Validation of noise models for single-cell transcriptomics
Abstract
Single-cell transcriptomics has recently emerged as a powerful technology to explore gene expression heterogeneity among single cells. Here we identify two major sources of technical variability: sampling noise and global cell-to-cell variation in sequencing efficiency. We propose noise models to correct for this, which we validate using single-molecule FISH. We demonstrate that gene expression variability in mouse embryonic stem cells depends on the culture condition.
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Count data is overdispersed in both bulk and single-cell RNA-seq.
“This is consistent with previous observations in both bulk and single-cell RNA-seq that count data is overdispersed [9, 12, 14, 28].”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [14]
By counting molecules rather than the number of amplified sequencing reads, one can account for biases related to amplification and obtain more accurate estimates of gene expression levels.
“The rationale is that by counting molecules rather than the number of amplified sequencing reads, one can account for biases related to amplification, and obtain more accurate estimates of gene expression levels71220.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [12]
These observations underscore the importance of using UMIs in single cell gene expression studies.
“These observations concur with previous studies1220 as they underscore the importance of using UMIs in single cell gene expression studies.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [12]
Protein abundance is often challenging to estimate via scRNA-seq data due to post-transcriptional regulation and relatively low RNA capture rates.
“Protein abundance is often challenging to estimate via scRNA-seq data due to post-transcriptional regulation and relatively low RNA capture rates10,11.”
From NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells (2022) · cites this paper as [10]
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UMI-count modeling and differential expression analysis for single-cell RNA sequencing
Chen, W., Li, Y., Easton, J., Finkelstein, D., Wu, G., & Chen, X. (2018) · Genome Biology · doi:10.1186/s13059-018-1438-9 · Cited by 113 · see citing works
Delmans, M., & Hemberg, M. (2015) · doi:10.1101/020735 · Cited by 111 · see citing works
Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods
Dal Molin, A., Baruzzo, G., & Di Camillo, B. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00062 · Cited by 107 · see citing works
Exploiting single-cell expression to characterize co-expression replicability
Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2016) · Genome Biology · doi:10.1186/s13059-016-0964-6 · Cited by 76 · see citing works
Improved reproducibility by assuring confidence in measurements in biomedical research
Plant, A. L., Locascio, L. E., May, W. E., & Gallagher, P. D. (2014) · Nature Methods · doi:10.1038/nmeth.3076 · Cited by 89 · see citing works
Hong, R., Koga, Y., Bandyadka, S., Leshchyk, A., Wang, Y., Akavoor, V., Cao, X., Sarfraz, I., Wang, Z., Alabdullatif, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29212-9 · Cited by 82 · see citing works
Discovery and characterization of variance QTLs in human induced pluripotent stem cells
Sarkar, A. K., Tung, P.-Y., Blischak, J. D., Burnett, J. E., Li, Y. I., Stephens, M., & Gilad, Y. (2019) · PLOS Genetics · doi:10.1371/journal.pgen.1008045 · Cited by 72 · see citing works
Single-nucleus RNA-seq2 reveals functional crosstalk between liver zonation and ploidy
Richter, M. L., Deligiannis, I. K., Yin, K., Danese, A., Lleshi, E., Coupland, P., Vallejos, C. A., Matchett, K. P., Henderson, N. C., Colome-Tatche, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24543-5 · Cited by 75 · see citing works
Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing
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- Authors
- Grün, D., Kester, L., & van Oudenaarden, A.
- Affiliations
- Royal Netherlands Academy of Arts and Sciences (NL)
- Venue
- Nature Methods
- Published
- 2014-04-20
- Volume
- 11
- Issue
- 6
- Pages
- 637-640
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W1981641790
- Cited by
- 721
- References
- 25
- Citations by year
- 2026: 20 · 2025: 47 · 2024: 48 · 2023: 50 · 2022: 83 · 2021: 82 · 2020: 68 · 2019: 78 · 2018: 72 · 2017: 55 · 2016: 64 · 2015: 45 · 2014: 9
- FWCI
- 23.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- KNAW research portal (Royal Academy of Art and Sciences (KNAW)) (submittedVersion)
- 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 › Gene Regulatory Network Analysis
- Keywords
- noise models, single-cell RNA sequencing, technical variability, sampling noise, sequencing efficiency, cell-to-cell variability, gene expression heterogeneity, single-molecule FISH, mouse embryonic stem cells, culture conditions, biological variation
- MeSH terms
- Animals, Gene Expression Regulation, Models, Biological, Transcription Factors, Observer Variation, Selection Bias, Sequence Analysis, RNA, Gene Expression Profiling, Mice, Embryonic Stem Cells, Signal-To-Noise Ratio
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