Erratum: Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data (Cell Systems (2018) 7(3) (284–294.e12), (S2405471218302783), (10.1016/j.cels.2018.06.011));Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data

Cell Systems

Volumn 7, Issue 3, 2018, Pages 284-294.e12

  Eling, Nils ^a,b   Richard, Arianne C ^b,c   Richardson, Sylvia ^d   Marioni, John C ^a,b   Vallejos, Catalina A ^e,f,g  

^a EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^d UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^e ALAN TURING INSTITUTE   (United Kingdom)

^f UNIVERSITY COLLEGE LONDON   (United Kingdom)

^g UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Bayesian; immune activation; single cell RNA sequencing; statistics; transcriptional noise; variability

Indexed keywords

CELL RECEPTOR; NOTCH1 RECEPTOR; OCTAMER TRANSCRIPTION FACTOR 2; RNA POLYMERASE; SMAD3 PROTEIN; TRANSCRIPTION FACTOR T BET; TRANSCRIPTOME;

ANIMAL CELL; ARTICLE; CD4+ T LYMPHOCYTE; CELL DIFFERENTIATION; CELL FATE; CELL POPULATION; CONTROLLED STUDY; GENE; GENE EXPRESSION; HIPPOCAMPAL CA1 REGION; IMMUNE RESPONSE GENE; IMMUNOCOMPETENT CELL; IN VIVO STUDY; MALARIA; MOUSE; NONHUMAN; PRIORITY JOURNAL; RNA SEQUENCE; SINGLE CELL ANALYSIS; T LYMPHOCYTE ACTIVATION; UPREGULATION; ANIMAL; BIOLOGICAL VARIATION; C57BL MOUSE; CELL LINEAGE; COMPUTER SIMULATION; GENE EXPRESSION REGULATION; GENETICS; IMMUNITY; LYMPHOCYTE ACTIVATION; PHYSIOLOGY; PROCEDURES; SEQUENCE ANALYSIS; THEORETICAL MODEL;

ANIMALS; BIOLOGICAL VARIATION, POPULATION; CD4-POSITIVE T-LYMPHOCYTES; CELL DIFFERENTIATION; CELL LINEAGE; COMPUTER SIMULATION; GENE EXPRESSION REGULATION; IMMUNITY; LYMPHOCYTE ACTIVATION; MICE; MICE, INBRED C57BL; MODELS, THEORETICAL; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; TRANSCRIPTOME;

EID: 85054462437     PISSN: 24054712     EISSN: 24054720     Source Type: Journal    
DOI: 10.1016/j.cels.2019.08.003     Document Type: Erratum

References (59)

1. Anders, S., Pyl, P.T., Huber, W., HTSeq - A python framework to work with high-throughput sequencing data. Bioinformatics 31 (2015), 166–169.
2. 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.
3. 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.
4. 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.
5. Bar-Even, A., Paulsson, J., Maheshri, N., Carmi, M., O'Shea, E., Pilpel, Y., Barkai, N., Noise in protein expression scales with natural protein abundance. Nat. Genet. 38 (2006), 636–643.
6. Bochkina, N., Richardson, S., Tail posterior probability for inference in pairwise and multiclass gene expression data. Biometrics 63 (2007), 1117–1125.
7. 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. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10 (2013), 1093–1095.
8. 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.
9. Buettner, F., Natarajan, K.N., Casale, F.P., Proserpio, V., Scialdone, A., Theis, F.J., Teichmann, S.A., Marioni, J.C., Stegle, O., Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33 (2015), 155–160.
10. Carroll, R.J., Measurement Error in Epidemiologic Studies. 1998, John Wiley & Sons, Ltd, Chichester.
11. 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.
12. Chikuma, S., Basics of PD-1 in self-tolerance, infection, and cancer immunity. Int. J. Clin. Oncol. 21 (2016), 448–455.
13. 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.
14. Crotty, S., T follicular helper cell differentiation, function, and roles in disease. Immunity 41 (2014), 529–542.
15. 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.
16. 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.
17. Elowitz, M.B., Levine, A.J., Siggia, E.D., Swain, P.S., Stochastic gene expression in a single cell. Science 297 (2002), 1183–1186.
18. Fernandez, C., Steel, M.F.J., Multivariate Student-t regression models: pitfalls and inference. Biometrika 86 (1999), 153–167.
19. 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.
20. 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.
21. Goolam, M., Scialdone, A., Graham, S.J.L., MacAulay, I.C., Jedrusik, A., Hupalowska, A., Voet, T., Marioni, J.C., Zernicka-Goetz, M., Heterogeneity in Oct4 and Sox2 targets biases cell fate in 4-cell mouse embryos. Cell 165 (2016), 61–74.
22. Grün, D., Kester, L., van Oudenaarden, A., Validation of noise models for single-cell transcriptomics. Nat. Methods 11 (2014), 637–640.
23. 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.
24. Jiang, L., Schlesinger, F., Davis, C.A., Zhang, Y., Li, R., Salit, M., Gingeras, T.R., Oliver, B., Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21 (2011), 1543–1551.
25. Kapourani, C.A., Sanguinetti, G., Higher order methylation features for clustering and prediction in epigenomic studies. Bioinformatics, 32, 2016 i405–ii412.
26. 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.
27. 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.
28. Kolodziejczyk, A.A., Kim, J.K., Tsang, J.C., Ilicic, T., Henriksson, J., Natarajan, K.N., Tuck, A.C., Gao, X., Bühler, M., Liu, P., et al. Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell 17 (2015), 471–485.
29. Korthauer, K.D., Chu, L.F., Newton, M.A., Li, Y., Thomson, J., Stewart, R., Kendziorski, C., A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biol., 17, 2016, 222.
30. 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., et al. Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria. Sci. Immunol., 2, 2017, eaal2192.
31. 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.
32. Lun, A.T.L., Bach, K., Marioni, J.C., Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol., 17, 2016, 75.
33. 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.
34. Macosko, E.Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A.R., Kamitaki, N., Martersteck, E.M., et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161 (2015), 1202–1214.
35. 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., et al. Aging increases cell-to-cell transcriptional variability upon immune stimulation. Science 355 (2017), 1433–1436.
36. Miller, K.S., On the inverse of the sum of matrices. Math. Mag. 54 (1981), 67–72.
37. Mohammed, H., Hernando-Herraez, I., Savino, A., Scialdone, A., Macaulay, I., Mulas, C., Chandra, T., Voet, T., Dean, W., Nichols, J., et al. Single-cell landscape of transcriptional heterogeneity and cell fate decisions during mouse early gastrulation. Cell Rep. 20 (2017), 1215–1228.
38. 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.
39. Mueller, K., Quandt, J., Marienfeld, R.B., Weihrich, P., Fiedler, K., Claussnitzer, M., Laumen, H., Vaeth, M., Berberich-Siebelt, F., Serfling, E., et al. Octamer-dependent transcription in T cells is mediated by NFAT and NF-κB. Nucleic Acids Res. 41 (2013), 2138–2154.
40. 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.
41. Newton, M.A., Noueiry, A., Sarkar, D., Ahlquist, P., Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5 (2004), 155–176.
42. Paul, F., Arkin, Y., Giladi, A., Jaitin, D.A., Kenigsberg, E., Keren-Shaul, H., Winter, D., Lara-Astiaso, D., Gury, M., Weiner, A., et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163 (2015), 1663–1677.
43. Richard, A., Boullu, L., Herbach, U., Bonnafoux, A., Morin, V., Vallin, E., Guillemin, A., Papili Gao, N., Gunawan, R., Cosette, J., et al. 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.
44. Roberts, G.O., Rosenthal, J.S., Examples of adaptive MCMC. J. Comp. Graph. Stat. 18 (2009), 349–367.
45. 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.
46. Strasser, A., Jost, P.J., Nagata, S., The many roles of Fas receptor signaling in the immune system. Immunity 30 (2009), 180–192.
47. 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.
48. 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.
49. 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.
50. Taniguchi, Y., Choi, P.J., Li, G.W., Chen, H., Babu, M., Hearn, J., Emili, A., Xie, X.S., Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329 (2010), 533–538.
51. Tung, P.Y., Blischak, J.D., Hsiao, C.J., Knowles, D.A., Burnett, J.E., Pritchard, J.K., Gilad, Y., Batch effects and the effective design of single-cell gene expression studies. Sci. Rep., 7, 2017, 39921.
52. Vallejos, C.A., Marioni, J.C., Richardson, S., BASiCS: Bayesian analysis of single-cell sequencing data. PLoS Comput. Biol., 11, 2015, e1004333.
53. 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.
54. 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.
55. Vallejos, C.A., Steel, M.F.J., Objective Bayesian survival analysis using shape mixtures of log-normal distributions. J. Am. Stat. Assoc. 110 (2015), 697–710.
56. West, M., Harrison, J., Bayesian Forecasting and Dynamic Models. 1989, Springer, Berlin.
57. Wu, T.D., Nacu, S., Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26 (2010), 873–881.
58. Zeisel, A., Muñoz-Manchado, A.B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347 (2015), 1138–1142.
59. Zeng, C., Mulas, F., Sui, Y., Guan, T., Miller, N., Tan, Y., Liu, F., Jin, W., Carrano, A.C., Huising, M.O., et al. Pseudotemporal ordering of single cells reveals metabolic control of postnatal β cell proliferation. Cell. Metab. 25 (2017), 1160–1175.e11.