REFERENCES
1. Golding I, Paulsson J, Zawilski SM, Cox EC. Real-time kinetics of gene activity in individual bacteria. Cell 2005;123:1025-36.
2. Cai L, Friedman N, Xie XS. Stochastic protein expression in individual cells at the single molecule level. Nature 2006;440:358-62.
3. Wang D, Bodovitz S. Single cell analysis: the new frontier in “omics”. Trends Biotechnol 2010;28:281-90.
4. Kalisky T, Blainey P, Quake SR. Genomic analysis at the single-cell level. Annu Rev Genet 2011;45:431-45.
5. Swanton C. Intratumor heterogeneity: evolution through space and time. Cancer Res 2012;72:4875-82.
6. Altschuler SJ, Wu LF. Cellular heterogeneity: do differences make a difference? Cell 2010;141:559-63.
7. Meacham CE, Morrison SJ. Tumour heterogeneity and cancer cell plasticity. Nature 2013;501:328-37.
8. Graf T, Stadtfeld M. Heterogeneity of embryonic and adult stem cells. Cell Stem Cell 2008;3:480-3.
9. Bengtsson M, Ståhlberg A, Rorsman P, Kubista M. Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Res 2005;15:1388-92.
10. Raj A, Peskin CS, Tranchina D, Vargas DY, Tyagi S. Stochastic mRNA synthesis in mammalian cells. PLoS Biol 2006;4:e309.
11. Waks Z, Silver PA. Nuclear origins of cell-to-cell variability. Cold Spring Harb Symp Quant Biol 2010;75:87-94.
12. Buenrostro JD, Wu B, Litzenburger UM, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 2015;523:486-90.
13. Shalek AK, Satija R, Adiconis X, et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 2013;498:236-40.
14. Patel AP, Tirosh I, Trombetta JJ, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 2014;344:1396-401.
15. Gawad C, Koh W, Quake SR. Single-cell genome sequencing: current state of the science. Nat Rev Genet 2016;17:175-88.
16. Irish JM, Kotecha N, Nolan GP. Mapping normal and cancer cell signalling networks: towards single-cell proteomics. Nat Rev Cancer 2006;6:146-55.
17. Tang F, Barbacioru C, Bao S, et al. Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis. Cell Stem Cell 2010;6:468-78.
18. Islam S, Kjällquist U, Moliner A, et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res 2011;21:1160-7.
19. Saliba AE, Westermann AJ, Gorski SA, Vogel J. Single-cell RNA-seq: advances and future challenges. Nucleic Acids Res 2014;42:8845-60.
20. Choi PJ, Xie XS, Shakhnovich EI. Stochastic switching in gene networks can occur by a single-molecule event or many molecular steps. J Mol Biol 2010;396:230-44.
21. Gupta PB, Fillmore CM, Jiang G, et al. Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell 2011;146:633-44.
22. Nagano T, Lubling Y, Stevens TJ, et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 2013;502:59-64.
23. Bahar R, Hartmann CH, Rodriguez KA, et al. Increased cell-to-cell variation in gene expression in ageing mouse heart. Nature 2006;441:1011-4.
24. Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA. The technology and biology of single-cell RNA sequencing. Mol Cell 2015;58:610-20.
25. Haque A, Engel J, Teichmann SA, Lönnberg T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med 2017;9:75.
26. Chen CL, Mahalingam D, Osmulski P, et al. Single-cell analysis of circulating tumor cells identifies cumulative expression patterns of EMT-related genes in metastatic prostate cancer. Prostate 2013;73:813-26.
27. Hu P, Zhang W, Xin H, Deng G. Single cell isolation and analysis. Front Cell Dev Biol 2016;4:116.
28. Hwang B, Lee JH, Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med 2018;50:96.
29. Setty M, Tadmor MD, Reich-Zeliger S, et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat Biotechnol 2016;34:637-45.
30. Bendall SC, Nolan GP, Roederer M, Chattopadhyay PK. A deep profiler’s guide to cytometry. Trends Immunol 2012;33:323-32.
31. Reece A, Xia B, Jiang Z, Noren B, McBride R, Oakey J. Microfluidic techniques for high throughput single cell analysis. Curr Opin Biotechnol 2016;40:90-6.
32. Bai Y, Gao M, Wen L, et al. Applications of microfluidics in quantitative biology. Biotechnol J 2018;13:e1700170.
33. Macosko EZ, Basu A, Satija R, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 2015;161:1202-14.
34. Klein AM, Mazutis L, Akartuna I, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 2015;161:1187-201.
35. Chen H, Sun J, Wolvetang E, Cooper-White J. High-throughput, deterministic single cell trapping and long-term clonal cell culture in microfluidic devices. Lab Chip 2015;15:1072-83.
36. Espina V, Milia J, Wu G, Cowherd S, Liotta LA. Laser capture microdissection. In: Taatjes DJ, Mossman BT, editors. Cell imaging techniques. Totowa: Humana Press; 2006. pp. 213-29.
37. Espina V, Wulfkuhle JD, Calvert VS, et al. Laser-capture microdissection. Nat Protoc 2006;1:586-603.
38. Civita P, Franceschi S, Aretini P, et al. Laser capture microdissection and RNA-Seq analysis: high sensitivity approaches to explain histopathological heterogeneity in human glioblastoma FFPE archived tissues. Front Oncol 2019;9:482.
39. Datta S, Malhotra L, Dickerson R, Chaffee S, Sen CK, Roy S. Laser capture microdissection: Big data from small samples. Histol Histopathol 2015;30:1255-69.
40. Cornelison DD, Wold BJ. Single-cell analysis of regulatory gene expression in quiescent and activated mouse skeletal muscle satellite cells. Dev Biol 1997;191:270-83.
41. Kamme F, Salunga R, Yu J, et al. Single-cell microarray analysis in hippocampus CA1: demonstration and validation of cellular heterogeneity. J Neurosci 2003;23:3607-15.
42. Tang F, Barbacioru C, Nordman E, et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat Protoc 2010;5:516-35.
43. Tang F, Barbacioru C, Wang Y, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 2009;6:377-82.
44. Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol 2019;15:e8746.
45. Kurimoto K, Yabuta Y, Ohinata Y, Saitou M. Global single-cell cDNA amplification to provide a template for representative high-density oligonucleotide microarray analysis. Nat Protoc 2007;2:739-52.
46. Kurimoto K, Yabuta Y, Ohinata Y, et al. An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res 2006;34:e42.
47. Zhu YY, Machleder EM, Chenchik A, Li R, Siebert PD. Reverse transcriptase template switching: a SMART approach for full-length cDNA library construction. Biotechniques 2001;30:892-7.
48. Islam S, Zeisel A, Joost S, et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat Methods 2014;11:163-6.
49. Ramsköld D, Luo S, Wang YC, et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat Biotechnol 2012;30:777-82.
50. Picelli S, Faridani OR, Björklund AK, Winberg G, Sagasser S, Sandberg R. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc 2014;9:171-81.
51. Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg G, Sandberg R. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods 2013;10:1096-8.
52. Fu GK, Hu J, Wang PH, Fodor SP. Counting individual DNA molecules by the stochastic attachment of diverse labels. Proc Natl Acad Sci U S A 2011;108:9026-31.
53. Kivioja T, Vähärautio A, Karlsson K, et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods 2011;9:72-4.
54. Hashimshony T, Wagner F, Sher N, Yanai I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep 2012;2:666-73.
55. Hashimshony T, Senderovich N, Avital G, et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol 2016;17:77.
56. Soumillon M, Cacchiarelli D, Semrau S, van Oudenaarden A, Mikkelsen TS. Characterization of directed differentiation by high-throughput single-cell RNA-Seq. BioRxiv 2014;003236.
57. Sheng K, Cao W, Niu Y, Deng Q, Zong C. Effective detection of variation in single-cell transcriptomes using MATQ-seq. Nat Methods 2017;14:267-70.
58. Hayashi T, Ozaki H, Sasagawa Y, Umeda M, Danno H, Nikaido I. Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs. Nat Commun 2018;9:619.
59. Ozsolak F, Goren A, Gymrek M, et al. Digital transcriptome profiling from attomole-level RNA samples. Genome Res 2010;20:519-25.
60. Sasagawa Y, Nikaido I, Hayashi T, et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol 2013;14:R31.
61. Jaitin DA, Kenigsberg E, Keren-Shaul H, et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 2014;343:776-9.
62. Wölfel R, Corman VM, Guggemos W, et al. Virological assessment of hospitalized patients with COVID-2019. Nature 2020;581:465-9.
63. Zheng GX, Terry JM, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells. Nat Commun 2017;8:14049.
64. Gierahn TM, Wadsworth MH 2nd, Hughes TK, et al. Seq-well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods 2017;14:395-8.
65. Fan HC, Fu GK, Fodor SP. Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 2015;347:1258367.
66. Habib N, Avraham-Davidi I, Basu A, et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods 2017;14:955-8.
67. Cao J, Packer JS, Ramani V, et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 2017;357:661-7.
68. Rosenberg AB, Roco CM, Muscat RA, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 2018;360:176-82.
69. Bhargava V, Ko P, Willems E, Mercola M, Subramaniam S. Quantitative transcriptomics using designed primer-based amplification. Sci Rep 2013;3:1740.
70. Nakamura T, Yabuta Y, Okamoto I, et al. SC3-seq: a method for highly parallel and quantitative measurement of single-cell gene expression. Nucleic Acids Res 2015;43:e60.
71. Ziegenhain C, Vieth B, Parekh S, et al. Comparative analysis of single-cell RNA sequencing methods. Mol Cell 2017;65:631-43.e4.
72. Svensson V, Natarajan KN, Ly LH, et al. Power analysis of single-cell RNA-sequencing experiments. Nat Methods 2017;14:381-7.
73. Zhang X, Li T, Liu F, et al. Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems. Mol Cell 2019;73:130-42.e5.
74. Mereu E, Lafzi A, Moutinho C, et al. Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. Nat Biotechnol 2020;38:747-55.
75. Poirion OB, Zhu X, Ching T, Garmire L. Single-cell transcriptomics bioinformatics and computational challenges. Front Genet 2016;7:163.
76. Finotello F, Rieder D, Hackl H, Trajanoski Z. Next-generation computational tools for interrogating cancer immunity. Nat Rev Genet 2019;20:724-46.
77. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 2018;36:411-20.
78. Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 2015;33:495-502.
79. Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 2018;19:15.
80. Yang X, Liu D, Liu F, et al. HTQC: a fast quality control toolkit for Illumina sequencing data. BMC Bioinformatics 2013;14:33.
81. Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 2014;15:R46.
82. Jiang P, Thomson JA, Stewart R. Quality control of single-cell RNA-seq by SinQC. Bioinformatics 2016;32:2514-6.
83. Diaz A, Liu SJ, Sandoval C, et al. SCell: integrated analysis of single-cell RNA-seq data. Bioinformatics 2016;32:2219-20.
84. Ilicic T, Kim JK, Kolodziejczyk AA, et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol 2016;17:29.
85. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009;25:1105-11.
86. Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013;29:15-21.
87. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods 2015;12:357-60.
88. Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 2016;34:525-7.
89. Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 2017;14:417-9.
90. Srivastava A, Malik L, Sarkar H, et al. Alignment and mapping methodology influence transcript abundance estimation. Genome Biol 2020;21:239.
91. Lytal N, Ran D, An L. Normalization methods on single-cell RNA-seq data: an empirical survey. Front Genet 2020;11:41.
92. Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol 2019;20:296.
93. Katayama S, Töhönen V, Linnarsson S, Kere J. SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization. Bioinformatics 2013;29:2943-5.
94. Vallejos CA, Marioni JC, Richardson S. BASiCS: bayesian analysis of single-cell sequencing data. PLoS Comput Biol 2015;11:e1004333.
95. Ding B, Zheng L, Zhu Y, et al. Normalization and noise reduction for single cell RNA-seq experiments. Bioinformatics 2015;31:2225-7.
96. Lun AT, Bach K, Marioni JC. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol 2016;17:75.
97. Bacher R, Chu LF, Leng N, et al. SCnorm: robust normalization of single-cell RNA-seq data. Nat Methods 2017;14:584-6.
98. Yip SH, Wang P, Kocher JA, Sham PC, Wang J. Corrigendum: Linnorm: improved statistical analysis for single cell RNA-seq expression data. Nucleic Acids Res 2017;45:13097.
99. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 2010;11:R25.
100. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139-40.
101. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550.
102. Tian L, Dong X, Freytag S, et al. Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nat Methods 2019;16:479-87.
103. Kadota K, Shimizu K. Commentary: a systematic evaluation of single cell RNA-Seq analysis pipelines. Front Genet 2020;11:941.
104. Kadota K, Nishiyama T, Shimizu K. A normalization strategy for comparing tag count data. Algorithms Mol Biol 2012;7:5.
105. Wagner F, Yan Y, Yanai I. K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data. bioRxiv 2018;217737.
106. Gong W, Kwak IY, Pota P, Koyano-Nakagawa N, Garry DJ. DrImpute: imputing dropout events in single cell RNA sequencing data. BMC Bioinformatics 2018;19:220.
107. Huang M, Wang J, Torre E, et al. SAVER: gene expression recovery for single-cell RNA sequencing. Nat Methods 2018;15:539-42.
110. Tran HTN, Ang KS, Chevrier M, et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol 2020;21:12.
111. Haghverdi L, Lun ATL, Morgan MD, Marioni JC. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol 2018;36:421-7.
112. Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell 2019;177:1888-902.e21.
113. Korsunsky I, Millard N, Fan J, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods 2019;16:1289-96.
114. Lin Y, Ghazanfar S, Wang KYX, et al. scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets. Proc Natl Acad Sci U S A 2019;116:9775-84.
115. Buettner F, Natarajan KN, Casale FP, et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotechnol 2015;33:155-60.
116. Barron M, Li J. Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Sci Rep 2016;6:33892.
117. Wang T, Li B, Nelson CE, Nabavi S. Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. BMC Bioinformatics 2019;20:40.
118. Van Der Maaten L. Accelerating t-SNE using tree-based algorithms. J Mach Learn Res 2015;15:3221-45.
119. Van Der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res 2008;9:2579-625.
120. Becht E, McInnes L, Healy J, et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol 2018:38-44.
121. Chen G, Ning B, Shi T. Single-cell RNA-Seq technologies and related computational data analysis. Front Genet 2019;10:317.
122. Kiselev VY, Andrews TS, Hemberg M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat Rev Genet 2019;20:273-82.
123. Cao J, Spielmann M, Qiu X, et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 2019;566:496-502.
124. Mu Q, Chen Y, Wang J. Deciphering brain complexity using single-cell sequencing. Genomics Proteomics Bioinformatics 2019;17:344-66.
125. Ofengeim D, Giagtzoglou N, Huh D, Zou C, Yuan J. Single-cell RNA sequencing: unraveling the brain one cell at a time. Trends Mol Med 2017;23:563-76.
126. Potter SS. Single-cell RNA sequencing for the study of development, physiology and disease. Nat Rev Nephrol 2018;14:479-92.
127. Ranzoni AM, Cvejic A. Single-cell biology: resolving biological complexity, one cell at a time. Development 2018;145:dev163972.
128. Levitin HM, Yuan J, Sims PA. Single-cell transcriptomic analysis of tumor heterogeneity. Trends Cancer 2018;4:264-8.
129. Zhang Q, He Y, Luo N, et al. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell 2019;179:829-45.e20.
130. Horning AM, Wang Y, Lin CK, et al. Single-cell RNA-seq reveals a subpopulation of prostate cancer cells with enhanced cell-cycle-related transcription and attenuated androgen response. Cancer Res 2018;78:853-64.
131. Wang W, Niu X, Stuart T, et al. A single-cell transcriptional roadmap for cardiopharyngeal fate diversification. Nat Cell Biol 2019;21:674-86.
132. Kharchenko PV, Silberstein L, Scadden DT. Bayesian approach to single-cell differential expression analysis. Nat Methods 2014;11:740-2.
133. Fan J, Salathia N, Liu R, et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat Methods 2016;13:241-4.
134. Wang T, Nabavi S. SigEMD: a powerful method for differential gene expression analysis in single-cell RNA sequencing data. Methods 2018;145:25-32.
135. Guo M, Wang H, Potter SS, Whitsett JA, Xu Y. SINCERA: a pipeline for single-cell RNA-Seq profiling analysis. PLoS Comput Biol 2015;11:e1004575.
136. Pierson E, Yau C. ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol 2015;16:241.
137. Wang B, Zhu J, Pierson E, Ramazzotti D, Batzoglou S. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat Methods 2017;14:414-6.
138. Bose S, Wan Z, Carr A, et al. Scalable microfluidics for single-cell RNA printing and sequencing. Genome Biol 2015;16.
139. Hou R, Denisenko E, Forrest ARR. scMatch: a single-cell gene expression profile annotation tool using reference datasets. Bioinformatics 2019;35:4688-95.
140. Aran D, Looney AP, Liu L, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol 2019;20:163-72.
141. Cao ZJ, Wei L, Lu S, Yang DC, Gao G. Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST. Nat Commun 2020;11:3458.
142. Tritschler S, Büttner M, Fischer DS, et al. Concepts and limitations for learning developmental trajectories from single cell genomics. Development 2019;146:dev170506.
143. Pang B, Xu J, Hu J, et al. Single-cell RNA-seq reveals the invasive trajectory and molecular cascades underlying glioblastoma progression. Mol Oncol 2019;13:2588-603.
144. Haghverdi L, Büttner M, Wolf FA, Buettner F, Theis FJ. Diffusion pseudotime robustly reconstructs lineage branching. Nat Methods 2016;13:845-8.
145. Chen H, Albergante L, Hsu JY, et al. Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nat Commun 2019;10:1903.
146. Chestnut B, Casie Chetty S, Koenig AL, Sumanas S. Single-cell transcriptomic analysis identifies the conversion of zebrafish Etv2-deficient vascular progenitors into skeletal muscle. Nat Commun 2020;11:2796.
147. Ji Z, Ji H. TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res 2016;44:e117.
148. Street K, Risso D, Fletcher RB, et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 2018;19:477.
149. Trapnell C, Cacchiarelli D, Grimsby J, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 2014;32:381-6.
150. Shin J, Berg DA, Zhu Y, et al. Single-cell RNA-Seq with waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 2015;17:360-72.
151. Marco E, Karp RL, Guo G, et al. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc Natl Acad Sci U S A 2014;111:E5643-50.
152. Singer M, Anderson AC. Revolutionizing cancer immunology: the power of next-generation sequencing technologies. Cancer Immunol Res 2019;7:168-73.
153. Giladi A, Amit I. Single-cell genomics: a stepping stone for future immunology discoveries. Cell 2018;172:14-21.
154. Cloonan N, Forrest AR, Kolle G, et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods 2008;5:613-9.
155. Yan L, Yang M, Guo H, et al. Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat Struct Mol Biol 2013;20:1131-9.
156. Biase FH, Cao X, Zhong S. Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell RNA sequencing. Genome Res 2014;24:1787-96.
157. Deng Q, Ramsköld D, Reinius B, Sandberg R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 2014;343:193-6.
158. Petropoulos S, Edsgärd D, Reinius B, et al. Single-cell RNA-Seq reveals lineage and X chromosome dynamics in human preimplantation embryos. Cell 2016;167:285.
159. Papalexi E, Satija R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol 2018;18:35-45.
160. Bar-On L, Birnberg T, Lewis KL, et al. CX3CR1+ CD8alpha+ dendritic cells are a steady-state population related to plasmacytoid dendritic cells. Proc Natl Acad Sci U S A 2010;107:14745-50.
161. Hume DA. Applications of myeloid-specific promoters in transgenic mice support in vivo imaging and functional genomics but do not support the concept of distinct macrophage and dendritic cell lineages or roles in immunity. J Leukoc Biol 2011;89:525-38.
162. Rosenberg R, Gertler R, Friederichs J, et al. Comparison of two density gradient centrifugation systems for the enrichment of disseminated tumor cells in blood. Cytometry 2002;49:150-8.
163. Zhu J, Yamane H, Paul WE. Differentiation of effector CD4 T cell populations (*). Annu Rev Immunol 2010;28:445-89.
164. Mahata B, Zhang X, Kolodziejczyk AA, et al. Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis. Cell Rep 2014;7:1130-42.
165. Desai TJ, Brownfield DG, Krasnow MA. Alveolar progenitor and stem cells in lung development, renewal and cancer. Nature 2014;507:190-4.
166. Kim CF, Jackson EL, Woolfenden AE, et al. Identification of bronchioalveolar stem cells in normal lung and lung cancer. Cell 2005;121:823-35.
167. Treutlein B, Brownfield DG, Wu AR, et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 2014;509:371-5.
168. Grün D, Lyubimova A, Kester L, et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 2015;525:251-5.
169. Hammoud SS, Low DH, Yi C, et al. Transcription and imprinting dynamics in developing postnatal male germline stem cells. Genes Dev 2015;29:2312-24.
170. Lau X, Munusamy P, Ng MJ, Sangrithi M. Single-cell RNA sequencing of the cynomolgus macaque testis reveals conserved transcriptional profiles during mammalian spermatogenesis. Dev Cell 2020;54:548-66.e7.
171. Green CD, Ma Q, Manske GL, et al. A comprehensive roadmap of murine spermatogenesis defined by single-cell RNA-Seq. Dev Cell 2018;46:651-67.e10.
172. Muraro MJ, Dharmadhikari G, Grün D, et al. A single-cell transcriptome Atlas of the human pancreas. Cell Syst 2016;3:385-394.e3.
174. Zeisel A, Muñoz-Manchado AB, Codeluppi S, et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 2015;347:1138-42.
175. Zeisel A, Hochgerner H, Lönnerberg P, et al. Molecular architecture of the mouse nervous system. Cell 2018;174:999-1014.e22.
176. Drokhlyansky E, Smillie CS, Van Wittenberghe N, et al. The human and mouse enteric nervous system at single-cell resolution. Cell 2020;182:1606-22.e23.
177. Almendro V, Marusyk A, Polyak K. Cellular heterogeneity and molecular evolution in cancer. Annu Rev Pathol 2013;8:277-302.
178. Tirosh I, Venteicher AS, Hebert C, et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 2016;539:309-13.
179. Miyamoto DT, Zheng Y, Wittner BS, et al. RNA-Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science 2015;349:1351-6.
180. Tirosh I, Izar B, Prakadan SM, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 2016;352:189-96.
181. Hong SP, Chan TE, Lombardo Y, et al. Single-cell transcriptomics reveals multi-step adaptations to endocrine therapy. Nat Commun 2019;10:3840.
182. McCray T, Moline D, Baumann B, Vander Griend DJ, Nonn L. Single-cell RNA-Seq analysis identifies a putative epithelial stem cell population in human primary prostate cells in monolayer and organoid culture conditions. Am J Clin Exp Urol 2019;7:123-38.
183. Maynard A, McCoach CE, Rotow JK, et al. Therapy-induced evolution of human lung cancer revealed by single-cell RNA sequencing. Cell 2020;182:1232-51.e22.
184. Davidson S, Efremova M, Riedel A, et al. Single-cell RNA sequencing reveals a dynamic stromal niche that supports tumor growth. Cell Rep 2020;31:107628.
185. Moon TS, Lou C, Tamsir A, Stanton BC, Voigt CA. Genetic programs constructed from layered logic gates in single cells. Nature 2012;491:249-53.
186. Fan X, Zhang X, Wu X, et al. Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol 2015;16:148.
187. Sasagawa Y, Danno H, Takada H, et al. Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads. Genome Biol 2018;19:29.
188. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016;32:3047-8.
189. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 2013;14:R36.
190. Wu TD, Nacu S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 2010;26:873-81.
191. Wang K, Singh D, Zeng Z, et al. MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 2010;38:e178.
192. Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol 2015;33:290-5.
193. Anders S, Pyl PT, Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 2015;31:166-9.
194. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014;30:923-30.
195. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 2011;12:323.
196. Trapnell C, Williams BA, Pertea G, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol 2010;28:511-5.
197. McCarthy DJ, Campbell KR, Lun AT, Wills QF. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 2017;33:1179-86.
198. Zhu X, Wolfgruber TK, Tasato A, Arisdakessian C, Garmire DG, Garmire LX. Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists. Genome Med 2017;9:108.
199. Gardeux V, David FPA, Shajkofci A, Schwalie PC, Deplancke B. ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data. Bioinformatics 2017;33:3123-5.
200. Lotfollahi M, Wolf FA, Theis FJ. scGen predicts single-cell perturbation responses. Nat Methods 2019;16:715-21.
201. Song Y, Botvinnik OB, Lovci MT, et al. Single-cell alternative splicing analysis with expedition reveals splicing dynamics during neuron differentiation. Mol Cell 2017;67:148-61.e5.
202. Huang Y, Sanguinetti G. BRIE: transcriptome-wide splicing quantification in single cells. Genome Biol 2017;18:123.
203. Qiu X, Hill A, Packer J, Lin D, Ma YA, Trapnell C. Single-cell mRNA quantification and differential analysis with Census. Nat Methods 2017;14:309-15.
204. Welch JD, Hu Y, Prins JF. Robust detection of alternative splicing in a population of single cells. Nucleic Acids Res 2016;44:e73.
205. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007;8:118-27.
206. Angerer P, Haghverdi L, Büttner M, Theis FJ, Marr C, Buettner F. Destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 2016;32:1241-3.
207. Ahmed S, Rattray M, Boukouvalas A. GrandPrix: scaling up the Bayesian GPLVM for single-cell data. Bioinformatics 2019;35:47-54.
