cgCorrect: a method to correct for confounding cell–cell variation due to cell growth in single-cell transcriptomics
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Abstract
Accessing gene expression at a single-cell level has unraveled often large heterogeneity among seemingly homogeneous cells, which remains obscured when using traditional population-based approaches. The computational analysis of single-cell transcriptomics data, however, still imposes unresolved challenges with respect to normalization, visualization and modeling the data. One such issue is differences in cell size, which introduce additional variability into the data and for which appropriate normalization techniques are needed. Otherwise, these differences in cell size may obscure genuine heterogeneities among cell populations and lead to overdispersed steady-state distributions of mRNA transcript numbers. We present cgCorrect, a statistical framework to correct for differences in cell size that are due to cell growth in single-cell transcriptomics data. We derive the probability for the cell-growth-corrected mRNA transcript number given the measured, cell size-dependent mRNA transcript number, based on the assumption that the average number of transcripts in a cell increases proportionally to the cell’s volume during the cell cycle. cgCorrect can be used for both data normalization and to analyze the steady-state distributions used to infer the gene expression mechanism. We demonstrate its applicability on both simulated data and single-cell quantitative real-time polymerase chain reaction (PCR) data from mouse blood stem and progenitor cells (and to quantitative single-cell RNA-sequencing data obtained from mouse embryonic stem cells). We show that correcting for differences in cell size affects the interpretation of the data obtained by typically performed computational analysis.
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Correcting for cell size via normalization, or dedicated tools such as cgCorrect, also partially corrects for cell cycle effects in scRNA-seq data.
“Thus, correcting for cell size via normalization, or dedicated tools such as cgCorrect (Blasi et al, 2017), also partially corrects for cell cycle effects in scRNA‐seq data.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Blasi et al, 2017
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558 · see citing works
Stochastic Gene Expression in a Single Cell
Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441 · see citing works
Controlling the false discovery rate in behavior genetics research
Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N., & Golani, I. (2001) · Behavioural Brain Research · doi:10.1016/s0166-4328(01)00297-2 · Cited by 4,146 · see citing works
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969 · see citing works
High-throughput droplet digital PCR system for absolute quantitation of DNA copy number
Hindson, B. J., Ness, K. D., Masquelier, D. A., Belgrader, P., Heredia, N. J., Makarewicz, A. J., Bright, I. J., Lucero, M. Y., Hiddessen, A. L., Legler, T. C., et al. (2011) · Analytical Chemistry · doi:10.1021/ac202028g · Cited by 2,792 · see citing works
Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences
Raj, A., & van Oudenaarden, A. (2008) · Cell · doi:10.1016/j.cell.2008.09.050 · Cited by 2,593 · see citing works
Vogelstein, B., & Kinzler, K. W. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.16.9236 · Cited by 1,770 · see citing works
Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells
Yan, L., Yang, M., Guo, H., Yang, L., Wu, J., Li, R., Liu, P., Lian, Y., Zheng, X., Yan, J., et al. (2013) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2660 · Cited by 1,740 · see citing works
Stochastic mRNA Synthesis in Mammalian Cells
Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y., & Tyagi, S. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040309 · Cited by 1,737 · see citing works
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609 · see citing works
Control of cell cycle transcription during G1 and S phases
Bertoli, C., Skotheim, J. M., & de Bruin, R. A. M. (2013) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3629 · Cited by 1,510 · see citing works
Raue, A., Kreutz, C., Maiwald, T., Bachmann, J., Schilling, M., Klingmüller, U., & Timmer, J. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp358 · Cited by 1,442 · cited 1× · see citing works
“We evaluate if the parameters of both steady-state distributions are identifiable and therefore capable of describing the data by calculating their profile likelihoods (Raue et al 2009).”
MethodIntrinsic noise in gene regulatory networks
Thattai, M., & van Oudenaarden, A. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.151588598 · Cited by 1,428 · see citing works
Quantitative single-cell RNA-seq with unique molecular identifiers
Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lönnerberg, P., & Linnarsson, S. (2014) · Nature Methods · doi:10.1038/nmeth.2772 · Cited by 1,183 · see citing works
Single-cell sequencing-based technologies will revolutionize whole-organism science
Shapiro, E., Biezuner, T., & Linnarsson, S. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3542 · Cited by 1,177 · see citing works
Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V., Scialdone, A., Theis, F. J., Teichmann, S. A., Marioni, J. C., & Stegle, O. (2015) · Nature Biotechnology · doi:10.1038/nbt.3102 · Cited by 1,136 · see citing works
Self-citationCounting absolute numbers of molecules using unique molecular identifiers
Kivioja, T., Vähärautio, A., Karlsson, K., Bonke, M., Enge, M., Linnarsson, S., & Taipale, J. (2012) · Nature Methods · doi:10.1038/nmeth.1778 · Cited by 1,010 · see citing works
Accounting for technical noise in single-cell RNA-seq experiments
Brennecke, P., Anders, S., Kim, J. K., Kołodziejczyk, A. A., Zhang, X., Proserpio, V., Baying, B., Benes, V., Teichmann, S. A., Marioni, J. C., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2645 · Cited by 979 · see citing works
Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq
Islam, S., Kjällquist, U., Moliner, A., Zajac, P., Fan, J.-B., Lönnerberg, P., & Linnarsson, S. (2011) · Genome Research · doi:10.1101/gr.110882.110 · Cited by 949 · see citing works
Analytical distributions for stochastic gene expression
Shahrezaei, V., & Swain, P. S. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0803850105 · Cited by 829 · see citing works
Guo, G., Huss, M., Tong, G. Q., Wang, C., Sun, L., Clarke, N. D., & Robson, P. (2010) · Developmental Cell · doi:10.1016/j.devcel.2010.02.012 · Cited by 806 · see citing works
Using Gene Expression Noise to Understand Gene Regulation
Munsky, B., Neuert, G., & van Oudenaarden, A. (2012) · Science · doi:10.1126/science.1216379 · Cited by 745 · see citing works
Markovian Modeling of Gene-Product Synthesis
Peccoud, J., & Ycart, B. (1995) · Theoretical Population Biology · doi:10.1006/tpbi.1995.1027 · Cited by 739 · see citing works
Quantitative assessment of single-cell RNA-sequencing methods
Wu, A. R., Neff, N. F., Kalisky, T., Dalerba, P., Treutlein, B., Rothenberg, M. E., Mburu, F. M., Mantalas, G. L., Sim, S., Clarke, M. F., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2694 · Cited by 696 · see citing works
Single cell analysis: the new frontier in 'Omics'
Wang, D., & Bodovitz, S. (2010) · Trends in biotechnology · doi:10.1016/j.tibtech.2010.03.002 · Cited by 669 · see citing works
Sasagawa, Y., Nikaido, I., Hayashi, T., Danno, H., Uno, K. D., Imai, T., & Ueda, H. R. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r31 · Cited by 455 · see citing works
Padovan-Merhar, O. M., Nair, G. P., Biaesch, A. G., Mayer, A., Scarfone, S., Foley, S. W., Wu, A. R., Churchman, L. S., Singh, A., & Raj, A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.03.005 · Cited by 434 · see citing works
Ozsolak, F., Platt, A. R., Jones, D. R., Reifenberger, J. G., Sass, L. E., McInerney, P., Thompson, J. F., Bowers, J., Jarosz, M., & Milos, P. M. (2009) · Nature · doi:10.1038/nature08390 · Cited by 433 · see citing works
Optical measurement of cycle-dependent cell growth
Mir, M., Wang, Z., Shen, Z. Z., Bednarz, M., Bashir, R., Golding, I., Prasanth, S. G., & Popescu, G. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1100506108 · Cited by 411 · see citing works
Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR
Warren, L. A., Bryder, D., Weissman, I., & Quake, S. (2006) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0608512103 · Cited by 373 · see citing works
Development and applications of single-cell transcriptome analysis
Tang, F., Lao, K., & Surani, M. A. (2011) · Nature Methods · doi:10.1038/nmeth.1557 · Cited by 337 · see citing works
Single-Molecule Approaches to Stochastic Gene Expression
Raj, A., & van Oudenaarden, A. (2009) · Annual Review of Biophysics · doi:10.1146/annurev.biophys.37.032807.125928 · Cited by 329 · see citing works
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data
Vallejos, C. A., Marioni, J. C., & Richardson, S. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004333 · Cited by 302 · see citing works
Single-molecule analysis of gene expression using two-color RNA labeling in live yeast
Hocine, S., Raymond, P., Zenklusen, D., Chao, J. A., & Singer, R. H. (2013) · Nature Methods · doi:10.1038/nmeth.2305 · Cited by 297 · see citing works
Image-based transcriptomics in thousands of single human cells at single-molecule resolution
Battich, N., Stoeger, T., & Pelkmans, L. (2013) · Nature Methods · doi:10.1038/nmeth.2657 · Cited by 269 · see citing works
Dynamics extracted from fixed cells reveal feedback linking cell growth to cell cycle
Kafri, R., Levy, J., Ginzberg, M. B., Oh, S., Lahav, G., & Kirschner, M. W. (2013) · Nature · doi:10.1038/nature11897 · Cited by 232 · see citing works
Control of ground-state pluripotency by allelic regulation of Nanog
Miyanari, Y., & Torres‐Padilla, M. (2012) · Nature · doi:10.1038/nature10807 · Cited by 231 · see citing works
Conlon, I. J., & RAFF, M. C. (2003) · Journal of Biology · doi:10.1186/1475-4924-2-7 · Cited by 208 · see citing works
Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data
Kim, J. K., & Marioni, J. C. (2013) · Genome biology · doi:10.1186/gb-2013-14-1-r7 · Cited by 207 · see citing works
Comprehensive qPCR profiling of gene expression in single neuronal cells
Citri, A., Pang, Z. P., Südhof, T. C., Wernig, M., & Malenka, R. C. (2011) · Nature Protocols · doi:10.1038/nprot.2011.430 · Cited by 169 · see citing works
Mitchison, J. M. (2003) · International review of cytology · doi:10.1016/s0074-7696(03)01004-0 · Cited by 168 · see citing works
Cell-Cycle Dependence of Transcription Dominates Noise in Gene Expression
Maheshri, N., Zopf, C. J., Quinn, K., & Zeidman, J. (2013) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 164 · see citing works
Methods for qPCR gene expression profiling applied to 1440 lymphoblastoid single cells
Livak, K. J., Wills, Q. F., Tipping, A. J., Datta, K., Mittal, R., Goldson, A. J., Sexton, D. W., & Holmes, C. C. (2012) · Methods · doi:10.1016/j.ymeth.2012.10.004 · Cited by 151 · see citing works
Microbial environments confound antibiotic efficacy
Lee, H. H., & Collins, J. J. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.740 · Cited by 143 · see citing works
Moignard, V., Macaulay, I., Swiers, G., Buettner, F., Schütte, J., Calero-Nieto, F., Kinston, S., Joshi, A., Hannah, R., Theis, F. J., et al. (2013) · Nature Cell Biology · doi:10.1038/ncb2709 · Cited by 116 · see citing works
Self-citationWhat do expression dynamics tell us about the mechanism of transcription?
Larson, D. R. (2011) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2011.07.010 · Cited by 104 · see citing works
Single-cell gene expression profiling using reverse transcription quantitative real-time PCR
Ståhlberg, A., & Bengtsson, M. (2010) · Methods · doi:10.1016/j.ymeth.2010.01.002 · Cited by 101 · see citing works
Allele-specific detection of single mRNA molecules in situ
Hansen, C. H., & van Oudenaarden, A. (2013) · Nature Methods · doi:10.1038/nmeth.2601 · Cited by 65 · see citing works
New technologies for measuring single cell mass
Popescu, G., Park, K., Mir, M., & Bashir, R. (2013) · Lab on a Chip · doi:10.1039/c3lc51033f · Cited by 64 · see citing works
Regulation of mRNA Translation during cellular division
Sivan, G., & Elroy‐Stein, O. (2008) · Cell Cycle · doi:10.4161/cc.7.6.5596 · Cited by 63 · see citing works
Buettner, F., & Theis, F. J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts385 · Cited by 58 · see citing works
Self-citationControl and maintenance of mammalian cell size
Cooper, S. (2004) · BMC Cell Biology · doi:10.1186/1471-2121-5-35 · Cited by 49 · see citing works
Sasagawa, Y., Nikaido, I., Hayashi, T., Danno, H., Uno, K. D., Imai, T., & Ueda, H. R. (2017) · Genome biology · doi:10.1186/s13059-017-1154-x · Cited by 40 · see citing works
Nosrati, N., Kapoor, N. R., & Kumar, V. (2014) · FEBS Journal · doi:10.1111/febs.12786 · Cited by 39 · see citing works
Optimal Scaling of Digital Transcriptomes
Glusman, G., Caballero-Pérez, J., Robinson, M., Kutlu, B., & Hood, L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077885 · Cited by 33 · see citing works
Make Them, Break Them, and Catch Them: Studying Rare Ubiquitin Chains
Uckelmann, M., & Sixma, T. K. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.03.024 · Cited by 21 · see citing works
Buettner, F., Moignard, V., Göttgens, B., & Theis, F. J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu134 · Cited by 21 · see citing works
Self-citationCell-cycle dependence of transcription dominates noise in gene expression
Zopf, C. J., Quinn, K., Zeidman, J., & Maheshri, N. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003161 · Cited by 4 · see citing works
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Research works printed but not linked: 1
- Jeffreys H 1961 The Theory of Probability 3rd edn (Oxford: Oxford University Press)
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Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231 · see citing works
Self-citationSingle-Cell Transcriptome Analysis in Plants: Advances and Challenges
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Concepts and limitations for learning developmental trajectories from single cell genomics
Tritschler, S., Büttner, M., Fischer, D. S., Lange, M., Bergen, V., Lickert, H., & Theis, F. J. (2019) · Development · doi:10.1242/dev.170506 · Cited by 233 · see citing works
Self-citationData analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications
Su, M., Pan, T., Chen, Q.-Z., Zhou, W., Gong, Y., Xu, G., Yan, H.-Y., Li, S., Shi, Q.-Z., Zhang, Y., et al. (2022) · Military Medical Research · doi:10.1186/s40779-022-00434-8 · Cited by 103 · see citing works
Intrinsic and extrinsic noise of gene expression in lineage trees
Thomas, P. (2019) · Scientific Reports · doi:10.1038/s41598-018-35927-x · Cited by 71 · see citing works
Thomas, P., & Shahrezaei, V. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0274 · Cited by 44 · see citing works
Tang, W., Jørgensen, A. C. S., Marguerat, S., Thomas, P., & Shahrezaei, V. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad395 · Cited by 44 · see citing works
Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data
Oh, S., & Li, R. W. (2021) · Genes · doi:10.3390/genes12030352 · Cited by 25 · see citing works
Durst, F. C., Grujovic, A., Ganser, I., Hoffmann, M., Ugocsai, P., Klein, C. A., & Czyż, Z. T. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216442 · Cited by 10 · see citing works
Reading the heart at single-cell resolution
Zhou, B., & Wang, L. (2020) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2020.08.010 · Cited by 8 · see citing works
Simulation of CRISPR-Cas9 editing on evolving barcode and accuracy of lineage tracing
Liu, F., Zhang, X. H.-F., & Yang, Y. (2024) · Scientific Reports · doi:10.1038/s41598-024-70154-7 · Cited by 3 · see citing works
FEM: mining biological meaning from cell level in single-cell RNA sequencing data
Liu, Y., Lü, N., Bi, C., Han, T., Guo, Z., Zhu, Y., Li, Y., He, C., & Lu, Z. (2021) · PeerJ · doi:10.7717/peerj.12570 · Cited by 3 · see citing works
Thomas, P., & Shahrezaei, V. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.23.352856 · Cited by 2 · see citing works
CycleMix: Gaussian mixture modeling of the cell cycle
Peplinski, J. E., & Andrews, T. S. (2026) · Bioinformatics Advances · doi:10.1093/bioadv/vbag179
- Authors
- Blasi, T., Buettner, F., Strasser, M. K., Marr, C., & Theis, F. J.
- Affiliations
- Helmholtz Munich (DE)
- Corresponding author
- Marr, Carsten
- Theis, Fabian J
- Venue
- Physical Biology
- Publisher
- IOP Publishing
- Published
- 2017-05-11
- Volume
- 14
- Issue
- 3
- Pages
- 036001
- ISSN
- 1478-3967 (ISSN-L)
- 1478-3975
- OpenAlex
- W2950872423
- Cited by
- 18
- References
- 59
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 7 · 2020: 3 · 2019: 4
- FWCI
- 0.78
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Physical Biology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Site cant be reached (submittedVersion, cc-by-nc-nd)
- Funders
- Deutsche Forschungsgemeinschaft (SPP 1356)
- Studienstiftung des Deutschen Volkes
- 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 Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- cell size heterogeneity, single-cell transcriptomics, normalization, stationary distribution, cell cycle, mouse embryonic stem cells, gene expression regulation, single-cell RNA sequencing
- MeSH terms
- Models, Genetic, RNA, Messenger, Gene Expression, Computational Biology, Gene Expression Profiling, Cell Size, Cell Enlargement
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