References

1. Watson JD, Crick FH. “Molecular structure of nucleic acids; a structure for deoxyribose nucleic acid.” Nature. 1953;171:737–8. DOI: 10.1038/171737a0

2. Wilkins MHF, Stokes AR, Wilson HR. “Molecular structure of deoxypentose nucleic acids.” Nature. 1953;171:738–40. DOI: 10.1038/171738a0

3. Franklin RE, Gosling RG. “Molecular configuration in sodium thymonucleate.” Nature. 1953;171:740–1. DOI: 10.1038/171740a0

4. Raz T, Kapranov P, Lipson D, Letovsky S, Milos PM, Thompson JF. “Protocol dependence of sequencing-based gene expression measurements.” PloS One. 2011;6:e19287. DOI: 10.1371/journal.pone.0019287

5. Sultan M, Amstislavskiy V, Risch T, Schuette M, Dökel S, Ralser M, Balzereit D, Lehrach H, Yaspo M-L. “Influence of RNA extraction methods and library selection schemes on RNA-seq data.” BMC Genomics. 2014;15:675. DOI: 10.1186/1471-2164-15-675

6. Voelker R, Small C, Bassham S, Catchen J, Sydes J, Cresko B. “RNA-seqlopedia.” Available from: https://web.archive.org/web/20181218043831/https://rnaseq.uoregon.edu/ Accessed: 2018-12-18

7. Mardis ER. “Next-generation DNA sequencing methods.” Annual Review of Genomics and Human Genetics. 2008;9:387–402. DOI: 10.1146/annurev.genom.9.081307.164359

8. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. “STAR: ultrafast universal RNA-seq aligner.” Bioinformatics. 2013;29:15–21. DOI: 10.1093/bioinformatics/bts635

9. Kim D, Langmead B, Salzberg SL. “HISAT: a fast spliced aligner with low memory requirements.” Nature Methods. 2015;12:357–60. DOI: 10.1038/nmeth.3317

10. Liao Y, Smyth GK, Shi W. “The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote.” Nucleic Acids Research. 2013;41:e108. DOI: 10.1093/nar/gkt214

11. Bray NL, Pimentel H, Melsted P, Pachter L. “Near-optimal probabilistic RNA-seq quantification.” Nature Biotechnology. 2016;34:525–7. DOI: 10.1038/nbt.3519

12. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. “Salmon provides fast and bias-aware quantification of transcript expression.” Nature Methods. 2017;14:417–9. DOI: 10.1038/nmeth.4197

13. Robinson MD, Oshlack A. “A scaling normalization method for differential expression analysis of RNA-seq data.” Genome Biology. 2010;11:R25. DOI: 10.1186/gb-2010-11-3-r25

14. Kim JK, Kolodziejczyk AA, Illicic T, Teichmann SA, Marioni JC. “Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression.” Nature Communications. 2015;6:8687. DOI: 10.1038/ncomms9687

15. Robinson MD, McCarthy DJ, Smyth GK. “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics. 2010;26:139–40. DOI: 10.1093/bioinformatics/btp616

16. McCarthy DJ, Chen Y, Smyth GK. “Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.” Nucleic Acids Research. 2012;40:4288–97. DOI: 10.1093/nar/gks042

17. Anders S, Huber W. “Differential expression analysis for sequence count data.” Genome Biology. 2010;11:R106. DOI: 10.1186/gb-2010-11-10-r106

18. Love MI, Huber W, Anders S. “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology. 2014;15:550. DOI: 10.1186/s13059-014-0550-8

19. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. “Mapping and quantifying mammalian transcriptomes by RNA-Seq.” Nature Methods. 2008;5:621–8. DOI: 10.1038/nmeth.1226

20. Wagner GP, Kin K, Lynch VJ. “Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples.” Theory in Biosciences. 2012;131:281–5. DOI: 10.1007/s12064-012-0162-3

21. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Research. 2015;43:e47. DOI: 10.1093/nar/gkv007

22. Law CW, Chen Y, Shi W, Smyth GK. “voom: Precision weights unlock linear model analysis tools for RNA-seq read counts.” Genome Biology. 2014;15:R29. DOI: 10.1186/gb-2014-15-2-r29

23. Risso D, Ngai J, Speed TP, Dudoit S. “Normalization of RNA-seq data using factor analysis of control genes or samples.” Nature Biotechnology. 2014;32:896–902. DOI: 10.1038/nbt.2931

24. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, Lao K, Surani MA. “mRNA-Seq whole-transcriptome analysis of a single cell.” Nature Methods. 2009;6:377–82. DOI: 10.1038/nmeth.1315

25. Hashimshony T, Wagner F, Sher N, Yanai I. “CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification.” Cell Reports. 2012;2:666–73. DOI: 10.1016/j.celrep.2012.08.003

26. Hashimshony T, Senderovich N, Avital G, Klochendler A, Leeuw Y de, Anavy L, Gennert D, Li S, Livak KJ, Rozenblatt-Rosen O, Dor Y, Regev A, Yanai I. “CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq.” Genome Biology. 2016;17:77. DOI: 10.1186/s13059-016-0938-8

27. Sasagawa Y, Nikaido I, Hayashi T, Danno H, Uno KD, Imai T, Ueda HR. “Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity.” Genome Biology. 2013;14:R31. DOI: 10.1186/gb-2013-14-4-r31

28. Sasagawa Y, Danno H, Takada H, Ebisawa M, Tanaka K, Hayashi T, Kurisaki A, Nikaido I. “Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads.” Genome Biology. 2018;19:29. DOI: 10.1186/s13059-018-1407-3

29. Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. “Smart-seq2 for sensitive full-length transcriptome profiling in single cells.” Nature Methods. 2013;10:1096–8. DOI: 10.1038/nmeth.2639

30. Svensson V, Vento-Tormo R, Teichmann SA. “Exponential scaling of single-cell RNA-seq in the past decade.” Nature Protocols. 2018;13:599. DOI: 10.1038/nprot.2017.149

31. 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. “Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.” Cell. 2015;161:1202–14. DOI: 10.1016/j.cell.2015.05.002

32. Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A, Li V, Peshkin L, Weitz DA, Kirschner MW. “Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.” Cell. 2015;161:1187–201. DOI: 10.1016/j.cell.2015.04.044

33. Zilionis R, Nainys J, Veres A, Savova V, Zemmour D, Klein AM, Mazutis L. “Single-cell barcoding and sequencing using droplet microfluidics.” Nature Protocols. 2017;12:44–73. DOI: 10.1038/nprot.2016.154

34. Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, Gregory MT, Shuga J, Montesclaros L, Underwood JG, Masquelier DA, Nishimura SY, Schnall-Levin M, Wyatt PW, et al. “Massively parallel digital transcriptional profiling of single cells.” Nature Communications. 2017;8:14049. DOI: 10.1038/ncomms14049

35. Zhang X, Li T, Liu F, Chen Y, Yao J, Li Z, Huang Y, Wang J. “Comparative Analysis of Droplet-Based Ultra-High-Throughput Single-Cell RNA-Seq Systems.” Molecular Cell. 2019;73:130–142.e5. DOI: 10.1016/j.molcel.2018.10.020

36. Kivioja T, Vähärautio A, Karlsson K, Bonke M, Enge M, Linnarsson S, Taipale J. “Counting absolute numbers of molecules using unique molecular identifiers.” Nature Methods. 2012;9:72–4. DOI: 10.1038/nmeth.1778

37. Islam S, Zeisel A, Joost S, La Manno G, Zajac P, Kasper M, Lönnerberg P, Linnarsson S. “Quantitative single-cell RNA-seq with unique molecular identifiers.” Nature Methods. 2014;11:163–6. DOI: 10.1038/nmeth.2772

38. Gierahn TM, Wadsworth MH 2nd, Hughes TK, Bryson BD, Butler A, Satija R, Fortune S, Love JC, Shalek AK. “Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput.” Nature Methods. 2017;14:395–8. DOI: 10.1038/nmeth.4179

39. Bose S, Wan Z, Carr A, Rizvi AH, Vieira G, Pe’er D, Sims PA. “Scalable microfluidics for single-cell RNA printing and sequencing.” Genome Biology. 2015;16:120. DOI: 10.1186/s13059-015-0684-3

40. Hu P, Fabyanic E, Kwon DY, Tang S, Zhou Z, Wu H. “Dissecting Cell-Type Composition and Activity-Dependent Transcriptional State in Mammalian Brains by Massively Parallel Single-Nucleus RNA-Seq.” Molecular Cell. 2017;68:1006–1015.e7. DOI: 10.1016/j.molcel.2017.11.017

41. Gao R, Kim C, Sei E, Foukakis T, Crosetto N, Chan L-K, Srinivasan M, Zhang H, Meric-Bernstam F, Navin N. “Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer.” Nature Communications. 2017;8:228. DOI: 10.1038/s41467-017-00244-w

42. Habib N, Avraham-Davidi I, Basu A, Burks T, Shekhar K, Hofree M, Choudhury SR, Aguet F, Gelfand E, Ardlie K, Weitz DA, Rozenblatt-Rosen O, Zhang F, Regev A. “Massively parallel single-nucleus RNA-seq with DroNc-seq.” Nature Methods. 2017;14:955–8. DOI: 10.1038/nmeth.4407

43. Wu H, Kirita Y, Donnelly EL, Humphreys BD. “Advantages of Single-Nucleus over Single-Cell RNA Sequencing of Adult Kidney: Rare Cell Types and Novel Cell States Revealed in Fibrosis.” Journal of the American Society of Nephrology: JASN. 2018;30:23–32. DOI: 10.1681/ASN.2018090912

44. Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, Satija R, Smibert P. “Simultaneous epitope and transcriptome measurement in single cells.” Nature Methods. 2017;14:865–8. DOI: 10.1038/nmeth.4380

45. Stoeckius M, Zheng S, Houck-Loomis B, Hao S, Yeung BZ, Mauck WM 3rd, Smibert P, Satija R. “Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics.” Genome Biology. 2018;19:224. DOI: 10.1186/s13059-018-1603-1

46. Moudgil A. “Multimodal scRNA-seq.” 2019. DOI: 10.5281/zenodo.2628012

47. Technology Innovation Lab, New York Genome Center. “cite-seq.com.” Available from: https://web.archive.org/web/20181218054100/https://cite-seq.com/ Accessed: 2018-12-18

48. Spanjaard B, Hu B, Mitic N, Olivares-Chauvet P, Janjuha S, Ninov N, Junker JP. “Simultaneous lineage tracing and cell-type identification using CRISPR-Cas9-induced genetic scars.” Nature Biotechnology. 2018;36:469–73. DOI: 10.1038/nbt.4124

49. Dixit A, Parnas O, Li B, Chen J, Fulco CP, Jerby-Arnon L, Marjanovic ND, Dionne D, Burks T, Raychowdhury R, Adamson B, Norman TM, Lander ES, Weissman JS, Friedman N, Regev A. “Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens.” Cell. 2016;167:1853–1866.e17. DOI: 10.1016/j.cell.2016.11.038

50. Mimitou EP, Cheng A, Montalbano A, Hao S, Stoeckius M, Legut M, Roush T, Herrera A, Papalexi E, Ouyang Z, Satija R, Sanjana NE, Koralov SB, Smibert P. “Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells.” Nature Methods. 2019;16:409–12. DOI: 10.1038/s41592-019-0392-0

51. Biddy BA, Kong W, Kamimoto K, Guo C, Waye SE, Sun T, Morris SA. “Single-cell mapping of lineage and identity in direct reprogramming.” Nature. 2018;564:219–24. DOI: 10.1038/s41586-018-0744-4

52. 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, et al. “G&T-seq: parallel sequencing of single-cell genomes and transcriptomes.” Nature Methods. 2015;12:519–22. DOI: 10.1038/nmeth.3370

53. Han KY, Kim K-T, Joung J-G, Son D-S, Kim YJ, Jo A, Jeon H-J, Moon H-S, Yoo CE, Chung W, Eum HH, Kim S, Kim HK, Lee JE, Ahn M-J, Lee H-O, Park D, Park W-Y. “SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells.” Genome Research. 2017; DOI: 10.1101/gr.223263.117

54. Wang LY, Guo J, Cao W, Zhang M, He J, Li Z. “Integrated sequencing of exome and mRNA of large-sized single cells.” Scientific Reports. 2018;8:384. DOI: 10.1038/s41598-017-18730-y

55. Hu Y, Huang K, An Q, Du G, Hu G, Xue J, Zhu X, Wang C-Y, Xue Z, Fan G. “Simultaneous profiling of transcriptome and DNA methylome from a single cell.” Genome Biology. 2016;17:88. DOI: 10.1186/s13059-016-0950-z

56. Cadwell CR, Scala F, Li S, Livrizzi G, Shen S, Sandberg R, Jiang X, Tolias AS. “Multimodal profiling of single-cell morphology, electrophysiology, and gene expression using Patch-seq.” Nature Protocols. 2017;12:2531. DOI: 10.1038/nprot.2017.120

57. Bian S, Hou Y, Zhou X, Li X, Yong J, Wang Y, Wang W, Yan J, Hu B, Guo H, Wang J, Gao S, Mao Y, Dong J, Zhu P, Xiu D, Yan L, Wen L, et al. “Single-cell multiomics sequencing and analyses of human colorectal cancer.” Science. 2018;362:1060–3. DOI: 10.1126/science.aao3791

58. Grün D, Kester L, Oudenaarden A van. “Validation of noise models for single-cell transcriptomics.” Nature Methods. 2014;11:637–40. DOI: 10.1038/nmeth.2930

59. Liu S, Trapnell C. “Single-cell transcriptome sequencing: recent advances and remaining challenges.” F1000Research. 2016;5. DOI: 10.12688/f1000research.7223.1

60. Adam M, Potter AS, Potter SS. “Psychrophilic proteases dramatically reduce single cell RNA-seq artifacts: A molecular atlas of kidney development.” Development. 2017;144:3625–32. DOI: 10.1242/dev.151142

61. Hicks SC, Townes FW, Teng M, Irizarry RA. “Missing data and technical variability in single-cell RNA-sequencing experiments.” Biostatistics. 2017; DOI: 10.1093/biostatistics/kxx053

62. Pierson E, Yau C. “ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis.” Genome Biology. 2015;16:241. DOI: 10.1186/s13059-015-0805-z

63. Risso D, Perraudeau F, Gribkova S, Dudoit S, Vert J-P. “A general and flexible method for signal extraction from single-cell RNA-seq data.” Nature Communications. 2018;9:284. DOI: 10.1038/s41467-017-02554-5

64. Van den Berge K, Perraudeau F, Soneson C, Love MI, Risso D, Vert J-P, Robinson MD, Dudoit S, Clement L. “Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications.” Genome Biology. 2018;19:24. DOI: 10.1186/s13059-018-1406-4

65. Miao Z, Deng K, Wang X, Zhang X. “DEsingle for detecting three types of differential expression in single-cell RNA-seq data.” Bioinformatics. 2018; DOI: 10.1093/bioinformatics/bty332

66. Svensson V. “Droplet scRNA-seq is not zero-inflated.” bioRxiv. 2019. p. 582064. DOI: 10.1101/582064

67. Dijk D van, Sharma R, Nainys J, Yim K, Kathail P, Carr AJ, Burdziak C, Moon KR, Chaffer CL, Pattabiraman D, Bierie B, Mazutis L, Wolf G, Krishnaswamy S, Pe’er D. “Recovering Gene Interactions from Single-Cell Data Using Data Diffusion.” Cell. 2018;174:P716–729.E27. DOI: 10.1016/j.cell.2018.05.061

68. Huang M, Wang J, Torre E, Dueck H, Shaffer S, Bonasio R, Murray JI, Raj A, Li M, Zhang NR. “SAVER: gene expression recovery for single-cell RNA sequencing.” Nature Methods. 2018;15:539–42. DOI: 10.1038/s41592-018-0033-z

69. Wang J, Agarwal D, Huang M, Hu G, Zhou Z, Conley VB, MacMullan H, Zhang NR. “Transfer learning in single-cell transcriptomics improves data denoising and pattern discovery.” bioRxiv. 2018. p. 457879. DOI: 10.1101/457879

70. Li WV, Li JJ. “An accurate and robust imputation method scImpute for single-cell RNA-seq data.” Nature Communications. 2018;9:997. DOI: 10.1038/s41467-018-03405-7

71. Andrews TS, Hemberg M. “False signals induced by single-cell imputation.” F1000Research. 2018;7. DOI: 10.12688/f1000research.16613.1

72. Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G, Juréus A, Marques S, Munguba H, He L, Betsholtz C, Rolny C, Castelo-Branco G, Hjerling-Leffler J, Linnarsson S. “Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq.” Science. 2015;347:1138–42. DOI: 10.1126/science.aaa1934

73. 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, Suvà ML, Regev A, Bernstein BE. “Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.” Science. 2014;344:1396–401. DOI: 10.1126/science.1254257

74. Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL, Espinoza FH, Desai TJ, Krasnow MA, Quake SR. “Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq.” Nature. 2014;509:371–5. DOI: 10.1038/nature13173

75. Usoskin D, Furlan A, Islam S, Abdo H, Lönnerberg P, Lou D, Hjerling-Leffler J, Haeggström J, Kharchenko O, Kharchenko PV, Linnarsson S, Ernfors P. “Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing.” Nature Neuroscience. 2015;18:145–53. DOI: 10.1038/nn.3881

76. Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A, Theis FJ, Teichmann SA, Marioni JC, Stegle O. “Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.” Nature Biotechnology. 2015;33:155–60. DOI: 10.1038/nbt.3102

77. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL. “The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.” Nature Biotechnology. 2014;32:381–6. DOI: 10.1038/nbt.2859

78. Regev A, Teichmann SA, Lander ES, Amit I, Benoist C, Birney E, Bodenmiller B, Campbell P, Carninci P, Clatworthy M, Clevers H, Deplancke B, Dunham I, Eberwine J, Eils R, Enard W, Farmer A, Fugger L, et al. “The Human Cell Atlas.” eLife. 2017;6. DOI: 10.7554/eLife.27041

79. Cao J, Packer JS, Ramani V, Cusanovich DA, Huynh C, Daza R, Qiu X, Lee C, Furlan SN, Steemers FJ, Adey A, Waterston RH, Trapnell C, Shendure J. “Comprehensive single-cell transcriptional profiling of a multicellular organism.” Science. 2017;357:661–7. DOI: 10.1126/science.aam8940

80. Tabula Muris Consortium, Overall coordination, Logistical coordination, Organ collection and processing, Library preparation and sequencing, Computational data analysis, Cell type annotation, Writing group, Supplemental text writing group, Principal investigators. “Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris.” Nature. 2018;562:367–72. DOI: 10.1038/s41586-018-0590-4

81. Wu B, Li Y, Liu Y, Jin K, Zhao K, An C, Li Q, Gong L, Zhao W, Hu J, Qian J, Ouyang H, Zou X. “Cell atlas of human uterus.” bioRxiv. 2018. p. 267849. DOI: 10.1101/267849

82. Han X, Wang R, Zhou Y, Fei L, Sun H, Lai S, Saadatpour A, Zhou Z, Chen H, Ye F, Huang D, Xu Y, Huang W, Jiang M, Jiang X, Mao J, Chen Y, Lu C, et al. “Mapping the Mouse Cell Atlas by Microwell-Seq.” Cell. 2018;172:1091–1107.e17. DOI: 10.1016/j.cell.2018.02.001

83. Saunders A, Macosko E, Wysoker A, Goldman M, Krienen F, Rivera H de, Bien E, Baum M, Wang S, Goeva A, Nemesh J, Kamitaki N, Brumbaugh S, Kulp D, McCarroll SA. “A Single-Cell Atlas of Cell Types, States, and Other Transcriptional Patterns from Nine Regions of the Adult Mouse Brain.” bioRxiv. 2018. p. 299081. DOI: 10.1101/299081

84. Plass M, Solana J, Wolf FA, Ayoub S, Misios A, Glažar P, Obermayer B, Theis FJ, Kocks C, Rajewsky N. “Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics.” Science. 2018;360. DOI: 10.1126/science.aaq1723

85. Bhaduri A, Nowakowski TJ, Pollen AA, Kriegstein AR. “Identification of cell types in a mouse brain single-cell atlas using low sampling coverage.” BMC Biology. 2018;16:113. DOI: 10.1186/s12915-018-0580-x

86. Yuan H, Yan M, Zhang G, Liu W, Deng C, Liao G, Xu L, Luo T, Yan H, Long Z, Shi A, Zhao T, Xiao Y, Li X. “CancerSEA: a cancer single-cell state atlas.” Nucleic Acids Research. 2018;47:D900–8. DOI: 10.1093/nar/gky939

87. Davie K, Janssens J, Koldere D, De Waegeneer M, Pech U, Kreft Ł, Aibar S, Makhzami S, Christiaens V, Bravo González-Blas C, Poovathingal S, Hulselmans G, Spanier KI, Moerman T, Vanspauwen B, Geurs S, Voet T, Lammertyn J, et al. “A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain.” Cell. 2018;174:982–998.e20. DOI: 10.1016/j.cell.2018.05.057

88. Taylor DM, Aronow BJ, Tan K, Bernt K, Salomonis N, Greene CS, Frolova A, Henrickson SE, Wells A, Pei L, Jaiswal JK, Whitsett J, Hamilton KE, MacParland SA, Kelsen J, Heuckeroth RO, Potter SS, Vella LA, et al. “The Pediatric Cell Atlas: Defining the Growth Phase of Human Development at Single-Cell Resolution.” Developmental Cell. 2019; DOI: 10.1016/j.devcel.2019.03.001

89. Lun ATL, McCarthy DJ, Marioni JC. “A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.” F1000Research. 2016;5:2122. DOI: 10.12688/f1000research.9501.2

90. Perraudeau F, Risso D, Street K, Purdom E, Dudoit S. “Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference.” F1000Research. 2017;6. DOI: 10.12688/f1000research.12122.1

91. Srivastava A, Malik L, Smith T, Sudbery I, Patro R. “Alevin efficiently estimates accurate gene abundances from dscRNA-seq data.” Genome Biology. 2019;20:65. DOI: 10.1186/s13059-019-1670-y

92. Smith T, Heger A, Sudbery I. “UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy.” Genome Research. 2017;27:491–9. DOI: 10.1101/gr.209601.116

93. Svensson V, Natarajan KN, Ly L-H, Miragaia RJ, Labalette C, Macaulay IC, Cvejic A, Teichmann SA. “Power analysis of single-cell RNA-sequencing experiments.” Nature Methods. 2017;14:381–7. DOI: 10.1038/nmeth.4220

94. Parekh S, Ziegenhain C, Vieth B, Enard W, Hellmann I. “zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs.” GigaScience. 2018;7. DOI: 10.1093/gigascience/giy059

95. Tian L, Su S, Dong X, Amann-Zalcenstein D, Biben C, Seidi A, Hilton DJ, Naik SH, Ritchie ME. “scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data.” PLoS Computational Biology. 2018;14:e1006361. DOI: 10.1371/journal.pcbi.1006361

96. Yang A, Troup M, Lin P, Ho JWK. “Falco: a quick and flexible single-cell RNA-seq processing framework on the cloud.” Bioinformatics. 2017;33:767–9. DOI: 10.1093/bioinformatics/btw732

97. DePasquale EAK, Schnell DJ, Valiente I, Blaxall BC, Grimes HL, Singh H, Salomonis N. “DoubletDecon: Cell-State Aware Removal of Single-Cell RNA-Seq Doublets.” bioRxiv. 2018. p. 364810. DOI: 10.1101/364810

98. McGinnis CS, Murrow LM, Gartner ZJ. “DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors.” Cell Systems. 2019;8:329–337.e4. DOI: 10.1016/j.cels.2019.03.003

99. Lun ATL, Riesenfeld S, Andrews T, Dao TP, Gomes T, participants in the 1st Human Cell Atlas Jamboree, Marioni JC. “EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data.” Genome Biology. 2019;20:63. DOI: 10.1186/s13059-019-1662-y

100. Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ, Marioni JC, Teichmann SA. “Classification of low quality cells from single-cell RNA-seq data.” Genome Biology. 2016;17:29. DOI: 10.1186/s13059-016-0888-1

101. McCarthy DJ, Campbell KR, Lun ATL, Wills QF. “Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R.” Bioinformatics. 2017;33:1179–86. DOI: 10.1093/bioinformatics/btw777

102. Leng N, Choi J, Chu L-F, Thomson JA, Kendziorski C, Stewart R. “OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data.” Bioinformatics. 2016; DOI: 10.1093/bioinformatics/btw004

103. Chen B, Herring CA, Lau KS. “pyNVR: Investigating factors affecting feature selection from scRNA-seq data for lineage reconstruction.” Bioinformatics. 2018; DOI: 10.1093/bioinformatics/bty950

104. Andrews TS, Hemberg M. “M3Drop: Dropout-based feature selection for scRNASeq.” Bioinformatics. 2018; DOI: 10.1093/bioinformatics/bty1044

105. Phipson B, Zappia L, Oshlack A. “Gene length and detection bias in single cell RNA sequencing protocols.” F1000Research. 2017;6. DOI: 10.12688/f1000research.11290.1

106. Brennecke P, Anders S, Kim JK, Kołodziejczyk AA, Zhang X, Proserpio V, Baying B, Benes V, Teichmann SA, Marioni JC, Heisler MG. “Accounting for technical noise in single-cell RNA-seq experiments.” Nature Methods. 2013;10:1093–5. DOI: 10.1038/nmeth.2645

107. Ding B, Zheng L, Zhu Y, Li N, Jia H, Ai R, Wildberg A, Wang W. “Normalization and noise reduction for single cell RNA-seq experiments.” Bioinformatics. 2015;31:2225–7. DOI: 10.1093/bioinformatics/btv122

108. Vallejos CA, Marioni JC, Richardson S. “BASiCS: Bayesian Analysis of Single-Cell Sequencing data.” PLoS Computational Biology. 2015;11:e1004333. DOI: 10.1371/journal.pcbi.1004333

109. Lun ATL, Bach K, Marioni JC. “Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.” Genome Biology. 2016;17:1–14. DOI: 10.1186/s13059-016-0947-7

110. Eling N, Richard AC, Richardson S, Marioni JC, Vallejos CA. “Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data.” Cell Systems. 2018;0. DOI: 10.1016/j.cels.2018.06.011

111. Stuart T, Satija R. “Integrative single-cell analysis.” Nature Reviews. Genetics. 2019; DOI: 10.1038/s41576-019-0093-7

112. Park J-E, Polanski K, Meyer K, Teichmann SA. “Fast Batch Alignment of Single Cell Transcriptomes Unifies Multiple Mouse Cell Atlases into an Integrated Landscape.” bioRxiv. 2018. p. 397042. DOI: 10.1101/397042

113. Gao X, Hu D, Gogol M, Li H. “ClusterMap: Compare multiple Single Cell RNA-Seq datasets across different experimental conditions.” Bioinformatics. 2019; DOI: 10.1093/bioinformatics/btz024

114. Büttner M, Miao Z, Wolf FA, Teichmann SA, Theis FJ. “A test metric for assessing single-cell RNA-seq batch correction.” Nature Methods. 2019;16:43–9. DOI: 10.1038/s41592-018-0254-1

115. Welch J, Kozareva V, Ferreira A, Vanderburg C, Martin C, Macosko E. “Integrative inference of brain cell similarities and differences from single-cell genomics.” bioRxiv. 2018. p. 459891. DOI: 10.1101/459891

116. Mereu E, Iacono G, Guillaumet-Adkins A, Moutinho C, Lunazzi G, Santos C, Miguel-Escalada I, Ferrer J, Real FX, Gut I, Heyn H. “matchSCore: Matching Single-Cell Phenotypes Across Tools and Experiments.” bioRxiv. 2018. p. 314831. DOI: 10.1101/314831

117. Hie B, Bryson B, Berger B. “Efficient integration of heterogeneous single-cell transcriptomes using Scanorama.” Nature Biotechnology. 2019; DOI: 10.1038/s41587-019-0113-3

118. Lin Y, Ghazanfar S, Wang KYX, Gagnon-Bartsch JA, Lo KK, Su X, Han Z-G, Ormerod JT, Speed TP, Yang P, Yang JYH. “scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets.” Proceedings of the National Academy of Sciences of the United States of America. 2019; DOI: 10.1073/pnas.1820006116

119. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. “Integrating single-cell transcriptomic data across different conditions, technologies, and species.” Nature Biotechnology. 2018; DOI: 10.1038/nbt.4096

120. Hotelling H. “RELATIONS BETWEEN TWO SETS OF VARIATES.” Biometrika. 1936;28:321–77. DOI: 10.1093/biomet/28.3-4.321

121. Hardoon DR, Szedmak S, Shawe-Taylor J. “Canonical correlation analysis: an overview with application to learning methods.” Neural Computation. 2004;16:2639–64. DOI: 10.1162/0899766042321814

122. Berndt DJ, Clifford J. “Using Dynamic Time Warping to Find Patterns in Time Series.” Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining. Seattle, WA; 1994. pp. 359–70. Available from: http://dl.acm.org/citation.cfm?id=3000850.3000887

123. Haghverdi L, Lun ATL, Morgan MD, Marioni JC. “Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.” Nature Biotechnology. 2018; DOI: 10.1038/nbt.4091

124. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3rd, Hao Y, Stoeckius M, Smibert P, Satija R. “Comprehensive Integration of Single-Cell Data.” Cell. 2019;177:1888–1902.e21. DOI: 10.1016/j.cell.2019.05.031

125. Kiselev VY, Andrews TS, Hemberg M. “Challenges in unsupervised clustering of single-cell RNA-seq data.” Nature Reviews. Genetics. 2019; DOI: 10.1038/s41576-018-0088-9

126. Guo M, Wang H, Potter SS, Whitsett JA, Xu Y. “SINCERA: a pipeline for single-cell RNA-seq profiling analysis.” PLoS Computational Biology. 2015;11:e1004575. DOI: 10.1371/journal.pcbi.1004575

127. Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A, Chandra T, Natarajan KN, Reik W, Barahona M, Green AR, Hemberg M. “SC3: consensus clustering of single-cell RNA-seq data.” Nature Methods. 2017;14:483–6. DOI: 10.1038/nmeth.4236

128. Anchang B, Hart TDP, Bendall SC, Qiu P, Bjornson Z, Linderman M, Nolan GP, Plevritis SK. “Visualization and cellular hierarchy inference of single-cell data using SPADE.” Nature Protocols. 2016;11:1264–79. DOI: 10.1038/nprot.2016.066

129. Satija R, Farrell JA, Gennert D, Schier AF, Regev A. “Spatial reconstruction of single-cell gene expression data.” Nature Biotechnology. 2015;33:495–502. DOI: 10.1038/nbt.3192

130. Duò A, Robinson MD, Soneson C. “A systematic performance evaluation of clustering methods for single-cell RNA-seq data.” F1000Research. 2018;7. DOI: 10.12688/f1000research.15666.1

131. Freytag S, Tian L, Lönnstedt I, Ng M, Bahlo M. “Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data.” F1000Research. 2018;7. DOI: 10.12688/f1000research.15809.1

132. Kim T, Chen IR, Lin Y, Wang AY-Y, Yang JYH, Yang P. “Impact of similarity metrics on single-cell RNA-seq data clustering.” Briefings in Bioinformatics. 2018; DOI: 10.1093/bib/bby076

133. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. “Fast unfolding of communities in large networks.” Journal of Statistical Mechanics. 2008;2008:P10008. DOI: 10.1088/1742-5468/2008/10/P10008

134. Kiselev VY, Yiu A, Hemberg M. “scmap: projection of single-cell RNA-seq data across data sets.” Nature Methods. 2018; DOI: 10.1038/nmeth.4644

135. Alquicira-Hernández J, Sathe A, Ji HP, Nguyen Q, Powell JE. “scPred: Cell type prediction at single-cell resolution.” bioRxiv. 2018. p. 369538. DOI: 10.1101/369538

136. Lieberman Y, Rokach L, Shay T. “CaSTLe - Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments.” PloS One. 2018;13:e0205499. DOI: 10.1371/journal.pone.0205499

137. Wagner F, Yanai I. “Moana: A robust and scalable cell type classification framework for single-cell RNA-Seq data.” bioRxiv. 2018. p. 456129. DOI: 10.1101/456129

138. Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA, Trapnell C. “Reversed graph embedding resolves complex single-cell trajectories.” Nature Methods. 2017;14:979–82. DOI: 10.1038/nmeth.4402

139. Ji Z, Ji H. “TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.” Nucleic Acids Research. 2016; DOI: 10.1093/nar/gkw430

140. Welch JD, Hartemink AJ, Prins JF. “SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data.” Genome Biology. 2016;17:106. DOI: 10.1186/s13059-016-0975-3

141. duVerle DA, Yotsukura S, Nomura S, Aburatani H, Tsuda K. “CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data.” BMC Bioinformatics. 2016;17:363. DOI: 10.1186/s12859-016-1175-6

142. Juliá M, Telenti A, Rausell A. “Sincell: an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq.” Bioinformatics. 2015;31:3380–2. DOI: 10.1093/bioinformatics/btv368

143. Chen J, Schlitzer A, Chakarov S, Ginhoux F, Poidinger M. “Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development.” Nature Communications. 2016;7:11988. DOI: 10.1038/ncomms11988

144. Street K, Risso D, Fletcher RB, Das D, Ngai J, Yosef N, Purdom E, Dudoit S. “Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.” BMC Genomics. 2018;19:477. DOI: 10.1186/s12864-018-4772-0

145. Cannoodt R, Saelens W, Yvan S. “Computational methods for trajectory inference from single-cell transcriptomics.” European Journal of Immunology. 2016;46:2496–506. DOI: 10.1002/eji.201646347

146. Pearson K. “On lines and planes of closest fit to systems of points in space.” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science. 1901;2:559–72. DOI: 10.1080/14786440109462720

147. Maaten L van der, Hinton G. “Visualizing Data using t-SNE.” Journal of Machine Learning Research. 2008;9:2579–605. Available from: http://www.jmlr.org/papers/v9/vandermaaten08a.html

148. McInnes L, Healy J. “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.” 2018; Available from: http://arxiv.org/abs/1802.03426

149. Saelens W, Cannoodt R, Todorov H, Saeys Y. “A comparison of single-cell trajectory inference methods.” Nature Biotechnology. 2019; DOI: 10.1038/s41587-019-0071-9

150. Svensson V, Pachter L. “RNA Velocity: Molecular Kinetics from Single-Cell RNA-Seq.” Molecular Cell. 2018;72:7–9. DOI: 10.1016/j.molcel.2018.09.026

151. La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V, Lidschreiber K, Kastriti ME, Lönnerberg P, Furlan A, Fan J, Borm LE, Liu Z, Bruggen D van, Guo J, He X, Barker R, Sundström E, et al. “RNA velocity of single cells.” Nature. 2018;560:494–8. DOI: 10.1038/s41586-018-0414-6

152. Delmans M, Hemberg M. “Discrete distributional differential expression (D3E)–a tool for gene expression analysis of single-cell RNA-seq data.” BMC Bioinformatics. 2016;17:110. DOI: 10.1186/s12859-016-0944-6

153. Finak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, Slichter CK, Miller HW, McElrath MJ, Prlic M, Linsley PS, Gottardo R. “MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.” Genome Biology. 2015;16:278. DOI: 10.1186/s13059-015-0844-5

154. Korthauer KD, Chu L-F, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. “A statistical approach for identifying differential distributions in single-cell RNA-seq experiments.” Genome Biology. 2016;17:222. DOI: 10.1186/s13059-016-1077-y

155. Kharchenko PV, Silberstein L, Scadden DT. “Bayesian approach to single-cell differential expression analysis.” Nature Methods. 2014;11:740–2. DOI: 10.1038/nmeth.2967

156. Soneson C, Robinson MD. “Bias, robustness and scalability in single-cell differential expression analysis.” Nature Methods. 2018;15:255–61. DOI: 10.1038/nmeth.4612

157. Qiu X, Hill A, Packer J, Lin D, Ma Y-A, Trapnell C. “Single-cell mRNA quantification and differential analysis with Census.” Nature Methods. 2017;14:309–15. DOI: 10.1038/nmeth.4150

158. Zhang JM, Kamath GM, Tse DN. “Valid post-clustering differential analysis for single-cell RNA-Seq.” bioRxiv. 2019. p. 463265. DOI: 10.1101/463265

159. Edsgärd D, Reinius B, Sandberg R. “scphaser: haplotype inference using single-cell RNA-seq data.” Bioinformatics. 2016; DOI: 10.1093/bioinformatics/btw484

160. Reinius B, Mold JE, Ramsköld D, Deng Q, Johnsson P, Michaëlsson J, Frisén J, Sandberg R. “Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA-seq.” Nature Genetics. 2016;48:1430–5. DOI: 10.1038/ng.3678

161. Jiang Y, Zhang NR, Li M. “SCALE: modeling allele-specific gene expression by single-cell RNA sequencing.” Genome Biology. 2017;18:74. DOI: 10.1186/s13059-017-1200-8

162. Choi K, Raghupathy N, Churchill GA. “scBASE: A Bayesian mixture model for the analysis of allelic expression in single cells.” bioRxiv. 2019. p. 383224. DOI: 10.1101/383224

163. Welch JD, Hu Y, Prins JF. “Robust detection of alternative splicing in a population of single cells.” Nucleic Acids Research. 2016;44:e73. DOI: 10.1093/nar/gkv1525

164. Huang Y, Sanguinetti G. “BRIE: transcriptome-wide splicing quantification in single cells.” Genome Biology. 2017;18:123. DOI: 10.1186/s13059-017-1248-5

165. Song Y, Botvinnik OB, Lovci MT, Kakaradov B, Liu P, Xu JL, Yeo GW. “Single-cell alternative splicing analysis with Expedition reveals splicing dynamics during neuron differentiation.” Molecular Cell. 2017;67:148–161.e5. DOI: 10.1016/j.molcel.2017.06.003

166. Poirion O, Zhu X, Ching T, Garmire LX. “Using single nucleotide variations in single-cell RNA-seq to identify subpopulations and genotype-phenotype linkage.” Nature Communications. 2018;9:4892. DOI: 10.1038/s41467-018-07170-5

167. Ding J, Lin C, Bar-Joseph Z. “Cell lineage inference from SNP and scRNA-Seq data.” Nucleic Acids Research. 2019; DOI: 10.1093/nar/gkz146

168. Petti AA, Williams SR, Miller CA, Fiddes IT, Srivatsan SN, Chen DY, Fronick CC, Fulton RS, Church DM, Ley TJ. “Mutation detection in thousands of acute myeloid leukemia cells using single cell RNA-sequencing.” bioRxiv. 2018. p. 434746. DOI: 10.1101/434746

169. Lindeman I, Emerton G, Mamanova L, Snir O, Polanski K, Qiao S-W, Sollid LM, Teichmann SA, Stubbington MJT. “BraCeR: B-cell-receptor reconstruction and clonality inference from single-cell RNA-seq.” Nature Methods. 2018;15:563–5. DOI: 10.1038/s41592-018-0082-3

170. Afik S, Raulet G, Yosef N. “Reconstructing B cell receptor sequences from short-read single cell RNA-sequencir with BRAPeS.” Bioinformatics. bioRxiv; 2018. p. e148.

171. Rizzetto S, Koppstein DNP, Samir J, Singh M, Reed JH, Cai CH, Lloyd AR, Eltahla AA, Goodnow CC, Luciani F. “B-cell receptor reconstruction from single-cell RNA-seq with VDJPuzzle.” Bioinformatics. 2018;34:2846–7. DOI: 10.1093/bioinformatics/bty203

172. Stubbington MJT, Lönnberg T, Proserpio V, Clare S, Speak AO, Dougan G, Teichmann SA. “T cell fate and clonality inference from single-cell transcriptomes.” Nature Methods. 2016;13:329–32. DOI: 10.1038/nmeth.3800

173. Afik S, Yates KB, Bi K, Darko S, Godec J, Gerdemann U, Swadling L, Douek DC, Klenerman P, Barnes EJ, Sharpe AH, Haining WN, Yosef N. “Targeted reconstruction of T cell receptor sequence from single cell RNA-seq links CDR3 length to T cell differentiation state.” Nucleic Acids Research. 2017;45:e148. DOI: 10.1093/nar/gkx615

174. Fan J, Lee H-O, Lee S, Ryu D-E, Lee S, Xue C, Kim SJ, Kim K, Barkas N, Park PJ, Park W-Y, Kharchenko PV. “Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data.” Genome Research. 2018;28:1217–27. DOI: 10.1101/gr.228080.117

175. Tian L, Dong X, Freytag S, Lê Cao K-A, Su S, JalalAbadi A, Amann-Zalcenstein D, Weber TS, Seidi A, Jabbari JS, Naik SH, Ritchie ME. “Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments.” Nature Methods. 2019; DOI: 10.1038/s41592-019-0425-8

176. Bertram JF, Douglas-Denton RN, Diouf B, Hughson MD, Hoy WE. “Human nephron number: implications for health and disease.” Pediatric Nephrology. 2011;26:1529–33. DOI: 10.1007/s00467-011-1843-8

177. Australian Bureau of Statistics. “National Health Survey: First Results, 2017-18.” Commonwealth of Australia; Commonwealth of Australia; 2018 Dec. Available from: http://www.abs.gov.au/ausstats/abs@.nsf/Lookup/by%20Subject/4364.0.55.001~2017-18~Main%20Features~Kidney%20disease~65

178. Australian Bureau of Statistics. “Causes of Death, Australia, 2017.” Commonwealth of Australia; Commonwealth of Australia; 2018 Sep. Available from: http://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/3303.0Main+Features12017?OpenDocument

179. Patel SR, Dressler GR. “The genetics and epigenetics of kidney development.” Seminars in Nephrology. 2013;33:314–26. DOI: 10.1016/j.semnephrol.2013.05.004

180. Little MH. “Improving our resolution of kidney morphogenesis across time and space.” Current Opinion in Genetics & Development. 2015;32:135–43. DOI: 10.1016/j.gde.2015.03.001

181. Lindström NO, McMahon JA, Guo J, Tran T, Guo Q, Rutledge E, Parvez RK, Saribekyan G, Schuler RE, Liao C, Kim AD, Abdelhalim A, Ruffins SW, Thornton ME, Baskin L, Grubbs B, Kesselman C, McMahon AP. “Conserved and Divergent Features of Human and Mouse Kidney Organogenesis.” Journal of the American Society of Nephrology: JASN. 2018;29:785–805. DOI: 10.1681/ASN.2017080887

182. Tian P, Lennon R. “The myriad possibility of kidney organoids.” Current Opinion in Nephrology and Hypertension. 2019;28:211–8. DOI: 10.1097/MNH.0000000000000498

183. Takahashi K, Yamanaka S. “Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors.” Cell. 2006;126:663–76. DOI: 10.1016/j.cell.2006.07.024

184. Nakagawa M, Koyanagi M, Tanabe K, Takahashi K, Ichisaka T, Aoi T, Okita K, Mochiduki Y, Takizawa N, Yamanaka S. “Generation of induced pluripotent stem cells without Myc from mouse and human fibroblasts.” Nature Biotechnology. 2008;26:101–6. DOI: 10.1038/nbt1374

185. Fatehullah A, Tan SH, Barker N. “Organoids as an in vitro model of human development and disease.” Nature Cell Biology. 2016;18:246–54. DOI: 10.1038/ncb3312

186. Takasato M, Er PX, Chiu HS, Maier B, Baillie GJ, Ferguson C, Parton RG, Wolvetang EJ, Roost MS, Chuva de Sousa Lopes SM, Little MH. “Kidney organoids from human iPS cells contain multiple lineages and model human nephrogenesis.” Nature. 2015;526:564–8. DOI: 10.1038/nature15695

187. Phipson B, Er PX, Combes AN, Forbes TA, Howden SE, Zappia L, Yen H-J, Lawlor KT, Hale LJ, Sun J, Wolvetang E, Takasato M, Oshlack A, Little MH. “Evaluation of variability in human kidney organoids.” Nature Methods. 2019;16:79–87. DOI: 10.1038/s41592-018-0253-2

188. Little MH, Hale LJ, Howden SE, Kumar SV. “Generating Kidney from Stem Cells.” Annual Review of Physiology. 2019;81:335–57. DOI: 10.1146/annurev-physiol-020518-114331

189. Bartfeld S, Clevers H. “Stem cell-derived organoids and their application for medical research and patient treatment.” Journal of Molecular Medicine. 2017;95:729–38. DOI: 10.1007/s00109-017-1531-7

190. Kumar SV, Er PX, Lawlor KT, Motazedian A, Scurr M, Ghobrial I, Combes AN, Zappia L, Oshlack A, Stanley EG, Little MH. “Kidney micro-organoids in suspension culture as a scalable source of human pluripotent stem cell-derived kidney cells.” Development. 2019;146. DOI: 10.1242/dev.172361

191. Wilson PC, Humphreys BD. “Kidney and organoid single-cell transcriptomics: the end of the beginning.” Pediatric Nephrology. 2019;1–7. DOI: 10.1007/s00467-018-4177-y

192. Brazovskaja A, Treutlein B, Camp JG. “High-throughput single-cell transcriptomics on organoids.” Current Opinion in Biotechnology. 2018;55:167–71. DOI: 10.1016/j.copbio.2018.11.002

193. Park J, Shrestha R, Qiu C, Kondo A, Huang S, Werth M, Li M, Barasch J, Suszták K. “Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease.” Science. 2018;360:758–63. DOI: 10.1126/science.aar2131

194. Young MD, Mitchell TJ, Vieira Braga FA, Tran MGB, Stewart BJ, Ferdinand JR, Collord G, Botting RA, Popescu D-M, Loudon KW, Vento-Tormo R, Stephenson E, Cagan A, Farndon SJ, Del Castillo Velasco-Herrera M, Guzzo C, Richoz N, Mamanova L, et al. “Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors.” Science. 2018;361:594–9. DOI: 10.1126/science.aat1699

195. Lindström NO, Guo J, Kim AD, Tran T, Guo Q, De Sena Brandine G, Ransick A, Parvez RK, Thornton ME, Basking L, Grubbs B, McMahon JA, Smith AD, McMahon AP. “Conserved and Divergent Features of Mesenchymal Progenitor Cell Types within the Cortical Nephrogenic Niche of the Human and Mouse Kidney.” Journal of the American Society of Nephrology: JASN. 2018;29:806–24. DOI: 10.1681/ASN.2017080890

196. Wu H, Uchimura K, Donnelly EL, Kirita Y, Morris SA, Humphreys BD. “Comparative Analysis and Refinement of Human PSC-Derived Kidney Organoid Differentiation with Single-Cell Transcriptomics.” Cell Stem Cell. 2018;23:869–881.e8. DOI: 10.1016/j.stem.2018.10.010

197. Davis S, Kutum R, Zappia L, Sorenson J, Kiselev V, Olivier P, Botvinnik O, Korthauer K, Gitter A, Campbell KR, Hickey P, Tuncel MA, MikeDMorgan, markrobinsonuzh, Vallejos C, Wang Z, Salomonis N, dyl4nm4rsh4ll, et al. “Awesome Single Cell.” DOI: 10.5281/zenodo.1294021

198. Zappia L, Phipson B, Oshlack A. “Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database.” PLoS Computational Biology. 2018;14:e1006245. DOI: 10.1371/journal.pcbi.1006245

199. Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, et al. “Orchestrating high-throughput genomic analysis with Bioconductor.” Nature Methods. 2015;12:115–21. DOI: 10.1038/nmeth.3252

200. Amezquita RA, Carey VJ, Carpp LN, Geistlinger L, Lun ATL, Marini F, Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pagès H, Smith M, Huber W, Morgan M, Gottardo R, Hicks SC. “Orchestrating Single-Cell Analysis with Bioconductor.” bioRxiv. 2019. p. 590562. DOI: 10.1101/590562

201. Zappia L, Phipson B, Oshlack A. “Splatter: simulation of single-cell RNA sequencing data.” Genome Biology. 2017;18:174. DOI: 10.1186/s13059-017-1305-0

202. Lun A, Risso D. “SingleCellExperiment: S4 Classes for Single Cell Data.” 2017. Available from: http://bioconductor.org/packages/SingleCellExperiment/

203. Campbell KR, Yau C. “Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers.” Wellcome Open Research. 2017;2:19. DOI: 10.12688/wellcomeopenres.11087.1

204. Campbell KR, Yau C. “Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data.” Nature Communications. 2018;9:2442. DOI: 10.1038/s41467-018-04696-6

205. Barron M, Zhang S, Li J. “A sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data.” Nucleic Acids Research. 2017;46:e14. DOI: 10.1093/nar/gkx1113

206. Tung P-Y, Blischak JD, Hsiao CJ, Knowles DA, Burnett JE, Pritchard JK, Gilad Y. “Batch effects and the effective design of single-cell gene expression studies.” Scientific Reports. 2017;7:39921. DOI: 10.1038/srep39921

207. Soneson C, Robinson MD. “Towards unified quality verification of synthetic count data with countsimQC.” Bioinformatics. 2017; DOI: 10.1093/bioinformatics/btx631

208. Zappia L, Oshlack A. “Clustering trees: a visualization for evaluating clusterings at multiple resolutions.” GigaScience. 2018;7. DOI: 10.1093/gigascience/giy083

209. Pedersen TL. “tidygraph: A Tidy API for Graph Manipulation.” 2018. Available from: https://CRAN.R-project.org/package=tidygraph

210. Csardi G, Nepusz T. “The igraph software package for complex network research.” InterJournal, Complex Systems. 2006;1695:1–9.

211. Pedersen TL. “ggraph: An Implementation of Grammar of Graphics for Graphs and Networks.” 2018. Available from: https://CRAN.R-project.org/package=ggraph

212. Anderson E. “The Irises of the Gaspe Peninsula.” Bulletin of the American Iris Society. 1935;59:2–5.

213. Fisher RA. “The use of multiple measurements in taxonomic problems.” Annals of Eugenics. 1936;7:179–88. DOI: 10.1111/j.1469-1809.1936.tb02137.x

214. Combes AN, Zappia L, Er PX, Oshlack A, Little MH. “Single-cell analysis reveals congruence between kidney organoids and human fetal kidney.” Genome Medicine. 2019;11:3. DOI: 10.1186/s13073-019-0615-0

215. Lawlor KT, Zappia L, Lefevre J, Park J-S, Hamilton NA, Oshlack A, Little MH, Combes AN. “Nephron progenitor commitment is a stochastic process influenced by cell migration.” eLife. 2019;8:e41156. DOI: 10.7554/eLife.41156

216. Zappia L, Combes AN, Er PX, Oshlack A, Little MH. “Combes organoid paper analysis code.” 2019. DOI: 10.5281/zenodo.2548990

217. Zappia L. “PhD thesis analysis.” 2019. DOI: 10.5281/zenodo.2622384

218. Lex A, Gehlenborg N, Strobelt H, Vuillemot R, Pfister H. “UpSet: Visualization of Intersecting Sets.” IEEE Transactions on Visualization and Computer Graphics. 2014;20:1983–92. DOI: 10.1109/TVCG.2014.2346248

219. Levenshtein VI. “Binary codes capable of correcting deletions, insertions, and reversals.” Soviet physics doklady. 1966. pp. 707–10.

220. Smedley D, Haider S, Durinck S, Pandini L, Provero P, Allen J, Arnaiz O, Awedh MH, Baldock R, Barbiera G, Bardou P, Beck T, Blake A, Bonierbale M, Brookes AJ, Bucci G, Buetti I, Burge S, et al. “The BioMart community portal: an innovative alternative to large, centralized data repositories.” Nucleic Acids Research. 2015;43:W589–98. DOI: 10.1093/nar/gkv350

221. Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, Huber W. “BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis.” Bioinformatics. 2005;21:3439–40. DOI: 10.1093/bioinformatics/bti525

222. Scialdone A, Natarajan KN, Saraiva LR, Proserpio V, Teichmann SA, Stegle O, Marioni JC, Buettner F. “Computational assignment of cell-cycle stage from single-cell transcriptome data.” Methods. 2015;85:54–61. DOI: 10.1016/j.ymeth.2015.06.021

223. Becht E, McInnes L, Healy J, Dutertre C-A, Kwok IWH, Ng LG, Ginhoux F, Newell EW. “Dimensionality reduction for visualizing single-cell data using UMAP.” Nature Biotechnology. 2018; DOI: 10.1038/nbt.4314

224. Sidiropoulos N, Sohi SH, Pedersen TL, Porse BT, Winther O, Rapin N, Bagger FO. “SinaPlot: An Enhanced Chart for Simple and Truthful Representation of Single Observations Over Multiple Classes.” Journal of Computational and Graphical Statistics: A Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America. 2018;27:673–6. DOI: 10.1080/10618600.2017.1366914

225. Jaakkola MK, Seyednasrollah F, Mehmood A, Elo LL. “Comparison of methods to detect differentially expressed genes between single-cell populations.” Briefings in Bioinformatics. 2016; DOI: 10.1093/bib/bbw057

226. Wolf FA, Hamey FK, Plass M, Solana J, Dahlin JS, Göttgens B, Rajewsky N, Simon L, Theis FJ. “PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.” Genome Biology. 2019;20:59. DOI: 10.1186/s13059-019-1663-x

227. Wolf FA, Angerer P, Theis FJ. “SCANPY: large-scale single-cell gene expression data analysis.” Genome Biology. 2018;19:15. DOI: 10.1186/s13059-017-1382-0

228. R Core Team. “R: A language and environment for statistical computing.” Vienna, Austria: R Foundation for Statistical Computing; 2018.

229. Lun A, Risso D. “SingleCellExperiment: S4 classes for single cell data.” 2019. R package version 1.4.1

230. Jacomy M, Venturini T, Heymann S, Bastian M. “ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software.” PloS One. 2014;9:e98679. DOI: 10.1371/journal.pone.0098679

231. Wickham H. “ggplot2: Elegant Graphics for Data Analysis.” Springer New York; 2010. ISBN: 9780387981406

232. Wilke CO. “cowplot: Streamlined plot theme and plot annotations for ’ggplot2’.” 2018. R package version 0.9.3

233. Gehlenborg N. “UpSetR: A more scalable alternative to Venn and Euler diagrams for visualizing intersecting sets.” 2017. R package version 1.3.3

234. Auguie B. “GridExtra: Miscellaneous functions for "grid" graphics.” 2017. R package version 2.3

235. Pedersen TL. “ggforce: Accelerating ’ggplot2’.” 2018. R package version 0.1.3

236. Wickham H. “tidyverse: Easily install and load the ’tidyverse’.” 2017. R package version 1.2.1

237. Wickham H, François R, Henry L, Müller K. “dplyr: A grammar of data manipulation.” 2018. R package version 0.7.8

238. Wickham H, Henry L. “tidyr: Easily tidy data with ’spread()’ and ’gather()’ functions.” 2018. R package version 0.8.2

239. Henry L, Wickham H. “purrr: Functional programming tools.” 2018. R package version 0.2.5

240. Morgan M, Van Twisk D. “LoomExperiment: LoomExperiment container.” 2019. R package version 1.0.2

241. Blischak J, Carbonetto P, Stephens M. “workflowr: A framework for reproducible and collaborative data science.” 2018. R package version 1.1.1

242. Xie Y. “knitr: A Comprehensive Tool for Reproducible Research in R.” In: Stodden V, Leisch F, Peng RD, editors. Implementing Reproducible Research. CRC Press; 2014. ISBN: 9781466561595

243. Xie Y. “Dynamic Documents with R and knitr.” CRC Press; 2016. DOI: 10.1201/b15166 ISBN: 9781482203547

244. Xie Y. “knitr: A general-purpose package for dynamic report generation in R.” 2018. R package version 1.20

245. Xie Y, Allaire JJ, Grolemund G. “R Markdown: The Definitive Guide.” CRC Press LLC; 2018. ISBN: 9781138359338

246. Allaire J, Xie Y, McPherson J, Luraschi J, Ushey K, Atkins A, Wickham H, Cheng J, Chang W. “rmarkdown: Dynamic documents for R.” 2018. R package version 1.10

247. Xie Y. “bookdown: Authoring books and technical documents with R Markdown.” 2018. R package version 0.7

248. Xie Y. “Bookdown: Authoring Books and Technical Documents with R Markdown.” CRC Press; 2016. ISBN: 9781138700109

249. Zappia L, Oshlack A. “clustree: Visualise clusterings at different resolutions.” 2019. R package version 0.3.0

250. Wickham H. “forcats: Tools for working with categorical variables (factors).” 2018. R package version 0.3.0

251. Wickham H, Chang W, Henry L, Pedersen TL, Takahashi K, Wilke C, Woo K. “ggplot2: Create elegant data visualisations using the grammar of graphics.” 2018. R package version 3.1.0

252. Hester J. “glue: Interpreted string literals.” 2018. R package version 1.3.0

253. Edmondson M. “googleAnalyticsR: Google Analytics API into R.” 2018. R package version 0.6.0

254. Edmondson M. “googleAuthR: Authenticate and create Google APIs.” 2018. R package version 0.7.0

255. Müller K. “here: A simpler way to find your files.” 2017. R package version 0.1

256. Ooms J, Temple Lang D, Hilaiel L. “jsonlite: A robust, high performance JSON parser and generator for R.” 2018. R package version 1.6

257. Ooms J. “The jsonlite Package: A Practical and Consistent Mapping Between JSON Data and R Objects.” 2014; Available from: http://arxiv.org/abs/1403.2805

258. Spinu V, Grolemund G, Wickham H. “lubridate: Make dealing with dates a little easier.” 2018. R package version 1.7.4

259. Grolemund G, Wickham H. “Dates and Times Made Easy with lubridate.” Journal of Statistical Software, Articles. 2011;40:1–25. DOI: 10.18637/jss.v040.i03

260. Bache SM, Wickham H. “magrittr: A forward-pipe operator for R.” 2014. R package version 1.5

261. Ushey K, McPherson J, Cheng J, Atkins A, Allaire J. “packrat: A dependency management system for projects and their R package dependencies.” 2018. R package version 0.4.9-3

262. Neuwirth E. “RColorBrewer: ColorBrewer palettes.” 2014. R package version 1.1-2

263. Wickham H, Hester J, Francois R. “readr: Read rectangular text data.” 2018. R package version 1.3.1

264. Csárdi G, Wickham H, Chang W, Hester J, Morgan M, Tenenbaum D. “remotes: R package installation from remote repositories, including ’GitHub’.” 2018. R package version 2.0.0

265. Wickham H. “stringr: Simple, consistent wrappers for common string operations.” 2018. R package version 1.3.1

266. Garnier S. “viridis: Default color maps from ’matplotlib’.” 2018. R package version 0.5.1