Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data
Open accessPublished version of a preprint
Abstract
Cell-to-cell transcriptional variability in otherwise homogeneous cell populations plays an important role in tissue function and development. Single-cell RNA sequencing can characterize this variability in a transcriptome-wide manner. However, technical variation and the confounding between variability and mean expression estimates hinder meaningful comparison of expression variability between cell populations. To address this problem, we introduce an analysis approach that extends the BASiCS statistical framework to derive a residual measure of variability that is not confounded by mean expression. This includes a robust procedure for quantifying technical noise in experiments where technical spike-in molecules are not available. We illustrate how our method provides biological insight into the dynamics of cell-to-cell expression variability, highlighting a synchronization of biosynthetic machinery components in immune cells upon activation. In contrast to the uniform up-regulation of the biosynthetic machinery, CD4 + T cells show heterogeneous up-regulation of immune-related and lineage-defining genes during activation and differentiation.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The regularization procedure explicitly learns and accounts for the well-established relationship between a gene's mean abundance and variance in single-cell data.
“Our regularization procedure explicitly learns and accounts for the well-established relationship [27] between a gene’s mean abundance and variance in single-cell data”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [27]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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
Taniguchi, Y., Choi, P. J., Li, G.-W., Chen, H., Babu, M., Hearn, J., Emili, A., & Xie, X. S. (2010) · Science · doi:10.1126/science.1188308 · Cited by 1,982 · cited 1× · see citing works
“The latter arises because biological noise is negatively correlated with protein abundance (BarEven et al., 2006; Newman et al., 2006; Taniguchi et al., 2010) or mean RNA expression (Brennecke et al.”
BackgroundBuettner, 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-citationPooling across cells to normalize single-cell RNA sequencing data with many zero counts
L. Lun, A. T., Bach, K., & Marioni, J. C. (2016) · Genome Biology · doi:10.1186/s13059-016-0947-7 · Cited by 1,108 · see citing works
Self-citationNoise in protein expression scales with natural protein abundance
Bar-Even, A., Paulsson, J., Maheshri, N., Carmi, M., O'Shea, E., Pilpel, Y., & Barkai, N. (2006) · Nature Genetics · doi:10.1038/ng1807 · Cited by 807 · see citing works
Synthetic spike-in standards for RNA-seq experiments
Jiang, L., Schlesinger, F., Davis, C. A., Zhang, Y., Li, R., Salit, M., Gingeras, T. R., & Oliver, B. (2011) · Genome Research · doi:10.1101/gr.121095.111 · Cited by 653 · see citing works
Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation
Kolodziejczyk, A. A., Kim, J. K., Tsang, J. C. H., Ilicic, T., Henriksson, J., Natarajan, K. N., Tuck, A. C., Gao, X., Bühler, M., Liu, P., et al. (2015) · Cell Stem Cell · doi:10.1016/j.stem.2015.09.011 · Cited by 570 · see citing works
Self-citationHeterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos
Goolam, M., Scialdone, A., Graham, S. J. L., Macaulay, I. C., Jedrusik, A., Hupalowska, A., Voet, T., Marioni, J. C., & Zernicka-Goetz, M. (2016) · Cell · doi:10.1016/j.cell.2016.01.047 · Cited by 484 · see citing works
Self-citationBatch effects and the effective design of single-cell gene expression studies
Tung, P.-Y., Blischak, J. D., Hsiao, C. J., Knowles, D. A., Burnett, J. E., Pritchard, J. K., & Gilad, Y. (2017) · Scientific Reports · doi:10.1038/srep39921 · Cited by 356 · 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
Self-citationA statistical approach for identifying differential distributions in single-cell RNA-seq experiments
Korthauer, K. D., Chu, L.-F., Newton, M. A., Li, Y., Thomson, J., Stewart, R., & Kendziorski, C. (2016) · Genome Biology · doi:10.1186/s13059-016-1077-y · Cited by 273 · see citing works
Flagged first, then by citations
Research works printed but not linked: 44
- Anders S. Pyl P.T. Huber W. HTSeq - A python framework to work with high-throughput sequencing data Bioinformatics 31 2015 166 169 25260700 PMC4287950
- Antolović V. Miermont A. Corrigan A.M. Chubb J.R. Generation of single-cell transcript variability by repression Curr. Biol. 27 2017 1811 1817.e3 28602650 PMC5483230
- Araki K. Morita M. Bederman A.G. Konieczny B.T. Kissick H.T. Sonenberg N. Ahmed R. Translation is actively regulated during the differentiation of CD8 + effector T cells Nat. Immunol. 18 2017 1046 1057 28714979 PMC5937989
- Bahar Halpern K. Tanami S. Landen S. Chapal M. Szlak L. Hutzler A. Nizhberg A. Itzkovitz S. Bursty gene expression in the intact mammalian liver Mol. Cell 58 2015 147 156 25728770 PMC4500162
- Bochkina N. Richardson S. Tail posterior probability for inference in pairwise and multiclass gene expression data Biometrics 63 2007 1117 1125 18078482
- Brennecke P. Anders S. Kim J.K. Kołodziejczyk A.A. Zhang X. Proserpio V. Baying B. Benes V. Teichmann S.A. Marioni J.C. Accounting for technical noise in single-cell RNA-seq experiments Nat. Methods 10 2013 1093 1095 24056876
- Buenrostro J.D. Wu B. Litzenburger U.M. Ruff D. Gonzales M.L. Snyder M.P. Chang H.Y. Greenleaf W.J. Single-cell chromatin accessibility reveals principles of regulatory variation Nature 523 2015 486 490 26083756 PMC4685948
- Carroll R.J. Measurement Error in Epidemiologic Studies 1998 John Wiley & Sons, Ltd Chichester
- Chang H.H. Hemberg M. Barahona M. Ingber D.E. Huang S. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells Nature 453 2008 544 547 18497826 PMC5546414
- Chikuma S. Basics of PD-1 in self-tolerance, infection, and cancer immunity Int. J. Clin. Oncol. 21 2016 448 455 26864303
- Choi Y.S. Kageyama R. Eto D. Escobar T.C. Johnston R.J. Monticelli L. Lao C. Crotty S. ICOS receptor instructs T follicular helper cell versus effector cell differentiation via induction of the transcriptional repressor Bcl6 Immunity 34 2011 932 946 21636296 PMC3124577
- Crotty S. T follicular helper cell differentiation, function, and roles in disease Immunity 41 2014 529 542 25367570 PMC4223692
- Delisle J.S. Giroux M. Boucher G. Landry J.R. Hardy M.P. Lemieux S. Jones R.G. Wilhelm B.T. Perreault C. The TGF-β-Smad3 pathway inhibits CD28-dependent cell growth and proliferation of CD4 T cells Genes Immun. 14 2013 115 126 23328844
- Dennis G. Sherman B.T. Hosack D.A. Yang J. Gao W. Lane H.C. Lempicki R.A. DAVID: database for annotation, visualization, and integrated discovery Genome Biol. 4 2003 P3 12734009
- Fuhrmann F. Lischke T. Gross F. Scheel T. Bauer L. Kalim K.W. Radbruch A. Herzel H. Hutloff A. Baumgrass R. Adequate immune response ensured by binary IL-2 and graded CD25 expression in a murine transfer model eLife 5 2016 1 17 PMC5201416 28035902
- Godefroy E. Zhong H. Pham P. Friedman D. Yazdanbakhsh K. TIGIT-positive circulating follicular helper T cells display robust B-cell help functions: potential role in sickle cell alloimmunization Haematologica 100 2015 1415 1425 26250578 PMC4825309
- Grün D. Kester L. van Oudenaarden A. Validation of noise models for single-cell transcriptomics Nat. Methods 11 2014 637 640 24747814
- Hornung G. Bar-ziv R. Rosin D. Tokuriki N. Tawfik D.S. Oren M. Barkai N. Noise-mean relationship in mutated promoters Genome Res. 22 2012 2409 2417 22820945 PMC3514670
- Kapourani C.A. Sanguinetti G. Higher order methylation features for clustering and prediction in epigenomic studies Bioinformatics 32 2016 i405–ii412 27587656
- Keren L. Van Dijk D. Weingarten-Gabbay S. Davidi D. Jona G. Weinberger A. Milo R. Segal E. Noise in gene expression is coupled to growth rate Genome Res. 25 2015 1893 1902 26355006 PMC4665010
- Klein A.M. Mazutis L. Akartuna I. Tallapragada N. Veres A. Li V. Peshkin L. Weitz D.A. Kirschner M.W. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells Cell 161 2015 1187 1201 26000487 PMC4441768
- Lönnberg T. Svensson V. James K.R. Fernandez-Ruiz D. Sebina I. Montandon R. Soon M.S.F. Fogg L.G. Nair A.S. Liligeto U.N. Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria Sci. Immunol. 2 2017 eaal2192 28345074 PMC5365145
- Love M.I. Huber W. Anders S. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2 Genome Biol. 15 2014 550 25516281 PMC4302049
- Lun A.T.L. Calero-Nieto F.J. Haim-Vilmovsky L. Göttgens B. Marioni J.C. Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data Genome Res. 27 2017 1795 1806 29030468 PMC5668938
- Macosko E.Z. Basu A. Satija R. Nemesh J. Shekhar K. Goldman M. Tirosh I. Bialas A.R. Kamitaki N. Martersteck E.M. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets Cell 161 2015 1202 1214 26000488 PMC4481139
- Martinez-Jimenez C.P. Eling N. Chen H.C. Vallejos C.A. Kolodziejczyk A.A. Connor F. Stojic L. Rayner T.F. Stubbington M.J.T. Teichmann S.A. Aging increases cell-to-cell transcriptional variability upon immune stimulation Science 355 2017 1433 1436 28360329 PMC5405862
- Mohammed H. Hernando-Herraez I. Savino A. Scialdone A. Macaulay I. Mulas C. Chandra T. Voet T. Dean W. Nichols J. Single-cell landscape of transcriptional heterogeneity and cell fate decisions during mouse early gastrulation Cell Rep. 20 2017 1215 1228 28768204 PMC5554778
- Mojtahedi M. Skupin A. Zhou J. Castaño I.G. Leong-Quong R.Y.Y. Chang H. Trachana K. Giuliani A. Huang S. Cell fate decision as high-dimensional critical state transition PLOS Biol. 14 2016 e2000640 PMC5189937 28027308
- Mueller K. Quandt J. Marienfeld R.B. Weihrich P. Fiedler K. Claussnitzer M. Laumen H. Vaeth M. Berberich-Siebelt F. Serfling E. Octamer-dependent transcription in T cells is mediated by NFAT and NF-κB Nucleic Acids Res. 41 2013 2138 2154 23293002 PMC3575799
- Newman J.R.S. Ghaemmaghami S. Ihmels J. Breslow D.K. Noble M. DeRisi J.L. Weissman J.S. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise Nature 441 2006 840 846 16699522
- Newton M.A. Noueiry A. Sarkar D. Ahlquist P. Detecting differential gene expression with a semiparametric hierarchical mixture method Biostatistics 5 2004 155 176 15054023
- Paul F. Arkin Y. Giladi A. Jaitin D.A. Kenigsberg E. Keren-Shaul H. Winter D. Lara-Astiaso D. Gury M. Weiner A. Transcriptional heterogeneity and lineage commitment in myeloid progenitors Cell 163 2015 1663 1677 26627738
- Richard A. Boullu L. Herbach U. Bonnafoux A. Morin V. Vallin E. Guillemin A. Papili Gao N. Gunawan R. Cosette J. Single-cell-based analysis highlights a surge in cell-to-cell molecular variability preceding irreversible commitment in a differentiation process PLOS Biol. 14 2016 1 35 PMC5191835 28027290
- Smallwood S.A. Lee H.J. Angermueller C. Krueger F. Saadeh H. Peat J. Andrews S.R. Stegle O. Reik W. Kelsey G. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity Nat. Methods 11 2014 817 820 25042786 PMC4117646
- Strasser A. Jost P.J. Nagata S. The many roles of Fas receptor signaling in the immune system Immunity 30 2009 180 192 19239902 PMC2956119
- Svensson V. Vento-Tormo R. Teichmann S.A. Exponential scaling of single-cell RNA-seq in the past decade Nat. Protoc. 13 2018 599 604 29494575
- Szabo S.J. Kim S.T. Costa G.L. Zhang X. Fathman C.G. Glimcher L.H. A novel transcription factor, T-bet, directs Th1 lineage commitment Cell 100 2000 655 669 10761931
- Tan, T.C.J., Knight, J., Sbarrato, T., Dudek, K., Willis, A.E., and Zamoyska, R. (2017) Suboptimal T-cell receptor signaling compromises protein translation, ribosome biogenesis, and proliferation of mouse CD8 T cells, In Proceedings of the National Academy of Sciences. PMC5544288 28696283
- Vallejos C.A. Richardson S. Marioni J.C. Beyond comparisons of means: understanding changes in gene expression at the single-cell level Genome Biol. 17 2016 70 27083558 PMC4832562
- Vallejos C.A. Risso D. Scialdone A. Dudoit S. Marioni J.C. Normalizing single-cell RNA sequencing data: challenges and opportunities Nat. Methods 14 2017 565 571 28504683 PMC5549838
- West M. Harrison J. Bayesian Forecasting and Dynamic Models 1989 Springer Berlin
- Wu T.D. Nacu S. Fast and SNP-tolerant detection of complex variants and splicing in short reads Bioinformatics 26 2010 873 881 20147302 PMC2844994
- Zeisel A. Muñoz-Manchado A.B. Codeluppi S. Lönnerberg P. La Manno G. Juréus A. Marques S. Munguba H. He L. Betsholtz C. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq Science 347 2015 1138 1142 25700174
- Zeng C. Mulas F. Sui Y. Guan T. Miller N. Tan Y. Liu F. Jin W. Carrano A.C. Huising M.O. Pseudotemporal ordering of single cells reveals metabolic control of postnatal β cell proliferation Cell. Metab. 25 2017 1160 1175.e11 28467932 PMC5501713
Cited by
Sorted by citations
Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479 · see citing works
“Our regularization procedure explicitly learns and accounts for the well-established relationship [Eling et al., 2018] between a gene’s mean abundance and variance in single cell data 3.”
MethodEleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327 · see citing works
Self-citationThe triumphs and limitations of computational methods for scRNA-seq
Kharchenko, P. V. (2021) · Nature Methods · doi:10.1038/s41592-021-01171-x · Cited by 321 · see citing works
Challenges in measuring and understanding biological noise
Eling, N., Morgan, M. D., & Marioni, J. C. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0130-6 · Cited by 247 · see citing works
Self-citationAnalytic Pearson residuals for normalization of single-cell RNA-seq UMI data
Lause, J., Berens, P., & Kobak, D. (2021) · Genome Biology · doi:10.1186/s13059-021-02451-7 · Cited by 195 · see citing works
- Authors
- Eling, N., Richard, A. C., Richardson, S., Marioni, J. C., & Vallejos, C. A.
- Affiliations
- European Bioinformatics Institute (GB)
- University of Cambridge (GB)
- Western General Hospital (GB)
- Corresponding author
- Marioni, John C.
- Vallejos, Catalina A.
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 7
- Issue
- 3
- Pages
- 284-294.e12
- ISSN
- 2405-4712 (ISSN-L)
- PMC
- PMC6167088
- OpenAlex
- W2889435420
- Cited by
- 99
- References
- 11
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 18 · 2023: 18 · 2022: 11 · 2021: 20 · 2020: 8 · 2019: 10
- FWCI
- 3.74
- Type
- journal article
- Language
- en
- Update notices
- erratum (2019-10-23)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc-nd)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- Medical Research Council (MC_UP_0801/1, MR/P014178/1)
- European Molecular Biology Laboratory
- Cancer Research UK
- Engineering and Physical Sciences Research Council (EP/N510129/1)
- Alan Turing Institute
- Center for Outcomes Research and Evaluation, Yale School of Medicine
- 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
- single-cell RNA sequencing, T cell activation, T cell differentiation, gene expression heterogeneity, immune-related genes
- MeSH terms
- Biological Variation, Population, Animals, Cell Differentiation, Computer Simulation, Gene Expression Regulation, Immunity, Lymphocyte Activation, Mice, Inbred C57BL, Models, Theoretical, CD4-Positive T-Lymphocytes, Sequence Analysis, RNA, Cell Lineage, Mice, Single-Cell Analysis, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
