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Annual Review of Biomedical Data Science

Volume 4, 2021

Single-Cell Analysis for Whole-Organism Datasets

Abstract

Cell atlases are essential companions to the genome as they elucidate how genes are used in a cell type–specific manner or how the usage of genes changes over the lifetime of an organism. This review explores recent advances in whole-organism single-cell atlases, which enable understanding of cell heterogeneity and tissue and cell fate, both in health and disease. Here we provide an overview of recent efforts to build cell atlases across species and discuss the challenges that the field is currently facing. Moreover, we propose the concept of having a knowledgebase that can scale with the number of experiments and computational approaches and a new feedback loop for development and benchmarking of computational methods that includes contributions from the users. These two aspects are key for community efforts in single-cell biology that will help produce a comprehensive annotated map of cell types and states with unparalleled resolution.

Keyword(s): cell atlas, cell phenotype knowledgebase, crowdsourcing annotations, open source methods, single-cell data analysis, single-cell transcriptomics
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/content/journals/10.1146/annurev-biodatasci-092820-031008
2021-07-20
2025-04-30
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Literature Cited

  1. 1. 
    Regev A, Teichmann SA, Lander ES, Amit I, Benoist C, Yosef N. 2017. The Human Cell Atlas. bioRxiv 10.1101.121202. https://doi.org/10.1101/121202
    [Crossref]
  2. 2. 
    Tabula Muris Consort 2018. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562:367–72
     [Google Scholar]
  3. 3. 
    Tabula Muris Consort 2020. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583:590–95
     [Google Scholar]
  4. 4. 
    Wen L, Tang F. 2018. Boosting the power of single-cell analysis. Nat. Biotechnol. 36:408–9
     [Google Scholar]
  5. 5. 
    Bao F, Deng Y, Wan S, Wang B, Dai Q et al. 2020. Characterizing tissue composition through combined analysis of single-cell morphologies and transcriptional states. bioRxiv 2020.09.05.284539. https://doi.org/10.1101/2020.09.05.284539
    [Crossref]
  6. 6. 
    Leonelli S. 2019. The challenges of big data biology. eLife 8:e47381
     [Google Scholar]
  7. 7. 
    Angerer P, Simon L, Tritschler S, Wolf FA, Fischer D, Theis FJ. 2017. Single cells make big data: new challenges and opportunities in transcriptomics. Curr. Opin. Syst. Biol. 4:85–91
     [Google Scholar]
  8. 8. 
    Galler K, Bräutigam K, Große C, Popp J, Neugebauer U. 2014. Making a big thing of a small cell—recent advances in single cell analysis. Analyst 139:1237–73
     [Google Scholar]
  9. 9. 
    Park M, Vorperian S, Wang S, Pisco AO 2020. Single-cell identity definition using random forests and recursive feature elimination. bioRxiv 2020.08.03.233650. https://doi.org/10.1101/2020.08.03.233650
    [Crossref]
  10. 10. 
    Brbić M, Zitnik M, Wang S, Pisco AO, Altman RB et al. 2020. MARS: discovering novel cell types across heterogeneous single-cell experiments. Nat. Methods 17:1200–6
     [Google Scholar]
  11. 11. 
    Antolović V, Miermont A, Corrigan AM, Chubb JR. 2017. Generation of single-cell transcript variability by repression. Curr. Biol. 27:1811–17.e3
     [Google Scholar]
  12. 12. 
    Cao J, Packer JS, Ramani V, Cusanovich DA, Huynh C et al. 2017. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357:661–67
     [Google Scholar]
  13. 13. 
    Spanjaard B, Hu B, Mitic N, Olivares-Chauvet P, Janjuha S et al. 2018. Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars. Nat. Biotechnol. 36:469–73
     [Google Scholar]
  14. 14. 
    Alemany A, Florescu M, Baron CS, Peterson-Maduro J, van Oudenaarden A. 2018. Whole-organism clone tracing using single-cell sequencing. Nature 556:108–12
     [Google Scholar]
  15. 15. 
    Briggs JA, Weinreb C, Wagner DE, Megason S, Peshkin L et al. 2018. The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science 360:eaar5780
     [Google Scholar]
  16. 16. 
    Siebert S, Farrell JA, Cazet JF, Abeykoon Y, Primack AS et al. 2019. Stem cell differentiation trajectories in Hydra resolved at single-cell resolution. Science 365:eaav9314
     [Google Scholar]
  17. 17. 
    Svensson V, da Veiga Beltrame E, Pachter L. 2019. A curated database reveals trends in single-cell transcriptomics. Database 2020:baaa073
     [Google Scholar]
  18. 18. 
    Han X, Wang R, Zhou Y, Fei L, Sun H et al. 2018. Mapping the Mouse Cell Atlas by Microwell-Seq. Cell 172:1091–107.e17
     [Google Scholar]
  19. 19. 
    Domcke S, Hill AJ, Daza RM, Cao J, O'Day DR et al. 2020. A human cell atlas of fetal chromatin accessibility. Science 370:eaba7612
     [Google Scholar]
  20. 20. 
    Cao J, O'Day DR, Pliner HA, Kingsley PD, Deng M et al. 2020. A human cell atlas of fetal gene expression. Science 370:eaba7721
     [Google Scholar]
  21. 21. 
    Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM et al. 2019. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566:496–502
     [Google Scholar]
  22. 22. 
    Poran A, Nötzel C, Aly O, Mencia-Trinchant N, Harris CT et al. 2017. Single-cell RNA sequencing reveals a signature of sexual commitment in malaria parasites. Nature 551:95–99
     [Google Scholar]
  23. 23. 
    Reid AJ, Talman AM, Bennett HM, Gomes AR, Sanders MJ et al. 2018. Single-cell RNA-seq reveals hidden transcriptional variation in malaria parasites. eLife 7:e33105
     [Google Scholar]
  24. 24. 
    Fincher CT, Wurtzel O, de Hoog T, Kravarik KM, Reddien PW. 2018. Cell type transcriptome atlas for the planarian Schmidtea mediterranea. Science 360:eaaq1736
     [Google Scholar]
  25. 25. 
    Plass M, Solana J, Wolf FA, Ayoub S, Misios A et al. 2018. Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics. Science 360:eaaq1723
     [Google Scholar]
  26. 26. 
    Sebé-Pedrós A, Saudemont B, Chomsky E, Plessier F, Mailhé MP et al. 2018. Cnidarian cell type diversity and regulation revealed by whole-organism single-cell RNA-seq. Cell 173:1520–34.e20
     [Google Scholar]
  27. 27. 
    Horie R, Hazbun A, Chen K, Cao C, Levine M, Horie T. 2018. Shared evolutionary origin of vertebrate neural crest and cranial placodes. Nature 560:228–32
     [Google Scholar]
  28. 28. 
    Horie T, Horie R, Chen K, Cao C, Nakagawa M et al. 2018. Regulatory cocktail for dopaminergic neurons in a protovertebrate identified by whole-embryo single-cell transcriptomics. Genes Dev. 32:19–201297–302
     [Google Scholar]
  29. 29. 
    Nadal-Ribelles M, Islam S, Wei W, Latorre P, Nguyen M et al. 2019. Sensitive high-throughput single-cell RNA-seq reveals within-clonal transcript correlations in yeast populations. Nat. Microbiol. 4:683–92
     [Google Scholar]
  30. 30. 
    Musser JM, Schippers KJ, Nickel M, Mizzon G, Kohn AB et al. 2019. Profiling cellular diversity in sponges informs animal cell type and nervous system evolution. bioRxiv 758276. https://doi.org/10.1101/758276
    [Crossref]
  31. 31. 
    Travaglini KJ, Nabhan AN, Penland L, Sinha R, Gillich A et al. 2019. A molecular cell atlas of the human lung from single cell RNA sequencing. Nature 587:619–25
     [Google Scholar]
  32. 32. 
    Zeisel A, Hochgerner H, Lonnerberg P, Johnsson A, Memic F et al. 2018. Molecular architecture of the mouse nervous system. Cell 174:4999–1014.e22
     [Google Scholar]
  33. 33. 
    Hodge RD, Bakken TE, Miller JA, Smith KA, Barkan ER et al. 2019. Conserved cell types with divergent features in human versus mouse cortex. Nature 573:61–68
     [Google Scholar]
  34. 34. 
    Delaney C, Schnell A, Cammarata LV, Yao-Smith A, Regev A et al. 2019. Combinatorial prediction of marker panels from single-cell transcriptomic data. Mol. Syst. Biol. 15:e9005
     [Google Scholar]
  35. 35. 
    Okawa S, del Sol A 2019. A general computational approach to predicting synergistic transcriptional cores that determine cell subpopulation identities. Nucleic Acids Res. 47:3333–43
     [Google Scholar]
  36. 36. 
    Jiang J, Wang C, Qi R, Fu H, Ma Q. 2020. scREAD: a single-cell RNA-Seq database for Alzheimer's Disease. iScience 23:11101769
     [Google Scholar]
  37. 37. 
    Aldridge S, Teichmann SA. 2020. Single cell transcriptomics comes of age. Nat. Commun. 11:4307
     [Google Scholar]
  38. 38. 
    He S, Wang LH, Liu Y, Li YQ, Chen H et al. 2020. Single-cell transcriptome profiling an adult human cell atlas of 15 major organs. Genome Biol. 21:294
     [Google Scholar]
  39. 39. 
    He P, Williams BA, Trout D, Marinov GK, Amrhein H et al. 2020. The changing mouse embryo transcriptome at whole tissue and single-cell resolution. Nature 583:760–67
     [Google Scholar]
  40. 40. 
    Han X, Zhou Z, Fei L, Sun H, Wang R et al. 2020. Construction of a human cell landscape at single-cell level. Nature 581:303–9
     [Google Scholar]
  41. 41. 
    Ren L, Wu C, Guo L, Yao J, Wang C et al. 2020. Single-cell transcriptional atlas of the Chinese horseshoe bat (Rhinolophus sinicus) provides insight into the cellular mechanisms which enable bats to be viral reservoirs. bioRxiv 2020.06.30.175778. https://doi.org/10.1101/2020.06.30.175778
    [Crossref]
  42. 42. 
    Han L, Wei X, Liu C, Volpe G, Wang Z et al. 2020. Single-cell atlas of a non-human primate reveals new pathogenic mechanisms of COVID-19. bioRxiv 2020.04.10.022103. https://doi.org/10.1101/2020.04.10.022103
    [Crossref]
  43. 43. 
    Hedges SB. 2002. The origin and evolution of model organisms. Nat. Rev. Genet. 3:838–49
     [Google Scholar]
  44. 44. 
    Shafer MER. 2019. Cross-species analysis of single-cell transcriptomic data. Front. Cell Dev. Biol. 7:175
     [Google Scholar]
  45. 45. 
    Shapiro E, Biezuner T, Linnarsson S. 2013. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14:618–30
     [Google Scholar]
  46. 46. 
    Li X, Wang K, Lyu Y, Pan H, Zhang J et al. 2020. Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis. Nat. Commun. 11:2338
     [Google Scholar]
  47. 47. 
    Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. 2018. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15:1053–58
     [Google Scholar]
  48. 48. 
    Rizvi AH, Camara PG, Kandror EK, Roberts TJ, Schieren I et al. 2017. Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development. Nat. Biotechnol. 35:551–60
     [Google Scholar]
  49. 49. 
    Raser JM, O'Shea EK 2005. Noise in gene expression: origins, consequences, and control. Science 309:2010–13
     [Google Scholar]
  50. 50. 
    Elowitz MB, Levine AJ, Siggia ED, Swain PS. 2002. Stochastic gene expression in a single cell. Science 297:1183–86
     [Google Scholar]
  51. 51. 
    GTEx Consort 2020. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369:1318–30
     [Google Scholar]
  52. 52. 
    Cha J, Lee I. 2020. Single-cell network biology for resolving cellular heterogeneity in human diseases. Exp. Mol. Med. 52:1798–808
     [Google Scholar]
  53. 53. 
    FitzGerald G, Botstein D, Califf R, Collins R, Peters K et al. 2018. The future of humans as model organisms. Science 361:552–53
     [Google Scholar]
  54. 54. 
    Kiselev VY, Andrews TS, Hemberg M. 2019. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20:273–82
     [Google Scholar]
  55. 55. 
    Luecken M, Büttner M, Chaichoompu K, Danese A, Interlandi M et al. 2020. Benchmarking atlas-level data integration in single-cell genomics. bioRxiv 2020.05.22.111161. https://doi.org/10.1101/2020.05.22.111161
    [Crossref]
  56. 56. 
    Tran HTN, Ang KS, Chevrier M, Zhang X, Lee NYS et al. 2020. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21:12
     [Google Scholar]
  57. 57. 
    Lütge A, Zyprych-Walczak J, Kunzmann UB, Crowell H, Calini D et al. 2020. CellMixS: quantifying and visualizing batch effects in single cell RNA-seq data. bioRxiv 2020.12.11.420885. https://doi.org/10.1101/2020.12.11.420885
    [Crossref]
  58. 58. 
    Su S, Tian L, Dong X, Hickey PF, Freytag S, Ritchie ME. 2020. CellBench: R/Bioconductor software for comparing single-cell RNA-seq analysis methods. Bioinformatics 36:2288–90
     [Google Scholar]
  59. 59. 
    Editorial 2019. Optimizing biological inferences from single-cell data. Nat. Rev. Genet. 20:249
     [Google Scholar]
  60. 60. 
    Covarrubias AJ, Kale A, Perrone R, Lopez-Dominguez JA, Pisco AO et al. 2019. Aging-related inflammation driven by cellular senescence enhances NAD consumption via activation of CD38+ pro-inflammatory macrophages. Nat. Metab. 2:1265–83
     [Google Scholar]
  61. 61. 
    Zhang MJ, Pisco AO, Darmanis S, Zou J. 2020. Mouse aging cell atlas analysis reveals global and cell type specific aging signatures. bioRxiv 2019.12.23.887604. https://doi.org/10.1101/2019.12.23.887604
    [Crossref]
  62. 62. 
    Qiu X, Zhang Y, Yang D, Hosseinzadeh S, Wang L et al. 2019. Mapping vector field of single cells. bioRxiv 696724. https://doi.org/10.1101/696724
    [Crossref]
  63. 63. 
    Fischer DS, Fiedler AK, Kernfeld EM, Genga RMJ, Bastidas-Ponce A et al. 2019. Inferring population dynamics from single-cell RNA-sequencing time series data. Nat. Biotechnol. 37:461–68
     [Google Scholar]
  64. 64. 
    Vento-Tormo R, Efremova M, Botting RA, Turco MY, Vento-Tormo M et al. 2018. Single-cell reconstruction of the early maternal–fetal interface in humans. Nature 563:347–53
     [Google Scholar]
  65. 65. 
    Ventre E, Espinasse T, Bréhier CE, Calvez V, Lepoutre T, Gandrillon O. 2020. Reduction of a stochastic model of gene expression: Lagrangian dynamics gives access to basins of attraction as cell types and metastabilty. bioRxiv 2020.09.04.283176. https://doi.org/10.1101/2020.09.04.283176
    [Crossref]
  66. 66. 
    Wagner DE, Klein AM. 2020. Lineage tracing meets single-cell omics: opportunities and challenges. Nat. Rev. Genet. 21:410–27
     [Google Scholar]
  67. 67. 
    Schiebinger G, Shu J, Tabaka M, Cleary B, Subramanian V et al. 2019. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176:928–43.e22
     [Google Scholar]
  68. 68. 
    Brenner S. 2010. Sequences and consequences. Philos. Trans. R. Soc. B 365:207–12
     [Google Scholar]
  69. 69. 
    Noble D. 2010. Biophysics and systems biology. Philos. Trans. R. Soc. A 368:1125–39
     [Google Scholar]
  70. 70. 
    Setty M, Kiseliovas V, Levine J, Gayoso A, Mazutis L, Pe'er D 2019. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37:451–60
     [Google Scholar]
  71. 71. 
    Bakken T, Cowell L, Aevermann BD, Novotny M, Hodge R et al. 2017. Cell type discovery and representation in the era of high-content single cell phenotyping. BMC Bioinform. 18:559
     [Google Scholar]
  72. 72. 
    Wu YE, Pan L, Zuo Y, Li X, Hong W 2017. Detecting activated cell populations using single-cell RNA-seq. Neuron 96:313–29.e6
     [Google Scholar]
  73. 73. 
    Denisenko E, Guo BB, Jones M, Hou R, de Kock L et al. 2020. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 21:130
     [Google Scholar]
  74. 74. 
    Ferreira PG, Muñoz-Aguirre M, Reverter F, Sá Godinho CP, Sousa A et al. 2018. The effects of death and post-mortem cold ischemia on human tissue transcriptomes. Nat. Commun. 9:490
     [Google Scholar]
  75. 75. 
    Lambert G, Estévez-Salmeron L, Oh S, Liao D, Emerson BM et al. 2011. An analogy between the evolution of drug resistance in bacterial communities and malignant tissues. Nat. Rev. Cancer 11:375–82
     [Google Scholar]
  76. 76. 
    Waddington CH. 1966. Principles of Development and Differentiation New York: Macmillan
     [Google Scholar]
  77. 77. 
    Waddington CH. 1975. The Evolution of an Evolutionist Edinburgh: Edinburgh Univ. Press
     [Google Scholar]
  78. 78. 
    Huang S, Eichler G, Yam YB, Ingber DE. 2005. Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys. Rev. Lett. 94:12128701
     [Google Scholar]
  79. 79. 
    Levine J, Simonds E, Bendall S, Davis K, Amir EA et al. 2015. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162:184–97
     [Google Scholar]
  80. 80. 
    Agarwal D, Sandor C, Volpato V, Caffrey TM, Monzón-Sandoval J et al. 2020. A single-cell atlas of the human substantia nigra reveals cell-specific pathways associated with neurological disorders. Nat. Commun. 11:4183
     [Google Scholar]
  81. 81. 
    Maynard A, McCoach CE, Rotow JK, Harris L, Haderk F et al. 2020. Therapy-induced evolution of human lung cancer revealed by single-cell RNA sequencing. Cell 182:1232–51.e22
     [Google Scholar]
  82. 82. 
    Buenrostro JD, Corces MR, Lareau CA, Wu B, Schep AN et al. 2018. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell 173:1535–48.e16
     [Google Scholar]
  83. 83. 
    Stuart T, Satija R. 2019. Integrative single-cell analysis. Nat. Rev. Genet. 20:257–72
     [Google Scholar]
  84. 84. 
    Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK et al. 2017. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14:865–68
     [Google Scholar]
  85. 85. 
    Peterson VM, Zhang KX, Kumar N, Wong J, Li L et al. 2017. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35:936–39
     [Google Scholar]
  86. 86. 
    Qiu P. 2020. Embracing the dropouts in single-cell RNA-seq analysis. Nat. Commun. 11:1169
     [Google Scholar]
  87. 87. 
    Zhang MJ, Ntranos V, Tse D. 2020. Determining sequencing depth in a single-cell RNA-seq experiment. Nat. Commun. 11:774
     [Google Scholar]
  88. 88. 
    Eisenstein M. 2020. The secret life of cells. Nat. Methods 17:7–10
     [Google Scholar]
  89. 89. 
    Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S et al. 2020. Integrated analysis of multimodal single-cell data. bioRxiv 2020.10.12.335331. https://doi.org/10.1101/2020.10.12.335331
    [Crossref]
  90. 90. 
    Rozenblatt-Rosen O, Stubbington MJT, Regev A, Teichmann SA. 2017. The Human Cell Atlas: from vision to reality. Nature 550:451–53
     [Google Scholar]
  91. 91. 
    Zhu C, Preissl S, Ren B. 2020. Single-cell multimodal omics: the power of many. Nat. Methods 17:11–14
     [Google Scholar]
  92. 92. 
    Granja JM, Klemm S, McGinnis LM, Kathiria AS, Mezger A et al. 2019. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37:1458–65
     [Google Scholar]
  93. 93. 
    Zhao E, Stone MR, Ren X, Pulliam T, Nghiem P et al. 2020. BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution. bioRxiv 2020.09.04.283812. https://doi.org/10.1101/2020.09.04.283812
    [Crossref]
  94. 94. 
    Cao J, Cusanovich DA, Ramani V, Aghamirzaie D, Pliner HA et al. 2018. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361:1380–85
     [Google Scholar]
  95. 95. 
    Liu L, Liu C, Quintero A, Wu L, Yuan Y et al. 2019. Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity. Nat. Commun. 10:470
     [Google Scholar]
  96. 96. 
    Aevermann BD, Novotny M, Bakken T, Miller JA, Diehl AD et al. 2018. Cell type discovery using single-cell transcriptomics: implications for ontological representation. Hum. Mol. Genet. 27:R40–47
     [Google Scholar]
  97. 97. 
    Lähnemann D, Köster J, Szczurek E, McCarthy DJ, Hicks SC et al. 2020. Eleven grand challenges in single-cell data science. Genome Biol. 21:31
     [Google Scholar]
  98. 98. 
    Krzak M, Raykov Y, Boukouvalas A, Cutillo L, Angelini C. 2019. Benchmark and parameter sensitivity analysis of single-cell RNA sequencing clustering methods. Front. Genet. 10:1253
     [Google Scholar]
  99. 99. 
    Slyper M, Porter CBM, Ashenberg O, Waldman J, Drokhlyansky E et al. 2020. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat. Med. 26:792–802
     [Google Scholar]
  100. 100. 
    Davis S, Kutum R, Zappia L, Sorenson J, Kiselev V et al. 2018. awesome-single-cell. Softw. Package. https://doi.org/10.5281/zenodo.1117762
    [Crossref] [Google Scholar]
  101. 101. 
    Zappia L, Phipson B, Oshlack A. 2018. Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database. PLOS Comput. Biol. 14:e1006245
     [Google Scholar]
  102. 102. 
    Luecken MD, Theis FJ. 2019. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15:e8746
     [Google Scholar]
  103. 103. 
    Lotfollahi M, Naghipourfar M, Luecken MD, Khajavi M, Büttner M et al. 2020. Query to reference single-cell integration with transfer learning. bioRxiv 2020.07.16.205997. https://doi.org/10.1101/2020.07.16.205997
    [Crossref]
  104. 104. 
    Zhang Y, Mao S, Mukherjee S, Kannan S, Seelig G. 2020. UNCURL-App: interactive database-driven analysis of single cell RNA sequencing data. bioRxiv 2020.04.15.043737. https://doi.org/10.1101/2020.04.15.043737
    [Crossref]
  105. 105. 
    Freeman J. 2015. Open source tools for large-scale neuroscience. Curr. Opin. Neurobiol. 32:156–63
     [Google Scholar]
  106. 106. 
    Sullivan DP, Winsnes CF, Åkesson L, Hjelmare M, Wiking M et al. 2018. Deep learning is combined with massive-scale citizen science to improve large-scale image classification. Nat. Biotechnol. 36:820–28
     [Google Scholar]
  107. 107. 
    Abdelaal T, Michielsen L, Cats D, Hoogduin D, Mei H et al. 2019. A comparison of automatic cell identification methods for single-cell RNA sequencing data. Genome Biol. 20:194
     [Google Scholar]
  108. 108. 
    Zhao X, Wu S, Fang N, Sun X, Fan J 2019. Evaluation of single-cell classifiers for single-cell RNA sequencing data sets. Brief. Bioinform. 21:51581–95
     [Google Scholar]
  109. 109. 
    Ziegenhain C, Vieth B, Parekh S, Hellmann I, Enard W. 2018. Quantitative single-cell transcriptomics. Brief. Funct. Genom. 17:220–32
     [Google Scholar]
  110. 110. 
    Mereu E, Lafzi A, Moutinho C, Ziegenhain C, McCarthy DJ et al. 2020. Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. Nat. Biotechnol. 38:747–55
     [Google Scholar]
  111. 111. 
    Ding J, Adiconis X, Simmons SK, Kowalczyk MS, Hession CC et al. 2020. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat. Biotechnol. 38:737–46
     [Google Scholar]
  112. 112. 
    Franzén O, Gan LM, Björkegren JLM. 2019. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database 2019:baz046
     [Google Scholar]
  113. 113. 
    Li B, Gould J, Yang Y, Sarkizova S, Tabaka M et al. 2020. Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq. Nat. Methods 17:793–98
     [Google Scholar]
  114. 114. 
    Chintapalli VR, Wang J, Dow JAT. 2007. Using FlyAtlas to identify better Drosophila melanogaster models of human disease. Nat. Genet. 39:715–20
     [Google Scholar]
  115. 115. 
    DeTomaso D, Jones MG, Subramaniam M, Ashuach T, Ye CJ, Yosef N. 2019. Functional interpretation of single cell similarity maps. Nat. Commun. 10:4376
     [Google Scholar]
  116. 116. 
    Zappia L, Phipson B, Oshlack A. 2017. Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 18:174
     [Google Scholar]
  117. 117. 
    Dibaeinia P, Sinha S. 2020. SERGIO: a single-cell expression simulator guided by gene regulatory networks. Cell Syst. 11:3252–71.e11
     [Google Scholar]
  118. 118. 
    Weber LM, Saelens W, Cannoodt R, Soneson C, Hapfelmeier A et al. 2019. Essential guidelines for computational method benchmarking. Genome Biol. 20:125
     [Google Scholar]
  119. 119. 
    Li K, Ouyang Z, Lin D, Mingueneau M, Chen W et al. 2020. cellxgene VIP unleashes full power of interactive visualization, plotting and analysis of scRNA-seq data in the scale of millions of cells. bioRxiv 2020.08.28.270652. https://doi.org/10.1101/2020.08.28.270652
    [Crossref]
  120. 120. 
    Diehl AD, Meehan TF, Bradford YM, Brush MH, Dahdul WM et al. 2016. The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability. J. Biomed. Semant. 7:44
     [Google Scholar]
  121. 121. 
    Wang S, Pisco AO, McGeever A, Brbic M, Zitnik M et al. 2020. Unifying single-cell annotations based on the Cell Ontology. bioRxiv 810234. https://doi.org/10.1101/810234
    [Crossref]
  122. 122. 
    Srivastava D, Iyer A, Kumar V, Sengupta D. 2018. CellAtlasSearch: a scalable search engine for single cells. Nucleic Acids Res. 46:W141–47
     [Google Scholar]
  123. 123. 
    Brenner S. 2003. Nature's gift to science (Nobel lecture). ChemBioChem 4:683–87
     [Google Scholar]
  124. 124. 
    Aging Atlas Consort 2020. Aging Atlas: a multi-omics database for aging biology. Nucleic Acids Res. 49:D1D825–30
     [Google Scholar]
  125. 125. 
    Camp JG, Treutlein B. 2017. Human organomics: a fresh approach to understanding human development using single-cell transcriptomics. Development 144:1584–87
     [Google Scholar]
  126. 126. 
    Chan JM, Quintanal-Villalonga Á, Gao V, Xie Y, Allaj V et al. 2020. Single cell profiling reveals novel tumor and myeloid subpopulations in small cell lung cancer. bioRxiv 2020.12.01.406363. https://doi.org/10.1101/2020.12.01.406363
    [Crossref]
  127. 127. 
    He Z, Brazovskaja A, Ebert S, Camp JG, Treutlein B. 2020. CSS: cluster similarity spectrum integration of single-cell genomics data. Genome Biol. 21:224
     [Google Scholar]
  128. 128. 
    Klimovskaia A, Lopez-Paz D, Bottou L, Nickel M. 2020. Poincaré maps for analyzing complex hierarchies in single-cell data. Nat. Commun. 11:2966
     [Google Scholar]
  129. 129. 
    Ranjan B, Schmidt F, Sun W, Park J, Honardoost MA et al. 2020. scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data. bioRxiv 2020.04.22.056473. https://doi.org/10.1101/2020.04.22.056473
    [Crossref]
  130. 130. 
    Ding J, Regev A. 2019. Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces. bioRxiv 853457. https://doi.org/10.1101/853457
    [Crossref]
  131. 131. 
    La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H et al. 2018. RNA velocity of single cells. Nature 560:494–98
     [Google Scholar]
  132. 132. 
    Rajewsky N, Almouzni G, Gorski SA, Aerts S, Amit I et al. 2020. LifeTime and improving European healthcare through cell-based interceptive medicine. Nature 587:377–86
     [Google Scholar]
  133. 133. 
    Peiffer-Smadja N, Maatoug R, Lescure FX, D'Ortenzio E, Pineau J, King JR 2020. Machine learning for COVID-19 needs global collaboration and data-sharing. Nat. Mach. Intell. 2:293–94
     [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-092820-031008
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Single-Cell Analysis for Whole-Organism Datasets
Annual Review of Biomedical Data Science 4, 207 (2021); https://doi.org/10.1146/annurev-biodatasci-092820-031008
/content/journals/10.1146/annurev-biodatasci-092820-031008
/content/journals/10.1146/annurev-biodatasci-092820-031008
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