-cell RNA-seq experiments
Open accessAbstract
Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.
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Existing approaches for identifying highly variable genes (HVG) ignore spatial information and hence do not measure spatial variability.
“However, existing approaches for identifying highly variable genes (HVG)9, as used for conventional scRNA-seq data, ignore spatial information and hence do not measure spatial variability (Fig. 1A).”
From SpatialDE: identification of spatially variable genes (2018) · cites this paper as [9]
These genes do not have high rank based on non-spatial HVG measures (including mean-CV2 relation or mean-dropout relation).
“Furthermore, these genes do not have high rank based on non-spatial HVG measures (including mean-CV2 relation9 or mean-dropout relation15, Supp. Fig. 8).”
From SpatialDE: identification of spatially variable genes (2018) · cites this paper as [9]
Both endogenous and ERCC spike-in control CV patterns approximately followed an over-dispersed Poisson distribution.
“Both endogenous and ERCC spike-in control CV patterns approximately followed an over-dispersed Poisson distribution (Supplementary Fig. S11), which is consistent with previous studies1120.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [11]
Methods that can quantify gene-level technical variation allow focus on biological variation that is likely to be of interest.
“Methods that can quantify gene-level technical variation allow us to focus on biological variation that is likely to be of interest [179].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [179]
The number of undetected transcripts is correlated with batch effects and other metrics of library quality, and weakly expressed transcripts are more frequently undetected.
“Indeed, the number of undetected transcripts is correlated to batch effects60 and other metrics of library quality, and transcripts that are weakly expressed when detected are also more frequently undetected1.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [60]
The main axes of variation in scRNA-seq data are often dominated by technical factors such as library complexity, proportion of reads aligned to the genome, and proportion of detected transcripts.
“The main axes of variation (i.e., first few principal components) in scRNA-seq data are often dominated by additional technical factors1,60, such as library complexity64, proportion of reads that were successfully aligned to the genome, and proportion of detected transcripts”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [60]
Addition of precisely quantified exogenous RNA species (spike-in genes) to each cell's lysate allows estimation of absolute amounts of RNA.
“The addition of precisely quantified exogenous RNA species (“spike‐in” genes) to each cell's lysate allows the estimation of absolute amounts of RNA (Brennecke et al, 2013).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Brennecke et al, 2013
Cell-type clustering performance can be improved by using only genes that vary more between cells than would be expected by chance.
“Cell‐type clustering performance can be improved by using only genes that vary more between cells than would be expected by chance (Brennecke et al, 2013), or by using “eigengenes” that explain variability in the data (e.g. derived via principal components analysis).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Brennecke et al, 2013
Log transformation mitigates (but does not remove) the mean-variance relationship in single-cell data.
“Secondly, log transformation mitigates (but does not remove) the mean–variance relationship in single‐cell data (Brennecke et al, 2013).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Brennecke et al, 2013
Extra Poisson variability includes biological variation and technical variation, and technical variation includes noise introduced by amplification.
“This extra Poisson variability includes biological variation (assumed to be the same across methods in our data) and technical variation, and the latter includes noise introduced by amplification (Brennecke et al., 2013; Gr € un et al., 2014; Stegle et al., 2015).”
From Comparative Analysis of Single-Cell RNA Sequencing Methods (2017) · cites this paper as Brennecke et al., 2013
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- Authors
- Brennecke, P., Anders, S., Kim, J. K., Kołodziejczyk, A. A., Zhang, X., Proserpio, V., Baying, B., Benes, V., Teichmann, S. A., Marioni, J. C., & Heisler, M. G.
- Affiliations
- European Molecular Biology Laboratory (DE)
- European Bioinformatics Institute (GB)
- MRC Laboratory of Molecular Biology (GB)
- The University of Sydney (AU)
- Corresponding author
- Marioni, John C
- Heisler, Marcus G
- ORCID
- Venue
- Nature Methods
- Published
- 2013-09-22
- Volume
- 10
- Issue
- 11
- Pages
- 1093-1095
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2025828346
- Cited by
- 979
- References
- 20
- Citations by year
- 2026: 34 · 2025: 43 · 2024: 63 · 2023: 68 · 2022: 88 · 2021: 94 · 2020: 102 · 2019: 114 · 2018: 95 · 2017: 103 · 2016: 79 · 2015: 58 · 2014: 35 · 2013: 3
- FWCI
- 25.98
- Type
- journal article
- Language
- en
- Update notices
- erratum (2014-01-30)
- Open access
- green
- www.nature.com
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- Institutional Research Information System University of Turin (University of Turin) (submittedVersion)
- Funders
- Biotechnology and Biological Sciences Research Council (1300642)
- 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, technical noise, spike-ins, biological variation, mRNA levels, Arabidopsis thaliana, Mus musculus
- MeSH terms
- Sequence Analysis, RNA, Single-Cell Analysis
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