Advertisement

Applications of Single-Cell Sequencing for Multiomics

  • Yungang Xu
  • Xiaobo Zhou
Part of the Methods in Molecular Biology book series (MIMB, volume 1754)

Abstract

Single-cell sequencing interrogates the sequence or chromatin information from individual cells with advanced next-generation sequencing technologies. It provides a higher resolution of cellular differences and a better understanding of the underlying genetic and epigenetic mechanisms of an individual cell in the context of its survival and adaptation to microenvironment. However, it is more challenging to perform single-cell sequencing and downstream data analysis, owing to the minimal amount of starting materials, sample loss, and contamination. In addition, due to the picogram level of the amount of nucleic acids used, heavy amplification is often needed during sample preparation of single-cell sequencing, resulting in the uneven coverage, noise, and inaccurate quantification of sequencing data. All these unique properties raise challenges in and thus high demands for computational methods that specifically fit single-cell sequencing data. We here comprehensively survey the current strategies and challenges for multiple single-cell sequencing, including single-cell transcriptome, genome, and epigenome, beginning with a brief introduction to multiple sequencing techniques for single cells.

Key words

Single-cell sequencing Single-cell transcriptome Genome Epigenome Multiomics Allele-specific expression Single nucleotide variant calling Clonal structure 

References

  1. 1.
    Stegle O, Teichmann SA, Marioni JC (2015) Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet 16(3):133–145.  https://doi.org/10.1038/nrg3833CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Gawad C, Koh W, Quake SR (2016) Single-cell genome sequencing: current state of the science. Nat Rev Genet 17(3):175–188.  https://doi.org/10.1038/nrg.2015.16CrossRefPubMedGoogle Scholar
  3. 3.
    Schwartzman O, Tanay A (2015) Single-cell epigenomics: techniques and emerging applications. Nat Rev Genet 16(12):716–726.  https://doi.org/10.1038/nrg3980CrossRefPubMedGoogle Scholar
  4. 4.
    Barkla BJ, Vera-Estrella R, Raymond C (2016) Single-cell-type quantitative proteomic and ionomic analysis of epidermal bladder cells from the halophyte model plant Mesembryanthemum crystallinum to identify salt-responsive proteins. BMC Plant Biol 16.  https://doi.org/10.1186/S12870-016-0797-1
  5. 5.
    Wu MY, Singh AK (2012) Single-cell protein analysis. Curr Opin Biotechnol 23(1):83–88.  https://doi.org/10.1016/j.copbio.2011.11.023CrossRefPubMedGoogle Scholar
  6. 6.
    Baslan T, Kendall J, Rodgers L, Cox H, Riggs M, Stepansky A, Troge J, Ravi K, Esposito D, Lakshmi B, Wigler M, Navin N, Hicks J (2012) Genome-wide copy number analysis of single cells. Nat Protoc 7(6):1024–1041.  https://doi.org/10.1038/nprot.2012.039CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Habib N, Li YQ, Heidenreich M, Swiech L, Avraham-Davidi I, Trombetta JJ, Hession C, Zhang F, Regev A (2016) Div-Seq: single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 353(6302):925–928.  https://doi.org/10.1126/science.aad7038CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Hou Y, Guo HH, Cao C, Li XL, Hu BQ, Zhu P, Wu XL, Wen L, Tang FC, Huang YY, Peng JR (2016) Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res 26(3):304–319.  https://doi.org/10.1038/cr.2016.23CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Gross A, Schoendube J, Zimmermann S, Steeb M, Zengerle R, Koltay P (2015) Technologies for single-cell isolation. Int J Mol Sci 16(8):16897–16919.  https://doi.org/10.3390/ijms160816897CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Osborne GW (2011) Recent advances in flow cytometric cell sorting. Methods Cell Biol 102:533–556.  https://doi.org/10.1016/B978-0-12-374912-3.00021-3CrossRefPubMedGoogle Scholar
  11. 11.
    Xin YR, Kim J, Ni M, Wei Y, Okamoto H, Lee J, Adler C, Cavino K, Murphy AJ, Yancopoulos GD, Lin HC, Gromada J (2016) Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells. Proc Natl Acad Sci U S A 113(12):3293–3298.  https://doi.org/10.1073/pnas.1602306113CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Strey HH, Brouzes E, Kruse T (2013) Droplet microfluidic technologies for high-throughput single-cell gene expression analysis. Biophys J 104(2):676aCrossRefGoogle Scholar
  13. 13.
    Brouzes E, Medkova M, Savenelli N, Marran D, Twardowski M, Hutchison JB, Rothberg JM, Link DR, Perrimon N, Samuels ML (2009) Droplet microfluidic technology for single-cell high-throughput screening. Proc Natl Acad Sci U S A 106(34):14195–14200.  https://doi.org/10.1073/pnas.0903542106CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Gomez-Sjoberg R, Leyrat AA, Pirone DM, Chen CS, Quake SR (2007) Versatile, fully automated, microfluidic cell culture system. Anal Chem 79(22):8557–8563.  https://doi.org/10.1021/ac071311wCrossRefPubMedGoogle Scholar
  15. 15.
    Ino K, Okochi M, Konishi N, Nakatochi M, Imai R, Shikida M, Ito A, Honda H (2008) Cell culture arrays using magnetic force-based cell patterning for dynamic single cell analysis. Lab Chip 8(1):134–142.  https://doi.org/10.1039/b712330bCrossRefPubMedGoogle Scholar
  16. 16.
    Di Carlo D, Wu LY, Lee LP (2006) Dynamic single cell culture array. Lab Chip 6(11):1445–1449.  https://doi.org/10.1039/b605937fCrossRefPubMedGoogle Scholar
  17. 17.
    Zhang K, Han X, Li Y, Li SY, Zu YL, Wang ZQ, Qin LD (2014) Hand-held and integrated single-cell pipettes. J Am Chem Soc 136(31):10858–10861.  https://doi.org/10.1021/ja5053279CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Frohlich J, Konig H (2000) New techniques for isolation of single prokaryotic cells. FEMS Microbiol Rev 24(5):567–572CrossRefGoogle Scholar
  19. 19.
    Voet T, Kumar P, Van Loo P, Cooke SL, Marshall J, Lin ML, Esteki MZ, Van der Aa N, Mateiu L, McBride DJ, Bignell GR, McLaren S, Teague J, Butler A, Raine K, Stebbings LA, Quail MA, D'Hooghe T, Moreau Y, Futreal PA, Stratton MR, Vermeesch JR, Campbell PJ (2013) Single-cell paired-end genome sequencing reveals structural variation per cell cycle. Nucleic Acids Res 41(12):6119–6138.  https://doi.org/10.1093/nar/gkt345CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Klitgaard K, Jensen TK, Angen O, Boye M (2007) Measurement of bacterial gene expression in vivo by laser capture microdissection and quantitative real-time RT-PCR. J Microbiol Methods 69(2):414–416.  https://doi.org/10.1016/j.mimet.2006.12.003CrossRefPubMedGoogle Scholar
  21. 21.
    Fink L, Kwapiszewska G, Wilhelm J, Bohle RM (2006) Laser-microdissection for cell type- and compartment-specific analyses on genomic and proteomic level. Exp Toxicol Pathol 57:25–29.  https://doi.org/10.1016/j.etp.2006.02.010CrossRefPubMedGoogle Scholar
  22. 22.
    Vannucci FA, Foster DN, Gebhart CJ (2013) Laser microdissection coupled with RNA-seq analysis of porcine enterocytes infected with an obligate intracellular pathogen (Lawsonia intracellularis). BMC Genomics 14.  https://doi.org/10.1186/1471-2164-14-421
  23. 23.
    Nakamura N, Ruebel K, Jin L, Qian X, Zhang H, Lloyd RV (2007) Laser capture microdissection for analysis of single cells. Methods Mol Med 132:11–18CrossRefGoogle Scholar
  24. 24.
    Huang G, Wang S (2013) Establishment of a new method to detect gene expression by laser capture microdissection-assisted single-cell real time RT-PCR without RNA purification. Mol Biol 47(4):509–514.  https://doi.org/10.1134/S0026893313040055CrossRefGoogle Scholar
  25. 25.
    Vandewoestyne M, Deforce D (2010) Laser capture microdissection in forensic research: a review. Int J Legal Med 124(6):513–521.  https://doi.org/10.1007/s00414-010-0499-4CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Decarlo K, Emley A, Dadzie OE, Mahalingam M (2011) Laser capture microdissection: methods and applications. Methods Mol Biol 755:1–15.  https://doi.org/10.1007/978-1-61779-163-5_1CrossRefPubMedGoogle Scholar
  27. 27.
    Fuller SA, Takahashi M, Hurrell JG (2001) Cloning of hybridoma cell lines by limiting dilution. Curr Protoc Mol Biol Chapter 11:Unit 11 18.  https://doi.org/10.1002/0471142727.mb1108s01CrossRefGoogle Scholar
  28. 28.
    Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63.  https://doi.org/10.1038/nrg2484CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Tang FC, Barbacioru C, Wang YZ, Nordman E, Lee C, Xu NL, Wang XH, Bodeau J, Tuch BB, Siddiqui A, Lao KQ, Surani MA (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6(5):377–U386.  https://doi.org/10.1038/Nmeth.1315CrossRefPubMedGoogle Scholar
  30. 30.
    Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li SQ, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32(4):381–U251.  https://doi.org/10.1038/nbt.2859CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Wills QF, Livak KJ, Tipping AJ, Enver T, Goldson AJ, Sexton DW, Holmes C (2013) Single-cell gene expression analysis reveals genetic associations masked in whole-tissue experiments. Nat Biotechnol 31(8):748.  https://doi.org/10.1038/nbt.2642CrossRefPubMedGoogle Scholar
  32. 32.
    Islam S, Kjallquist U, Moliner A, Zajac P, Fan JB, Lonnerberg P, Linnarsson S (2011) Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res 21(7):1160–1167.  https://doi.org/10.1101/gr.110882.110CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Islam S, Kjallquist U, Moliner A, Zajac P, Fan JB, Lonnerberg P, Linnarsson S (2012) Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing. Nat Protoc 7(5):813–828.  https://doi.org/10.1038/nprot.2012.022CrossRefPubMedGoogle Scholar
  34. 34.
    Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M, Lonnerberg P, Linnarsson S (2014) Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods 11(2):163–166.  https://doi.org/10.1038/nmeth.2772CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Hashimshony T, Wagner F, Sher N, Yanai I (2012) CEL-seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep 2(3):666–673.  https://doi.org/10.1016/j.celrep.2012.08.003CrossRefPubMedGoogle Scholar
  36. 36.
    Ramskold D, Luo SJ, Wang YC, Li R, Deng QL, Faridani OR, Daniels GA, Khrebtukova I, Loring JF, Laurent LC, Schroth GP, Sandberg R (2012) Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 30(8):777–782.  https://doi.org/10.1038/nbt.2282CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Picelli S, Bjorklund AK, Faridani OR, Sagasser S, Winberg G, Sandberg R (2013) Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 10(11):1096–1098.  https://doi.org/10.1038/Nmeth.2639CrossRefPubMedGoogle Scholar
  38. 38.
    Sasagawa Y, Nikaido I, Hayashi T, Danno H, Uno KD, Imai T, Ueda HR (2017) Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity (vol 14: R31, 2013). Genome Biol 18.  https://doi.org/10.1186/S13059-017-1154-X
  39. 39.
    Nakamura T, Yabuta Y, Okamoto I, Aramaki S, Yokobayashi S, Kurimoto K, Sekiguchi K, Nakagawa M, Yamamoto T, Saitou M (2015) SC3-seq: a method for highly parallel and quantitative measurement of single-cell gene expression. Nucleic Acids Res 43(9).  https://doi.org/10.1093/nar/gkv134CrossRefGoogle Scholar
  40. 40.
    Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I, Mildner A, Cohen N, Jung S, Tanay A, Amit I (2014) Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343(6172):776–779.  https://doi.org/10.1126/science.1247651CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Fan HC, Fu GK, Fodor SP (2015) Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 347(6222):1258367.  https://doi.org/10.1126/science.1258367CrossRefPubMedGoogle Scholar
  42. 42.
    Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM, Trombetta JJ, Weitz DA, Sanes JR, Shalek AK, Regev A, McCarroll SA (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161(5):1202–1214.  https://doi.org/10.1016/j.cell.2015.05.002CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, Peshkin L, Weitz DA, Kirschner MW (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161(5):1187–1201.  https://doi.org/10.1016/j.cell.2015.04.044CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Fan XY, Zhang XN, Wu XL, Guo HS, Hu YQ, Tang FC, Huang YY (2015) Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol 16.  https://doi.org/10.1186/S13059-015-0706-1
  45. 45.
    Kang Y, Norris MH, Zarzycki-Siek J, Nierman WC, Donachie SP, Hoang TT (2011) Transcript amplification from single bacterium for transcriptome analysis. Genome Res 21(6):925–935.  https://doi.org/10.1101/gr.116103.110CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA (2015) The technology and biology of single-cell RNA sequencing. Mol Cell 58(4):610–620.  https://doi.org/10.1016/j.molcel.2015.04.005CrossRefPubMedGoogle Scholar
  47. 47.
    Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M, Leonhardt H, Heyn H, Hellmann I, Enard W (2017) Comparative analysis of single-cell RNA sequencing methods. Mol Cell 65(4):631–643.  https://doi.org/10.1016/j.molcel.2017.01.023CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Svensson V, Natarajan KN, Ly LH, Miragaia RJ, Labalette C, Macaulay IC, Cvejic A, Teichmann SA (2017) Power analysis of single-cell RNA-sequencing experiments. Nat Methods 14(4):381–387.  https://doi.org/10.1038/nmeth.4220CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Clark SJ, Lee HJ, Smallwood SA, Kelsey G, Reik W (2016) Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity. Genome Biol 17.  https://doi.org/10.1186/s13059-016-0944-x
  50. 50.
    Plongthongkum N, Diep DH, Zhang K (2014) Advances in the profiling of DNA modifications: cytosine methylation and beyond. Nat Rev Genet 15(10):647–661.  https://doi.org/10.1038/nrg3772CrossRefPubMedGoogle Scholar
  51. 51.
    Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H, Peet J, Andrews SR, Stegle O, Reik W, Kelsey G (2014) Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods 11(8):817–820.  https://doi.org/10.1038/Nmeth.3035CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Guo HS, Zhu P, Wu XL, Li XL, Wen L, Tang FC (2013) Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res 23(12):2126–2135.  https://doi.org/10.1101/gr.161679.113CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Miura F, Enomoto Y, Dairiki R, Ito T (2012) Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res 40(17).  https://doi.org/10.1093/nar/gks454CrossRefGoogle Scholar
  54. 54.
    Farlik M, Sheffield NC, Nuzzo A, Datlinger P, Schonegger A, Klughammer J, Bock C (2015) Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep 10(8):1386–1397.  https://doi.org/10.1016/j.celrep.2015.02.001CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Lorthongpanich C, Cheow LF, Balu S, Quake SR, Knowles BB, Burkholder WF, Solter D, Messerschmidt DM (2013) Single-cell DNA-methylation analysis reveals epigenetic chimerism in preimplantation embryos. Science 341(6150):1110–1112.  https://doi.org/10.1126/science.1240617CrossRefPubMedGoogle Scholar
  56. 56.
    Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, Haugen E, Sheffield NC, Stergachis AB, Wang H, Vernot B, Garg K, John S, Sandstrom R, Bates D, Boatman L, Canfield TK, Diegel M, Dunn D, Ebersol AK, Frum T, Giste E, Johnson AK, Johnson EM, Kutyavin T, Lajoie B, Lee BK, Lee K, London D, Lotakis D, Neph S, Neri F, Nguyen ED, Qu HZ, Reynolds AP, Roach V, Safi A, Sanchez ME, Sanyal A, Shafer A, Simon JM, Song LY, Vong S, Weaver M, Yan YQ, Zhang ZC, Zhang ZZ, Lenhard B, Tewari M, Dorschner MO, Hansen RS, Navas PA, Stamatoyannopoulos G, Iyer VR, Lieb JD, Sunyaev SR, Akey JM, Sabo PJ, Kaul R, Furey TS, Dekker J, Crawford GE, Stamatoyannopoulos JA (2012) The accessible chromatin landscape of the human genome. Nature 489(7414):75–82.  https://doi.org/10.1038/nature11232CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Schones DE, Cui KR, Cuddapah S, Roh TY, Barski A, Wang ZB, Wei G, Zhao KJ (2008) Dynamic regulation of nucleosome positioning in the human genome. Cell 132(5):887–898.  https://doi.org/10.1016/j.cell.2008.02.022CrossRefPubMedGoogle Scholar
  58. 58.
    Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ (2013) Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 10(12):1213.  https://doi.org/10.1038/Nmeth.2688CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Adli M, Zhu JA, Bernstein BE (2010) Genome-wide chromatin maps derived from limited numbers of hematopoietic progenitors. Nat Methods 7(8):615–U624.  https://doi.org/10.1038/Nmeth.1478CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Lara-Astiaso D, Weiner A, Lorenzo-Vivas E, Zaretsky I, Jaitin DA, David E, Keren-Shaul H, Mildner A, Winter D, Jung S, Friedman N, Amit I (2014) Chromatin state dynamics during blood formation. Science 345(6199):943–949.  https://doi.org/10.1126/science.1256271CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Brind'Amour J, Liu S, Hudson M, Chen C, Karimi MM, Lorincz MC (2015) An ultra-low-input native ChIP-seq protocol for genome-wide profiling of rare cell populations. Nat Commun 6.  https://doi.org/10.1038/Ncomms7033
  62. 62.
    Cao ZN, Chen CY, He B, Tan K, Lu C (2015) A microfluidic device for epigenomic profiling using 100 cells. Nat Methods 12(10):959–962.  https://doi.org/10.1038/Nmeth.3488CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Cusanovich DA, Daza R, Adey A, Pliner HA, Christiansen L, Gunderson KL, Steemers FJ, Trapnell C, Shendure J (2015) Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348(6237):910–914.  https://doi.org/10.1126/science.aab1601CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Buenostro JD, Wu BJ, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, Chang HY, Greenleaf WJ (2015) Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523(7561):486–U264.  https://doi.org/10.1038/nature14590CrossRefGoogle Scholar
  65. 65.
    Dekker J, Rippe K, Dekker M, Kleckner N (2002) Capturing chromosome conformation. Science 295(5558):1306–1311.  https://doi.org/10.1126/science.1067799CrossRefPubMedGoogle Scholar
  66. 66.
    Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, Amit I, Lajoie BR, Sabo PJ, Dorschner MO, Sandstrom R, Bernstein B, Bender MA, Groudine M, Gnirke A, Stamatoyannopoulos J, Mirny LA, Lander ES, Dekker J (2009) Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326(5950):289–293.  https://doi.org/10.1126/science.1181369CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    Simonis M, Klous P, Splinter E, Moshkin Y, Willemsen R, de Wit E, van Steensel B, de Laat W (2006) Nuclear organization of active and inactive chromatin domains uncovered by chromosome conformation capture-on-chip (4C). Nat Genet 38(11):1348–1354.  https://doi.org/10.1038/ng1896CrossRefPubMedGoogle Scholar
  68. 68.
    Lanctot C, Cheutin T, Cremer M, Cavalli G, Cremer T (2007) Dynamic genome architecture in the nuclear space: regulation of gene expression in three dimensions. Nat Rev Genet 8(2):104–115.  https://doi.org/10.1038/nrg2041CrossRefPubMedGoogle Scholar
  69. 69.
    Parada LA, Roix JJ, Misteli T (2003) An uncertainty principle in chromosome positioning. Trends Cell Biol 13(8):393–396.  https://doi.org/10.1016/S0962-8924(03)00149-1CrossRefPubMedGoogle Scholar
  70. 70.
    Nagano T, Lubling Y, Stevens TJ, Schoenfelder S, Yaffe E, Dean W, Laue ED, Tanay A, Fraser P (2013) Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502(7469):59.  https://doi.org/10.1038/nature12593CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Dey SS, Kester L, Spanjaard B, Bienko M, van Oudenaarden A (2015) Integrated genome and transcriptome sequencing of the same cell. Nat Biotechnol 33(3):285.  https://doi.org/10.1038/nbt.3129CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Macaulay IC, Teng MJ, Haerty W, Kumar P, Ponting CP, Voet T (2016) Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq. Nat Protoc 11(11):36–58.  https://doi.org/10.1038/nprot.2016.138CrossRefGoogle Scholar
  73. 73.
    Macaulay IC, Haerty W, Kumar P, Li YI, Hu TX, Teng MJ, Goolam M, Saurat N, Coupland P, Shirley LM, Smith M, Van der Aa N, Banerjee R, Ellis PD, Quail MA, Swerdlow HP, Zernicka-Goetz M, Livesey FJ, Ponting CP, Voet T (2015) G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat Methods 12(6):519.  https://doi.org/10.1038/nmeth.3370CrossRefPubMedGoogle Scholar
  74. 74.
    Hu YJ, Huang K, An Q, Du GZ, Hu GL, Xue JF, Zhu XM, Wang CY, Xue ZG, Fan GP (2016) Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biol 17.  https://doi.org/10.1186/s13059-016-0950-z
  75. 75.
    Zong CH, Lu SJ, Chapman AR, Xie XS (2012) Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338(6114):1622–1626.  https://doi.org/10.1126/science.1229164CrossRefPubMedPubMedCentralGoogle Scholar
  76. 76.
    Picelli S, Faridani OR, Bjorklund AK, Winberg G, Sagasser S, Sandberg R (2014) Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc 9(1):171–181.  https://doi.org/10.1038/nprot.2014.006CrossRefPubMedGoogle Scholar
  77. 77.
    Laszlo AH, Derrington IM, Brinkerhoff H, Langford KW, Nova IC, Samson JM, Bartlett JJ, Pavlenok M, Gundlach JH (2013) Detection and mapping of 5-methylcytosine and 5-hydroxymethylcytosine with nanopore MspA. Proc Natl Acad Sci U S A 110(47):18904–18909.  https://doi.org/10.1073/pnas.1310240110CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.
    Laszlo AH, Derrington IM, Manrao EA, Gundlach JH (2013) Detection and mapping of 5-methylcytosine and 5-hydroxymethylcytosine in short strands of ssDNA using nanopore sequencing with MspA. Biophys J 104(2):211aCrossRefGoogle Scholar
  79. 79.
    Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, Teichmann SA, Marioni JC, Stegie O (2015) Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotechnol 33(2):155–160.  https://doi.org/10.1038/nbt.3102CrossRefPubMedGoogle Scholar
  80. 80.
    Buettner F, Pratanwanich N, Marioni JC, Stegle O (2016) Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects. bioRxiv 2016:087775Google Scholar
  81. 81.
    Leng N, Choi J, Chu LF, Thomson JA, Kendziorski C, Stewart R (2016) OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data. Bioinformatics 32(9):1408–1410.  https://doi.org/10.1093/bioinformatics/btw004CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Brennecke P, Anders S, Kim JK, Kolodziejczyk AA, Zhang XW, Proserpio V, Baying B, Benes V, Teichmann SA, Marioni JC, Heisler MG (2013) Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods 10(11):1093–1095.  https://doi.org/10.1038/Nmeth.2645CrossRefPubMedGoogle Scholar
  83. 83.
    Katayama S, Tohonen V, Linnarsson S, Kere J (2013) SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization. Bioinformatics 29(22):2943–2945.  https://doi.org/10.1093/bioinformatics/btt511CrossRefPubMedPubMedCentralGoogle Scholar
  84. 84.
    Ding B, Zheng LN, Zhu Y, Li N, Jia HY, Ai RZ, Wildberg A, Wang W (2015) Normalization and noise reduction for single cell RNA-seq experiments. Bioinformatics 31(13):2225–2227.  https://doi.org/10.1093/bioinformatics/btv122CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Bacher R, Chu L-F, Leng N, Gasch AP, Thomson JA, Stewart RM, Newton M, Kendziorski C (2016) SCnorm: a quantile-regression based approach for robust normalization of single-cell RNA-seq data. bioRxiv 2016:090167Google Scholar
  86. 86.
    Sengupta D, Rayan NA, Lim M, Lim B, Prabhakar S (2016) Fast, scalable and accurate differential expression analysis for single cells. bioRxiv 2016:049734Google Scholar
  87. 87.
    Cole M RD, Wagner A, Ngai J, Purdom E, Dudoit S, Yosef N. SCONE: correcting and evaluating the influence of unwanted variation on single-cell RNA-seq data. https://niryosef.wordpress.com/tools/scone/
  88. 88.
    Kharchenko PV, Silberstein L, Scadden DT (2014) Bayesian approach to single-cell differential expression analysis. Nat Methods 11(7):740–U184.  https://doi.org/10.1038/Nmeth.2967CrossRefPubMedPubMedCentralGoogle Scholar
  89. 89.
    Vieth B, Ziegenhain C, Parekh S, Enard W, Hellmann I (2017) powsimR: power analysis for bulk and single cell RNA-seq experiments. bioRxiv 2017:117150Google Scholar
  90. 90.
    Finak G, McDavid A, Yajima M, Deng JY, Gersuk V, Shalek AK, Slichter CK, Miller HW, McElrath MJ, Prlic M, Linsley PS, Gottardo R (2015) MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol 16.  https://doi.org/10.1186/S13059-015-0844-5
  91. 91.
    Delmans M, Hemberg M (2016) Discrete distributional differential expression ((DE)-E-3) – a tool for gene expression analysis of single-cell RNA-seq data. BMC Bioinformatics 17.  https://doi.org/10.1186/S12859-016-0944-6
  92. 92.
    Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C (2016) A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biol 17(1):222.  https://doi.org/10.1186/s13059-016-1077-yCrossRefPubMedPubMedCentralGoogle Scholar
  93. 93.
    Korthauer KD, Chu L-F, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C (2016) scDD: a statistical approach for identifying differential distributions in single-cell RNA-seq experiments. bioRxiv 2016:035501Google Scholar
  94. 94.
    Jia C, Kelly D, Kim J, Li M, Zhang N (2017) Accounting for technical noise in single-cell RNA sequencing analysis. bioRxiv 2017:116939Google Scholar
  95. 95.
    Svensson V, Teichmann SA, Stegle O (2017) SpatialDE-identification of spatially variable genes. bioRxiv 2017:143321Google Scholar
  96. 96.
    Grun D, Lyubimova A, Kester L, Wiebrands K, Basak O, Sasaki N, Clevers H, van Oudenaarden A (2015) Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525(7568):251.  https://doi.org/10.1038/nature14966CrossRefPubMedGoogle Scholar
  97. 97.
    Zeisel A, Munoz-Manchado AB, Codeluppi S, Lonnerberg P, La Manno G, Jureus A, Marques S, Munguba H, He LQ, Betsholtz C, Rolny C, Castelo-Branco G, Hjerling-Leffler J, Linnarsson S (2015) Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347(6226):1138–1142.  https://doi.org/10.1126/science.aaa1934CrossRefPubMedGoogle Scholar
  98. 98.
    Pierson E, Yau C (2015) ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol 16.  https://doi.org/10.1186/S13059-015-0805-Z
  99. 99.
    Angerer P, Haghverdi L, Buttner M, Theis FJ, Marr C, Buettner F (2016) destiny: diffusion maps for large-scale single cell data in R. Bioinformatics 32(8):1241–1243.  https://doi.org/10.1093/bioinformatics/btv715CrossRefPubMedGoogle Scholar
  100. 100.
    Xu C, Su ZC (2015) Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31(12):1974–1980.  https://doi.org/10.1093/bioinformatics/btv088CrossRefPubMedGoogle Scholar
  101. 101.
    Marco E, Karp RL, Guo GJ, Robson P, Hart AH, Trippa L, Yuan GC (2014) Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc Natl Acad Sci U S A 111(52):E5643–E5650.  https://doi.org/10.1073/pnas.1408993111CrossRefPubMedPubMedCentralGoogle Scholar
  102. 102.
    Leng N, Chu LF, Barry C, Li Y, Choi J, Li XM, Jiang P, Stewart RM, Thomson JA, Kendziorski C (2015) Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments. Nat Methods 12(10):947–950.  https://doi.org/10.1038/Nmeth.3549CrossRefPubMedPubMedCentralGoogle Scholar
  103. 103.
    Ji ZC, Ji HK (2016) TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res 44(13).  https://doi.org/10.1093/nar/gkw430CrossRefGoogle Scholar
  104. 104.
    Specht AT, Li J (2017) LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering. Bioinformatics 33(5):764–766.  https://doi.org/10.1093/bioinformatics/btw729CrossRefPubMedGoogle Scholar
  105. 105.
    Welch JD, Hartemink AJ, Prins JF (2016) SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data. Genome Biol 17.  https://doi.org/10.1186/S13059-016-0975-3
  106. 106.
    duVerle D, Yotsukura S, Nomura S, Aburatani H, Tsuda K (2016) CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data. BMC Bioinformatics 17.  https://doi.org/10.1186/S12859-016-1175-6
  107. 107.
    Rashid S, Kotton DN, Bar-Joseph Z (2017) TASIC: determining branching models from time series single cell data. Bioinformatics.  https://doi.org/10.1093/bioinformatics/btx173CrossRefGoogle Scholar
  108. 108.
    Lönnberg T, Svensson V, James KR, Fernandez-Ruiz D, Sebina I, Montandon R, Soon MS, Fogg LG, Nair AS, Liligeto U (2017) Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria. Sci Immunol 2(9):eaa12192CrossRefGoogle Scholar
  109. 109.
    Matsumoto H, Kiryu H (2016) SCOUP: a probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation. BMC Bioinformatics 17.  https://doi.org/10.1186/S12859-016-1109-3
  110. 110.
    McCarthy DJ, Campbell KR, Lun ATL, Wills QF (2017) Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33(8):1179–1186.  https://doi.org/10.1093/bioinformatics/btw777CrossRefPubMedPubMedCentralGoogle Scholar
  111. 111.
    Campbell KR, Yau C (2017) switchde: inference of switch-like differential expression along single-cell trajectories. Bioinformatics 33(8):1241–1242.  https://doi.org/10.1093/bioinformatics/btw798CrossRefPubMedGoogle Scholar
  112. 112.
    Campbell K, Yau C (2016) Ouija: incorporating prior knowledge in single-cell trajectory learning using Bayesian nonlinear factor analysis. bioRxiv 2016:060442Google Scholar
  113. 113.
    Campbell K, Ponting CP, Webber C (2015) Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell RNA-seq profiles. bioRxiv 2015:027219Google Scholar
  114. 114.
    Risso D, Perraudeau F, Gribkova S, Dudoit S, Vert J-P (2017) ZINB-WaVE: a general and flexible method for signal extraction from single-cell RNA-seq data. bioRxiv 2017:125112Google Scholar
  115. 115.
    Shaham U, Stanton KP, Li H, Montgomery R, Kluger Y (2016) Removal of batch effects using distribution-matching residual networks. arXiv 2016:161004181Google Scholar
  116. 116.
    Streets AM, Huang YY (2014) How deep is enough in single-cell RNA-seq? Nat Biotechnol 32(10):1005–1006.  https://doi.org/10.1038/nbt.3039CrossRefPubMedGoogle Scholar
  117. 117.
    Saliba AE, Westermann AJ, Gorski SA, Vogel J (2014) Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res 42(14):8845–8860.  https://doi.org/10.1093/nar/gku555CrossRefPubMedPubMedCentralGoogle Scholar
  118. 118.
    Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25(9):1105–1111.  https://doi.org/10.1093/bioinformatics/btp120CrossRefPubMedPubMedCentralGoogle Scholar
  119. 119.
    Guttman M, Garber M, Levin JZ, Donaghey J, Robinson J, Adiconis X, Fan L, Koziol MJ, Gnirke A, Nusbaum C, Rinn JL, Lander ES, Regev A (2010) Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat Biotechnol 28(5):503–U166.  https://doi.org/10.1038/nbt.1633CrossRefPubMedPubMedCentralGoogle Scholar
  120. 120.
    Jiang LC, Schlesinger F, Davis CA, Zhang Y, Li RH, Salit M, Gingeras TR, Oliver B (2011) Synthetic spike-in standards for RNA-seq experiments. Genome Res 21(9):1543–1551.  https://doi.org/10.1101/gr.121095.111CrossRefPubMedPubMedCentralGoogle Scholar
  121. 121.
    Fu GK, Hu J, Wang PH, Fodor SPA (2011) Counting individual DNA molecules by the stochastic attachment of diverse labels. Proc Natl Acad Sci U S A 108(22):9026–9031.  https://doi.org/10.1073/pnas.1017621108CrossRefPubMedPubMedCentralGoogle Scholar
  122. 122.
    Anders S, Pyl PT, Huber W (2015) HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics 31(2):166–169.  https://doi.org/10.1093/bioinformatics/btu638CrossRefPubMedGoogle Scholar
  123. 123.
    Welch JD, Hu Y, Prins JF (2016) Robust detection of alternative splicing in a population of single cells. Nucleic Acids Res 44(8).  https://doi.org/10.1093/nar/gkv1525CrossRefGoogle Scholar
  124. 124.
    Qiu X, Hill A, Packer J, Lin D, Ma YA, Trapnell C (2017) Single-cell mRNA quantification and differential analysis with census. Nat Methods 14(3):309–315.  https://doi.org/10.1038/nmeth.4150CrossRefPubMedPubMedCentralGoogle Scholar
  125. 125.
    Davis MP, van Dongen S, Abreu-Goodger C, Bartonicek N, Enright AJ (2013) Kraken: a set of tools for quality control and analysis of high-throughput sequence data. Methods 63(1):41–49.  https://doi.org/10.1016/j.ymeth.2013.06.027CrossRefPubMedPubMedCentralGoogle Scholar
  126. 126.
    Thorvaldsdottir H, Robinson JT, Mesirov JP (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14(2):178–192.  https://doi.org/10.1093/bib/bbs017CrossRefPubMedGoogle Scholar
  127. 127.
    Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP (2011) Integrative genomics viewer. Nat Biotechnol 29(1):24–26.  https://doi.org/10.1038/nbt.1754CrossRefPubMedPubMedCentralGoogle Scholar
  128. 128.
    Krebs JE, Lewin B, Goldstein ES, Kilpatrick ST (2014) Lewin’s genes XI. Jones & Bartlett Publishers, Burlington, MAGoogle Scholar
  129. 129.
    Brennecke P, Anders S, Kim JK, Kolodziejczyk AA, Zhang XW, Proserpio V, Baying B, Benes V, Teichmann SA, Marioni JC, Heisler MG (2014) Accounting for technical noise in single-cell RNA-seq experiments (vol 10: p. 1093, 2013). Nat Methods 11(2):210–210.  https://doi.org/10.1038/nmeth0214-210bCrossRefGoogle Scholar
  130. 130.
    Vallejos CA, Marioni JC, Richardson S (2015) BASiCS: Bayesian analysis of single-cell sequencing data. PLoS Comput Biol 11(6).  https://doi.org/10.1371/journal.pcbi.1004333CrossRefGoogle Scholar
  131. 131.
    Vallejos CA, Richardson S, Marioni JC (2016) Beyond comparisons of means: understanding changes in gene expression at the single-cell level. Genome Biol 17.  https://doi.org/10.1186/s13059-016-0930-3
  132. 132.
    Risso D, Ngai J, Speed TP, Dudoit S (2014) Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol 32(9):896–902.  https://doi.org/10.1038/nbt.2931CrossRefPubMedPubMedCentralGoogle Scholar
  133. 133.
    Yadav VK, De S (2015) An assessment of computational methods for estimating purity and clonality using genomic data derived from heterogeneous tumor tissue samples. Brief Bioinform 16(2):232–241.  https://doi.org/10.1093/bib/bbu002CrossRefPubMedGoogle Scholar
  134. 134.
    Tang FC, Barbacioru C, Bao SQ, Lee C, Nordman E, Wang XH, Lao KQ, Surani MA (2010) Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-seq analysis. Cell Stem Cell 6(5):468–478.  https://doi.org/10.1016/j.stem.2010.03.015CrossRefPubMedPubMedCentralGoogle Scholar
  135. 135.
    Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH, Desai TJ, Krasnow MA, Quake SR (2014) Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509(7500):371.  https://doi.org/10.1038/nature13173CrossRefPubMedPubMedCentralGoogle Scholar
  136. 136.
    Durruthy-Durruthy R, Gottlieb A, Hartman BH, Waldhaus J, Laske RD, Altman R, Heller S (2014) Reconstruction of the mouse otocyst and early neuroblast lineage at single-cell resolution. Cell 157(4):964–978.  https://doi.org/10.1016/j.cell.2014.03.036CrossRefPubMedPubMedCentralGoogle Scholar
  137. 137.
    Moignard V, Macaulay IC, Swiers G, Buettner F, Schutte J, Calero-Nieto FJ, Kinston S, Joshi A, Hannah R, Theis FJ, Jacobsen SE, de Bruijn MF, Gottgens B (2013) Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysisNat. Cell Biol 15(4):363.  https://doi.org/10.1038/ncb2709CrossRefGoogle Scholar
  138. 138.
    Mahata B, Zhang XW, Kolodziejczyk AA, Proserpio V, Haim-Vilmovsky L, Taylor AE, Hebenstreit D, Dingler FA, Moignard V, Gottgens B, Arlt W, McKenzie ANJ, Teichmann SA (2014) Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis. Cell Rep 7(4):1130–1142.  https://doi.org/10.1016/j.celrep.2014.04.011CrossRefPubMedPubMedCentralGoogle Scholar
  139. 139.
    Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, Cahill DP, Nahed BV, Curry WT, Martuza RL, Louis DN, Rozenblatt-Rosen O, Suva ML, Regev A, Bernstein BE (2014) Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344(6190):1396–1401.  https://doi.org/10.1126/science.1254257CrossRefPubMedPubMedCentralGoogle Scholar
  140. 140.
    Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10).  https://doi.org/10.1186/Gb-2010-11-10-R106CrossRefGoogle Scholar
  141. 141.
    Zhou XB, Lindsay H, Robinson MD (2014) Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Res 42(11).  https://doi.org/10.1093/nar/gku310CrossRefGoogle Scholar
  142. 142.
    Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140.  https://doi.org/10.1093/bioinformatics/btp616CrossRefPubMedPubMedCentralGoogle Scholar
  143. 143.
    Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L (2014) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks (vol 7: p. 562, 2012). Nat Protoc 9(10):2513–2513.  https://doi.org/10.1038/nprot1014-2513aCrossRefGoogle Scholar
  144. 144.
    Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 7(3):562–578.  https://doi.org/10.1038/nprot.2012.016CrossRefPubMedPubMedCentralGoogle Scholar
  145. 145.
    Rapaport F, Khanin R, Liang YP, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND, Betel D (2013) Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol 14(9).  https://doi.org/10.1186/Gb-2013-14-9-R95CrossRefGoogle Scholar
  146. 146.
    Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT, Raychowdhury R, Schwartz S, Yosef N, Malboeuf C, Lu DN, Trombetta JJ, Gennert D, Gnirke A, Goren A, Hacohen N, Levin JZ, Park H, Regev A (2013) Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498(7453):236–240.  https://doi.org/10.1038/nature12172CrossRefPubMedPubMedCentralGoogle Scholar
  147. 147.
    Anders S, Reyes A, Huber W (2012) Detecting differential usage of exons from RNA-seq data. Genome Res 22(10):2008–2017.  https://doi.org/10.1101/gr.133744.111CrossRefPubMedPubMedCentralGoogle Scholar
  148. 148.
    Katz Y, Wang ET, Airoldi EM, Burge CB (2010) Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nat Methods 7(12):1009–U1101.  https://doi.org/10.1038/Nmeth.1528CrossRefPubMedPubMedCentralGoogle Scholar
  149. 149.
    Kim JK, Marioni JC (2013) Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data. Genome Biol 14(1):R7.  https://doi.org/10.1186/gb-2013-14-1-r7CrossRefPubMedPubMedCentralGoogle Scholar
  150. 150.
    Deng Q, Ramskold D, Reinius B, Sandberg R (2014) Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343(6167):193–196.  https://doi.org/10.1126/science.1245316CrossRefPubMedGoogle Scholar
  151. 151.
    Grun D, Kester L, van Oudenaarden A (2014) Validation of noise models for single-cell transcriptomics. Nat Methods 11(6):637.  https://doi.org/10.1038/Nmeth.2930CrossRefPubMedGoogle Scholar
  152. 152.
    McManus CJ, Coolon JD, Duff MO, Eipper-Mains J, Graveley BR, Wittkopp PJ (2010) Regulatory divergence in Drosophila revealed by mRNA-seq. Genome Res 20(6):816–825.  https://doi.org/10.1101/gr.102491.109CrossRefPubMedPubMedCentralGoogle Scholar
  153. 153.
    Gawad C, Koh W, Quake SR (2014) Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. Proc Natl Acad Sci U S A 111(50):17947–17952.  https://doi.org/10.1073/pnas.1420822111CrossRefPubMedPubMedCentralGoogle Scholar
  154. 154.
    Zhang CZ, Adalsteinsson VA, Francis J, Cornils H, Jung J, Maire C, Ligon KL, Meyerson M, Love JC (2015) Calibrating genomic and allelic coverage bias in single-cell sequencing. Nat Commun 6:6822.  https://doi.org/10.1038/ncomms7822CrossRefPubMedPubMedCentralGoogle Scholar
  155. 155.
    Daley T, Smith AD (2014) Modeling genome coverage in single-cell sequencing. Bioinformatics 30(22):3159–3165.  https://doi.org/10.1093/bioinformatics/btu540CrossRefPubMedPubMedCentralGoogle Scholar
  156. 156.
    Clingenpeel S, Clum A, Schwientek P, Rinke C, Woyke T (2015) Reconstructing each cell's genome within complex microbial communities-dream or reality? Front Microbiol 5.  https://doi.org/10.3389/Fmicb.2014.00771
  157. 157.
    Wang Y, Waters J, Leung ML, Unruh A, Roh W, Shi X, Chen K, Scheet P, Vattathil S, Liang H, Multani A, Zhang H, Zhao R, Michor F, Meric-Bernstam F, Navin NE (2014) Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512(7513):155–160.  https://doi.org/10.1038/nature13600CrossRefPubMedPubMedCentralGoogle Scholar
  158. 158.
    Nikolenko SI, Korobeynikov AI, Alekseyev MA (2013) BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics 14(Suppl 1):S7.  https://doi.org/10.1186/1471-2164-14-S1-S7CrossRefPubMedPubMedCentralGoogle Scholar
  159. 159.
    Zhang C, Zhang C, Chen S, Yin X, Pan X, Lin G, Tan Y, Tan K, Xu Z, Hu P, Li X, Chen F, Xu X, Li Y, Zhang X, Jiang H, Wang W (2013) A single cell level based method for copy number variation analysis by low coverage massively parallel sequencing. PLoS One 8(1):e54236.  https://doi.org/10.1371/journal.pone.0054236CrossRefPubMedPubMedCentralGoogle Scholar
  160. 160.
    Cheng JQ, Vanneste E, Konings P, Voet T, Vermeesch JR, Moreau Y (2011) Single-cell copy number variation detection. Genome Biol 12(8).  https://doi.org/10.1186/Gb-2011-12-8-R80CrossRefGoogle Scholar
  161. 161.
    Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, Lesin VM, Nikolenko SI, Pham S, Prjibelski AD, Pyshkin AV, Sirotkin AV, Vyahhi N, Tesler G, Alekseyev MA, Pevzner PA (2012) SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19(5):455–477.  https://doi.org/10.1089/cmb.2012.0021CrossRefPubMedPubMedCentralGoogle Scholar
  162. 162.
    Peng Y, Leung HCM, Yiu SM, Chin FYL (2012) IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28(11):1420–1428.  https://doi.org/10.1093/bioinformatics/bts174CrossRefPubMedGoogle Scholar
  163. 163.
    Navin N, Kendall J, Troge J, Andrews P, Rodgers L, McIndoo J, Cook K, Stepansky A, Levy D, Esposito D, Muthuswamy L, Krasnitz A, McCombie WR, Hicks J, Wigler M (2011) Tumour evolution inferred by single-cell sequencing. Nature 472(7341):90–U119.  https://doi.org/10.1038/nature09807CrossRefPubMedPubMedCentralGoogle Scholar
  164. 164.
    Kuipers J, Jahn K, Beerenwinkel N (2017) Advances in understanding tumour evolution through single-cell sequencing. Biochim Biophys Acta 1867(2):127–138.  https://doi.org/10.1016/j.bbcan.2017.02.001CrossRefPubMedPubMedCentralGoogle Scholar
  165. 165.
    Nik-Zainal S, Van Loo P, Wedge DC, Alexandrov LB, Greenman CD, Lau KW, Raine K, Jones D, Marshall J, Ramakrishna M, Shlien A, Cooke SL, Hinton J, Menzies A, Stebbings LA, Leroy C, Jia MM, Rance R, Mudie LJ, Gamble SJ, Stephens PJ, McLaren S, Tarpey PS, Papaemmanuil E, Davies HR, Varela I, McBride DJ, Bignell GR, Leung K, Butler AP, Teague JW, Martin S, Jonsson G, Mariani O, Boyault S, Miron P, Fatima A, Langerod A, Aparicio SAJR, Tutt A, Sieuwerts AM, Borg A, Thomas G, Salomon AV, Richardson AL, Borresen-Dale AL, Futreal PA, Stratton MR, Campbell PJ, Consortium ICG (2012) The life history of 21 breast cancers. Cell 149(5).  https://doi.org/10.1016/j.cell.2012.04.023CrossRefGoogle Scholar
  166. 166.
    Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95(25):14863–14868.  https://doi.org/10.1073/pnas.95.25.14863CrossRefPubMedPubMedCentralGoogle Scholar
  167. 167.
    Roth A, McPherson A, Laks E, Biele J, Yap D, Wan A, Smith MA, Nielsen CB, McAlpine JN, Aparicio S, Bouchard-Cote A, Shah SP (2016) Clonal genotype and population structure inference from single-cell tumor sequencing. Nat Methods 13(7):573.  https://doi.org/10.1038/Nmeth.3867CrossRefPubMedGoogle Scholar
  168. 168.
    Fraley C, Raftery AE (2002) Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc 97(458):611–631.  https://doi.org/10.1198/016214502760047131CrossRefGoogle Scholar
  169. 169.
    Meng XL, Rubin DB (1993) Maximum-likelihood-estimation via the Ecm algorithm–a general framework. Biometrika 80(2):267–278.  https://doi.org/10.2307/2337198CrossRefGoogle Scholar
  170. 170.
    Fraley C, Raftery AE (1998) How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput J 41(8):578–588CrossRefGoogle Scholar
  171. 171.
    Fraley C, Raftery AE (1999) MCLUST: software for model-based cluster analysis. J Classif 16(2):297–306CrossRefGoogle Scholar
  172. 172.
    Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, Hall KP, Evers DJ, Barnes CL, Bignell HR, Boutell JM, Bryant J, Carter RJ, Cheetham RK, Cox AJ, Ellis DJ, Flatbush MR, Gormley NA, Humphray SJ, Irving LJ, Karbelashvili MS, Kirk SM, Li H, Liu XH, Maisinger KS, Murray LJ, Obradovic B, Ost T, Parkinson ML, Pratt MR, Rasolonjatovo IMJ, Reed MT, Rigatti R, Rodighiero C, Ross MT, Sabot A, Sankar SV, Scally A, Schroth GP, Smith ME, Smith VP, Spiridou A, Torrance PE, Tzonev SS, Vermaas EH, Walter K, Wu XL, Zhang L, Alam MD, Anastasi C, Aniebo IC, Bailey DMD, Bancarz IR, Banerjee S, Barbour SG, Baybayan PA, Benoit VA, Benson KF, Bevis C, Black PJ, Boodhun A, Brennan JS, Bridgham JA, Brown RC, Brown AA, Buermann DH, Bundu AA, Burrows JC, Carter NP, Castillo N, Catenazzi MCE, Chang S, Cooley RN, Crake NR, Dada OO, Diakoumakos KD, Dominguez-Fernandez B, Earnshaw DJ, Egbujor UC, Elmore DW, Etchin SS, Ewan MR, Fedurco M, Fraser LJ, Fajardo KVF, Furey WS, George D, Gietzen KJ, Goddard CP, Golda GS, Granieri PA, Green DE, Gustafson DL, Hansen NF, Harnish K, Haudenschild CD, Heyer NI, Hims MM, Ho JT, Horgan AM, Hoschler K, Hurwitz S, Ivanov DV, Johnson MQ, James T, Jones TAH, Kang GD, Kerelska TH, Kersey AD, Khrebtukova I, Kindwall AP, Kingsbury Z, Kokko-Gonzales PI, Kumar A, Laurent MA, Lawley CT, Lee SE, Lee X, Liao AK, Loch JA, Lok M, Luo SJ, Mammen RM, Martin JW, McCauley PG, McNitt P, Mehta P, Moon KW, Mullens JW, Newington T, Ning ZM, Ng BL, Novo SM, O'Neill MJ, Osborne MA, Osnowski A, Ostadan O, Paraschos LL, Pickering L, Pike AC, Pike AC, Pinkard DC, Pliskin DP, Podhasky J, Quijano VJ, Raczy C, Rae VH, Rawlings SR, Rodriguez AC, Roe PM, Rogers J, Bacigalupo MCR, Romanov N, Romieu A, Roth RK, Rourke NJ, Ruediger ST, Rusman E, Sanches-Kuiper RM, Schenker MR, Seoane JM, Shaw RJ, Shiver MK, Short SW, Sizto NL, Sluis JP, Smith MA, Sohna JES, Spence EJ, Stevens K, Sutton N, Szajkowski L, Tregidgo CL, Turcatti G, vandeVondele S, Verhovsky Y, Virk SM, Wakelin S, Walcott GC, Wang JW, Worsley GJ, Yan JY, Yau L, Zuerlein M, Rogers J, Mullikin JC, Hurles ME, McCooke NJ, West JS, Oaks FL, Lundberg PL, Klenerman D, Durbin R, Smith AJ (2008) Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456(7218):53–59.  https://doi.org/10.1038/nature07517CrossRefPubMedPubMedCentralGoogle Scholar
  173. 173.
    Kim KI, Simon R (2014) Using single cell sequencing data to model the evolutionary history of a tumor. BMC Bioinformatics 15.  https://doi.org/10.1186/1471-2105-15-27
  174. 174.
    Yang ZH, Rannala B (2012) Molecular phylogenetics: principles and practice. Nat Rev Genet 13(5):303–314.  https://doi.org/10.1038/nrg3186CrossRefPubMedGoogle Scholar
  175. 175.
    Miller CA, McMichael J, Dang HX, Maher CA, Ding L, Ley TJ, Mardis ER, Wilson RK (2016) Visualizing tumor evolution with the fishplot package for R. BMC Genomics 17.  https://doi.org/10.1186/S12864-016-3195-Z
  176. 176.
    Ross EM, Markowetz F (2016) OncoNEM: inferring tumor evolution from single-cell sequencing data. Genome Biol 17.  https://doi.org/10.1186/s13059-016-0929-9
  177. 177.
    Jahn K, Kuipers J, Beerenwinkel N (2016) Tree inference for single-cell data. Genome Biol 17.  https://doi.org/10.1186/s13059-016-0936-x
  178. 178.
    Roth A, Khattra J, Yap D, Wan A, Laks E, Biele J, Ha G, Aparicio S, Bouchard-Cote A, Shah SP (2014) PyClone: statistical inference of clonal population structure in cancer. Nat Methods 11(4):396.  https://doi.org/10.1038/Nmeth.2883CrossRefPubMedPubMedCentralGoogle Scholar
  179. 179.
    Jiao W, Vembu S, Deshwar AG, Stein L, Morris Q (2014) Inferring clonal evolution of tumors from single nucleotide somatic mutations. BMC Bioinformatics 15.  https://doi.org/10.1186/1471-2105-15-35
  180. 180.
    Ha G, Roth A, Khattra J, Ho J, Yap DM, Prentice LM, Melnyk N, McPherson A, Bashashati A, Laks E, Biele J, Ding JR, Le A, Rosner J, Shumansky K, Marra MA, Gilks CB, Huntsman DG, McAlpine JN, Aparicio S, Shah SP (2014) TITAN: inference of copy number architectures, in clonal cell populations from tumor whole-genome sequence data. Genome Res 24(11):1881–1893.  https://doi.org/10.1101/gr.180281.114CrossRefPubMedPubMedCentralGoogle Scholar
  181. 181.
    Strino F, Parisi F, Micsinai M, Kluger Y (2013) TrAp: a tree approach for fingerprinting subclonal tumor composition. Nucleic Acids Res 41(17).  https://doi.org/10.1093/nar/gkt641CrossRefGoogle Scholar
  182. 182.
    El-Kebir M, Oesper L, Acheson-Field H, Raphael BJ (2015) Reconstruction of clonal trees and tumor composition from multi-sample sequencing data. Bioinformatics 31(12):62–70.  https://doi.org/10.1093/bioinformatics/btv261CrossRefGoogle Scholar
  183. 183.
    Qiao Y, Quinlan AR, Jazaeri AA, Verhaak RGW, Wheeler DA, Marth GT (2014) SubcloneSeeker: a computational framework for reconstructing tumor clone structure for cancer variant interpretation and prioritization. Genome Biol 15(8).  https://doi.org/10.1186/S13059-014-0443-XCrossRefGoogle Scholar
  184. 184.
    Deshwar AG, Vembu S, Yung CK, Jang GH, Stein L, Morris Q (2015) PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors. Genome Biol 16.  https://doi.org/10.1186/s13059-015-0602-8CrossRefGoogle Scholar
  185. 185.
    Malikic S, McPherson AW, Donmez N, Sahinalp CS (2015) Clonality inference in multiple tumor samples using phylogeny. Bioinformatics 31(9):1349–1356.  https://doi.org/10.1093/bioinformatics/btv003CrossRefPubMedGoogle Scholar
  186. 186.
    Davis A, Navin NE (2016) Computing tumor trees from single cells. Genome Biol 17.  https://doi.org/10.1186/S13059-016-0987-Z
  187. 187.
    Potter NE, Ermini L, Papaemmanuil E, Cazzaniga G, Vijayaraghavan G, Titley I, Ford A, Campbell P, Kearney L, Greaves M (2013) Single-cell mutational profiling and clonal phylogeny in cancer. Genome Res 23(12):2115–2125.  https://doi.org/10.1101/gr.159913.113CrossRefPubMedPubMedCentralGoogle Scholar
  188. 188.
    Salehi S, Steif A, Roth A, Aparicio S, Bouchard-Cote A, Shah SP (2017) ddClone: joint statistical inference of clonal populations from single cell and bulk tumour sequencing data. Genome Biol 18.  https://doi.org/10.1186/s13059-017-1169-3
  189. 189.
    Miller CA, White BS, Dees ND, Griffith M, Welch JS, Griffith OL, Vij R, Tomasson MH, Graubert TA, Walter MJ, Ellis MJ, Schierding W, DiPersio JF, Ley TJ, Mardis ER, Wilson RK, Ding L (2014) SciClone: inferring clonal architecture and tracking the spatial and temporal patterns of tumor evolution. PLoS Comput Biol 10(8).  https://doi.org/10.1371/journal.pcbi.1003665CrossRefGoogle Scholar
  190. 190.
    Ernst J, Kellis M (2015) Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nat Biotechnol 33(4):364–U374.  https://doi.org/10.1038/nbt.3157CrossRefPubMedPubMedCentralGoogle Scholar
  191. 191.
    Stevens M, Cheng JB, Li DF, Xie MC, Hong CB, Maire CL, Ligon KL, Hirst M, Marra MA, Costello JF, Wang T (2013) Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods. Genome Res 23(9):1541–1553.  https://doi.org/10.1101/gr.152231.112CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Systems Medicine, School of Biomedical InformaticsUTHealth at HoustonHoustonUSA
  2. 2.Center for Bioinformatics and Systems BiologyWake Forest School of MedicineWinston-SalemUSA

Personalised recommendations