Power analysis of single-cell rnA-sequencing experiments

Nature Methods

Volumn 14, Issue 4, 2017, Pages 381-387

  Svensson, Valentine ^a,b   Natarajan, Kedar Nath ^a,b   Ly, Lam Ha ^b   Miragaia, Ricardo J ^b,c   Labalette, Charlotte ^b,d,e   Macaulay, Iain C ^b   Cvejic, Ana ^b,d,e   Teichmann, Sarah A ^a,b  

^a EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^b WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^c UNIVERSITY OF MINHO   (Portugal)

^d University of Cambridge   (United Kingdom)

^e UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEMENTARY DNA; MESSENGER RNA; RNA; POLYADENYLIC ACID;

ARTICLE; CELL POPULATION; CELLS BY BODY ANATOMY; FLUORESCENCE IN SITU HYBRIDIZATION; FREEZE THAWING; GENE EXPRESSION; MEASUREMENT ACCURACY; NONHUMAN; POWER ANALYSIS; PRIORITY JOURNAL; RNA DEGRADATION; RNA SEQUENCE; SENSITIVITY ANALYSIS; SINGLE CELL ANALYSIS; TRANSCRIPTOMICS; ANIMAL; EMBRYONIC STEM CELL; FREEZING; MOUSE; PHYSIOLOGY; PROCEDURES; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS; STANDARDS; STATISTICS AND NUMERICAL DATA; WORKFLOW;

ANIMALS; EMBRYONIC STEM CELLS; FREEZING; MICE; POLY A; RNA, MESSENGER; SENSITIVITY AND SPECIFICITY; SEQUENCE ANALYSIS, RNA; SINGLE-CELL ANALYSIS; WORKFLOW;

EID: 85014524493     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/nmeth.4220     Document Type: Article

References (45)

1. Macaulay, I. C. & Voet, T. Single cell genomics: advances and future perspectives. PLoS Genet. 10, e1004126 (2014).
2. Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133-145 (2015).
3. Wu, A. R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41-46 (2014).
4. Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Preprint at http://biorxiv. org/content/early/2016/06/29/035758/(2016).
5. External RNA Controls Consortium. Proposed methods for testing and selecting the ERCC external RNA controls. BMC Genomics 6, 150 (2005).
6. Jiang, L. et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res. 21, 1543-1551 (2011).
7. Munro, S. A. et al. Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures. Nat. Commun. 5, 5125 (2014).
8. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666-673 (2012).
9. Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163-166 (2014).
10. Viphakone, N., Voisinet-Hakil, F. & Minvielle-Sebastia, L. Molecular dissection of mRNA poly(A) tail length control in yeast. Nucleic Acids Res. 36, 2418-2433 (2008).
11. Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637-640 (2014).
12. Walker, E. & Nowacki, A. S. Understanding equivalence and noninferiority testing. J. Gen. Intern. Med. 26, 192-196 (2011).
13. SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat. Biotechnol. 32, 903-914 (2014).
14. Kapteyn, J., He, R., McDowell, E. T. & Gang, D. R. Incorporation of non-natural nucleotides into template-switching oligonucleotides reduces background and improves cDNA synthesis from very small RNA samples. BMC Genomics 11, 413 (2010).
15. Mahata, B. et al. Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis. Cell Rep. 7, 1130-1142 (2014).
16. Buettner, F. et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155-160 (2015).
17. Pollen, A. A. et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053-1058 (2014).
18. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371-375 (2014).
19. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096-1098 (2013).
20. Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138-1142 (2015).
21. Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776-779 (2014).
22. Ferreira, T. et al. Silencing of odorant receptor genes by G protein signaling ensures the expression of one odorant receptor per olfactory sensory neuron. Neuron 81, 847-859 (2014).
23. Owens, N. D. L. et al. Measuring absolute RNA copy numbers at high temporal resolution reveals transcriptome kinetics in development. Cell Rep. 14, 632-647 (2016).
24. Llorens-Bobadilla, E. et al. Single-cell transcriptomics reveals a population of dormant neural stem cells that become activated upon brain injury. Cell Stem Cell 17, 329-340 (2015).
25. Fan, X. et al. Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol. 16, 148 (2015).
26. Dang, Y. et al. Tracing the expression of circular RNAs in human pre-implantation embryos. Genome Biol. 17, 130 (2016).
27. Velten, L. et al. Single-cell polyadenylation site mapping reveals 3 isoform choice variability. Mol. Syst. Biol. 11, 812 (2015).
28. Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016).
29. Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663-1677 (2015).
30. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202-1214 (2015).
31. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187-1201 (2015).
32. Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519-522 (2015).
33. Scialdone, A. et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85, 54-61 (2015).
34. Padovan-Merhar, O. et al. Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms. Mol. Cell 58, 339-352 (2015).
35. Sansom, S. N. et al. Population and single-cell genomics reveal the Aire dependency, relief from Polycomb silencing, and distribution of self-antigen expression in thymic epithelia. Genome Res. 24, 1918-1931 (2014).
36. Wilson, N. K. et al. Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations. Cell Stem Cell 16, 712-724 (2015).
37. Streets, A. M. et al. Microfluidic single-cell whole-transcriptome sequencing. Proc. Natl. Acad. Sci. USA 111, 7048-7053 (2014).
38. Guo, F. et al. The transcriptome and DNA methylome landscapes of human primordial germ cells. Cell 161, 1437-1452 (2015).
39. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Preprint at http://biorxiv. org/content/early/2016/07/26/065912/(2016).
40. Brennecke, P. et al. Single-cell transcriptome analysis reveals coordinated ectopic gene-expression patterns in medullary thymic epithelial cells. Nat. Immunol. 16, 933-941 (2015).
41. Patro, R., Duggal, G., Love, M. I., Irizarry, M. A. & Kingsford, C. Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference. Preprint at http://biorxiv. org/content/early/2016/08/30/021592/(2015).
42. Srivastava, A., Sarkar, H., Gupta, N. & Patro, R. RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes. Bioinformatics 32, i192-i200 (2016).
43. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525-527 (2016).
44. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825-2830 (2011).
45. Carpenter, B., Gelman, A., Hoffman, M., Lee, D. & Goodrich, B. Stan: A probabilistic programming language. J. Stat. Softw. 76, 1-32 (2017).