1932

Abstract

Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed.

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2020-07-20
2024-06-15
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Literature Cited

  1. 1. 
    Tanay A, Regev A 2017. Scaling single-cell genomics from phenomenology to mechanism. Nature 541:331–38
    [Google Scholar]
  2. 2. 
    Svensson V, Vento-Tormo R, Teichmann SA 2018. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13:599–604
    [Google Scholar]
  3. 3. 
    Regev A, Teichmann SA, Lander ES, Amit I, Benoist C et al. 2017. The Human Cell Atlas. eLife 6:e27041
    [Google Scholar]
  4. 4. 
    Svensson V, da Veiga Beltrame E 2019.A curated database reveals trends in single cell transcriptomics. bioRxiv 742304. https://doi.org/10.1101/742304
    [Crossref]
  5. 5. 
    Tang F, Barbacioru C, Wang Y, Nordman E, Lee C et al. 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6:377–82
    [Google Scholar]
  6. 6. 
    Chen X, Teichmann SA, Meyer KB 2018. From tissues to cell types and back: single-cell gene expression analysis of tissue architecture. Annu. Rev. Biomed. Data Sci. 1:29–51
    [Google Scholar]
  7. 7. 
    Ding J, Adiconis X, Simmons SK, Kowalczyk MS, Hession CC et al. 2019.Systematic comparative analysis of single cell RNA-sequencing methods. bioRxiv 632216. https://doi.org/10.1101/632216
    [Crossref]
  8. 8. 
    Mereu E, Lafzi A, Moutinho C, Ziegenhain C, MacCarthy DJ et al. 2019.Benchmarking single-cell RNA sequencing protocols for Cell Atlas Projects. bioRxiv 630087. https://doi.org/10.1101/630087
    [Crossref]
  9. 9. 
    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]
  10. 10. 
    Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C et al. 2013. STAR: ultrafast universal RNA-seq aligner: supplementary data. Bioinformatics 29:15–21
    [Google Scholar]
  11. 11. 
    Kim D, Paggi JM, Park C, Bennett C, Salzberg SL 2019. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37:907–15
    [Google Scholar]
  12. 12. 
    Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL 2013. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14:R36
    [Google Scholar]
  13. 13. 
    Li B, Dewey CN 2011. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinform. 12:323
    [Google Scholar]
  14. 14. 
    Anders S, Pyl PT, Huber W 2015. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31:166–69
    [Google Scholar]
  15. 15. 
    Liao Y, Smyth GK, Shi W 2014. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923–30
    [Google Scholar]
  16. 16. 
    Liao Y, Smyth GK, Shi W 2019. The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads. Nucleic Acids Res. 47:e47
    [Google Scholar]
  17. 17. 
    10x Genomics 2019.What is Cell Ranger? Tech. Support Memo., 10x Genomics, Pleasanton, CA. https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger
  18. 18. 
    Van den Berge K, Hembach KM, Soneson C, Tiberi S, Clement L et al. 2019. RNA sequencing data: Hitchhiker's guide to expression analysis. Annu. Rev. Biomed. Data Sci. 2:139–73
    [Google Scholar]
  19. 19. 
    Parekh S, Ziegenhain C, Vieth B, Enard W, Hellmann I 2018. zUMIs—a fast and flexible pipeline to process RNA sequencing data with UMIs. Gigascience 7:6giy059
    [Google Scholar]
  20. 20. 
    Melsted P, Ntranos V, Pachter L 2019. The barcode, UMI, set format and BUStools. Bioinformatics 35:214472–73
    [Google Scholar]
  21. 21. 
    Melsted P, Booeshaghi AS, Gao F, Beltrame E, Lu L et al. 2019.Modular and efficient pre-processing of single-cell RNA-seq. bioRxiv 673285. https://doi.org/10.1101/673285
    [Crossref]
  22. 22. 
    Srivastava A, Malik L, Smith T, Sudbery I, Patro R 2019. Alevin efficiently estimates accurate gene abundances from dscRNA-seq data. Genome Biol. 20:65
    [Google Scholar]
  23. 23. 
    Petukhov V, Guo J, Baryawno N, Severe N, Scadden DT et al. 2018. dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments. Genome Biol. 19:78
    [Google Scholar]
  24. 24. 
    Farouni R, Najafabadi HS 2019.Statistical modeling, estimation, and remediation of sample index hopping in multiplexed droplet-based single-cell RNA-seq data. bioRxiv 617225. https://doi.org/10.1101/617225
    [Crossref]
  25. 25. 
    Zhang MJ, Ntranos V, Tse D 2018.One read per cell per gene is optimal for single-cell RNA-seq. bioRxiv 389296. https://doi.org/10.1101/389296
    [Crossref]
  26. 26. 
    Svensson V, Beltrame EdV, Pachter L 2019.Quantifying the tradeoff between sequencing depth and cell number in single-cell RNA-seq. bioRxiv 762773. https://doi.org/10.1101/762773
    [Crossref]
  27. 27. 
    Baran-Gale J, Chandra T, Kirschner K 2018. Experimental design for single-cell RNA sequencing. Brief. Funct. Genom. 17:233–39
    [Google Scholar]
  28. 28. 
    Tung PY, Blischak JD, Hsiao CJ, Knowles DA, Burnett JE et al. 2017. Batch effects and the effective design of single-cell gene expression studies. Sci. Rep. 7:39921
    [Google Scholar]
  29. 29. 
    Lafzi A, Moutinho C, Picelli S, Heyn H 2018. Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies. Nat. Protoc. 13:2742–57
    [Google Scholar]
  30. 30. 
    Stoeckius M, Zheng S, Houck-Loomis B, Hao S, Yeung BZ et al. 2018. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19:224
    [Google Scholar]
  31. 31. 
    McGinnis CS, Patterson DM, Winkler J, Conrad DN, Hein MY et al. 2019. MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices. Nat. Methods 16:619–26
    [Google Scholar]
  32. 32. 
    Villani AC, Satija R, Reynolds G, Sarkizova S, Shekhar K et al. 2017. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356:eaah4573
    [Google Scholar]
  33. 33. 
    van den Brink SC, Sage F, Vértesy Ã, Spanjaard B, Peterson-Maduro J et al. 2017. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14:935–36
    [Google Scholar]
  34. 34. 
    O'Flanagan CH, Campbell KR, Zhang AW, Kabeer F, Lim JLP et al. 2019. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 20:210
    [Google Scholar]
  35. 35. 
    Lun AT, Riesenfeld S, Andrews T, Dao TP, Gomes T, Marioni JC 2019. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20:63
    [Google Scholar]
  36. 36. 
    McGinnis CS, Murrow LM, Gartner ZJ 2019. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8:329–37
    [Google Scholar]
  37. 37. 
    Wolock SL, Lopez R, Klein AM 2019. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8:281–91
    [Google Scholar]
  38. 38. 
    Xu J, Falconer C, Nguyen Q, Crawford J, McKinnon BD et al. 2019. Genotype-free demultiplexing of pooled single-cell RNA-Seq. Genome Biol. 20:290
    [Google Scholar]
  39. 39. 
    Heaton H, Talman AM, Knights A, Imaz M, Durbin R et al. 2019.souporcell: robust clustering of single cell RNAseq by genotype and ambient RNA inference without reference genotypes. bioRxiv 699637. https://doi.org/10.1101/699637
    [Crossref]
  40. 40. 
    Vieira Braga FA, Kar G, Berg M, Carpaij OA, Polanski K et al. 2019. A cellular census of human lungs identifies novel cell states in health and in asthma. Nat. Med. 25:1153–63
    [Google Scholar]
  41. 41. 
    Young MD, Behjati S 2018.SoupX removes ambient RNA contamination from droplet based single cell RNA sequencing data. bioRxiv 303727. https://doi.org/10.1101/303727
    [Crossref]
  42. 42. 
    Smillie CS, Biton M, Ordovas-Montanes J, Sullivan KM, Burgin G et al. 2019. Intra- and inter-cellular rewiring of the human colon during ulcerative colitis. Cell 178:714–30
    [Google Scholar]
  43. 43. 
    Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ et al. 2016. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 17:29
    [Google Scholar]
  44. 44. 
    Hie B, Cho H, DeMeo B, Bryson B, Berger B 2019. Geometric sketching compactly summarizes the single-cell transcriptomic landscape. Cell Syst. 8:483–93
    [Google Scholar]
  45. 45. 
    Iacono G, Mereu E, Guillaumet-Adkins A, Corominas R, Cuscó I et al. 2018. bigSCale: an analytical framework for big-scale single-cell data. Genome Res. 28:878–90
    [Google Scholar]
  46. 46. 
    Baran Y, Bercovich A, Sebe-Pedros A, Lubling Y, Giladi A et al. 2018. MetaCell: analysis of single cell RNA-seq data using K-nn graph partitions. Genome Biol. 20:206
    [Google Scholar]
  47. 47. 
    Hie B, Cho H, Bryson B, Berger B 2019.Coexpression uncovers a unified single-cell transcriptomic landscape. bioRxiv 719088. https://doi.org/10.1101/719088
    [Crossref]
  48. 48. 
    Wolf FA, Hamey FK, Plass M, Solana J, Dahlin JS et al. 2019. PAGA: Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 29:59
    [Google Scholar]
  49. 49. 
    Eraslan G, Simon LM, Mircea M, Mueller NS, Theis FJ 2019. Single-cell RNA-seq denoising using a deep count autoencoder. Nat. Commun. 10:390
    [Google Scholar]
  50. 50. 
    Huang M, Wang J, Torre E, Dueck H, Shaffer S et al. 2018. SAVER: gene expression recovery for single-cell RNA sequencing. Nat. Methods 15:539–42
    [Google Scholar]
  51. 51. 
    Li WV, Li JJ 2018. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat. Commun. 9:997
    [Google Scholar]
  52. 52. 
    van Dijk D, Sharma R, Nainys J, Yim K, Kathail P et al. 2018. Recovering gene interactions from single-cell data using data diffusion. Cell 174:716–29
    [Google Scholar]
  53. 53. 
    Linderman GC, Zhao J, Kluger Y 2018.Zero-preserving imputation of sc RNA-seq data using low-rank approximation. bioRxiv 397588. https://doi.org/10.1101/397588
    [Crossref]
  54. 54. 
    Zhang L, Zhang S 2020.Comparison of computational methods for imputing single-cell RNA-sequencing data. IEEE/ACM Trans. Comput. Biol. Bioinform. 17:376–89
  55. 55. 
    Andrews TS, Hemberg M 2018. False signals induced by single-cell imputation. F1000Research 7:1740
    [Google Scholar]
  56. 56. 
    Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E et al. 2019. Comprehensive integration of single-cell data. Cell 177:1888–902
    [Google Scholar]
  57. 57. 
    Vallejos CA, Risso D, Scialdone A, Dudoit S, Marioni JC 2017. Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat. Methods 14:565–71
    [Google Scholar]
  58. 58. 
    Bacher R, Chu LF, Leng N, Gasch AP, Thomson JA et al. 2017. SCnorm: robust normalization of single-cell RNA-seq data. Nat. Methods 14:584–86
    [Google Scholar]
  59. 59. 
    Hafemeister C, Satija R 2019. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20:296
    [Google Scholar]
  60. 60. 
    Wolf FA, Angerer P, Theis FJ 2018. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19:15
    [Google Scholar]
  61. 61. 
    Lun AT, Bach K, Marioni JC 2016. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17:75
    [Google Scholar]
  62. 62. 
    Tian L, Dong X, Freytag S, Lê Cao KA, Su S et al. 2019. Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nat. Methods 16:479–87
    [Google Scholar]
  63. 63. 
    Büttner M, Miao Z, Wolf FA, Teichmann SA, Theis FJ 2019. A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods 16:43–49
    [Google Scholar]
  64. 64. 
    Yip SH, Wang P, Kocher JPA, Sham PC, Wang J 2017. Linnorm: improved statistical analysis for single cell RNA-seq expression data. Nucleic Acids Res. 45:e179
    [Google Scholar]
  65. 65. 
    Qiu X, Hill A, Packer J, Lin D, Ma YA, Trapnell C 2017. Single-cell mRNA quantification and differential analysis with Census. Nat. Methods 14:309–15
    [Google Scholar]
  66. 66. 
    Love MI, Huber W, Anders S 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15:550
    [Google Scholar]
  67. 67. 
    Cole MB, Risso D, Wagner A, DeTomaso D, Ngai J et al. 2019. Performance assessment and selection of normalization procedures for single-cell RNA-seq. Cell Syst. 8:315–28
    [Google Scholar]
  68. 68. 
    Townes FW, Hicks SC, Aryee MJ, Irizarry RA 2019. Feature selection and dimension reduction for single cell RNA-seq based on a multinomial model. Genome Biol. 20:295
    [Google Scholar]
  69. 69. 
    Lun A 2018.Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing data. bioRxiv 404962. https://doi.org/10.1101/404962
    [Crossref]
  70. 70. 
    Brennecke P, Anders S, Kim JK, Kołodziejczyk AA, Zhang X et al. 2013. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10:1093–98
    [Google Scholar]
  71. 71. 
    Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A et al. 2015. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–201
    [Google Scholar]
  72. 72. 
    Yip SH, Sham PC, Wang J 2018. Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data. Brief. Bioinform. 20:1583–89
    [Google Scholar]
  73. 73. 
    Smyth GK 2005. Limma: linear models for microarray data. In Bioinformatics and Computational Biology Solutions Using R and Bioconductored. R Gentleman, VJ Carey, W Huber, RA Irizarry, S Dudoitpp397–420 New York: Springer
    [Google Scholar]
  74. 74. 
    Tirosh I, Izar B, Prakadan SM, Wadsworth MH, Treacy D et al. 2016. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352:189–96
    [Google Scholar]
  75. 75. 
    Johnson WE, Li C, Rabinovic A 2007. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118–27
    [Google Scholar]
  76. 76. 
    Haghverdi L, Lun A, Morgan M, Marioni J 2018. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36:421–27
    [Google Scholar]
  77. 77. 
    Butler A, Hoffman P, Smibert P, Papalexi E, Satija R 2018. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36:411–20
    [Google Scholar]
  78. 78. 
    Dekel T, Oron S, Rubinstein M, Avidan S, Freeman WT 2015. Best-buddies similarity for robust template matching. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognitionpp2021–29 Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  79. 79. 
    Hie B, Bryson B, Berger B 2019. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37:685–91
    [Google Scholar]
  80. 80. 
    Polański K, Young MD, Miao Z, Meyer KB, Teichmann SA, Park J-E 2020.BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36:964–65
  81. 81. 
    Barkas N, Petukhov V, Nikolaeva D, Lozinsky Y, Demharter S et al. 2019. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. Methods 16:695–98
    [Google Scholar]
  82. 82. 
    Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F et al. 2018. Fast, sensitive, and accurate integration of single cell data with Harmony. Nat. Methods 16:1289–96
    [Google Scholar]
  83. 83. 
    Welch J, Kozareva V, Ferrara A, Vanderburg C, Martin C, Macosko E 2019. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177:1873–87
    [Google Scholar]
  84. 84. 
    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]
  85. 85. 
    Crow M, Paul A, Ballouz S, Huang ZJ, Gillis J 2018. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat. Commun. 9:884
    [Google Scholar]
  86. 86. 
    Rousseeuw PJ 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20:53–65
    [Google Scholar]
  87. 87. 
    Stuart T, Satija R 2019. Integrative single-cell analysis. Nat. Rev. Genet. 20:257–72
    [Google Scholar]
  88. 88. 
    Loh PR, Baym M, Berger B 2012. Compressive genomics. Nat. Biotechnol. 30:627–30
    [Google Scholar]
  89. 89. 
    Yu YW, Daniels NM, Danko DC, Berger B 2015. Entropy-scaling search of massive biological data. Cell Syst. 1:130–40
    [Google Scholar]
  90. 90. 
    Cleary B, Cong L, Cheung A, Lander ES, Regev A 2017. Efficient generation of transcriptomic profiles by random composite measurements. Cell 171:1424–36
    [Google Scholar]
  91. 91. 
    Stein-O'Brien GL, Arora R, Culhane AC, Favorov AV, Garmire LX et al. 2018. Enter the matrix: factorization uncovers knowledge from omics. Trends Genet. 34:790–805
    [Google Scholar]
  92. 92. 
    van der Maaten LJP, Hinton GE 2008. Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9:2579–605
    [Google Scholar]
  93. 93. 
    Wattenberg M, Viégas F, Johnson I 2016.How to use t-SNE effectively. Distill. http://doi.org/10.23915/distill.00002
    [Crossref]
  94. 94. 
    Cho H, Berger B, Peng J 2018. Generalizable and scalable visualization of single-cell data using neural networks. Cell Syst. 7:185–91
    [Google Scholar]
  95. 95. 
    Linderman GC, Rachh M, Hoskins JG, Steinerberger S, Kluger Y 2019. Fast interpolation-based t-SNE for improved visualization of single-cell RNA-seq data. Nat. Methods 16:243–45
    [Google Scholar]
  96. 96. 
    van der Maaten L 2014. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15:3221–45
    [Google Scholar]
  97. 97. 
    Jacomy M, Venturini T, Heymann S, Bastian M 2014. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLOS ONE 9:6e98679
    [Google Scholar]
  98. 98. 
    Weinreb C, Wolock S, Klein AM 2018. SPRING: a kinetic interface for visualizing high dimensional single-cell expression data. Bioinformatics 34:1246–48
    [Google Scholar]
  99. 99. 
    McInnes L, Healy J 2018.UMAP: uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426 [stat.ML]
  100. 100. 
    Becht E, McInnes L, Healy J, Dutertre CA, Kwok IW et al. 2019. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37:38–44
    [Google Scholar]
  101. 101. 
    Chen Z, An S, Bai X, Gong F, Ma L, Wan L 2019. DensityPath: an algorithm to visualize and reconstruct cell state-transition path on density landscape for single-cell RNA sequencing data. Bioinformatics 35:2593–601
    [Google Scholar]
  102. 102. 
    An S, Ma L, Wan L 2019. TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data. BMC Genom. 20:224
    [Google Scholar]
  103. 103. 
    Moon KR, van Dijk D, Wang Z, Gigante S, Burkhardt DB et al. 2019. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37:1482–92
    [Google Scholar]
  104. 104. 
    Xu C, Lopez R, Mehlman E, Regier J, Jordan MI, Yosef N 2019.Harmonization and annotation of single-cell transcriptomics data with deep generative models. bioRxiv 532895. https://doi.org/10.1101/532895
    [Crossref]
  105. 105. 
    Mikolov T, Sutskever I, Chen K, Corrado G, Dean J 2013.Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26 (NIPS 2013). https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality
  106. 106. 
    Pennington J, Socher R, Manning CD 2014. GloVe: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)pp1532–43 Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  107. 107. 
    Kingma DP, Welling M 2014.Auto-encoding variational Bayes. Paper presented at International Conference on Learning Representations (ICLR 2014), Banff, Can., Apr. 14–16
  108. 108. 
    Rezende DJ, Mohamed S, Wierstra D 2014. Stochastic backpropagation and approximate inference in deep generative models. Proc. Mach. Learn. Res. 32:21278–86
    [Google Scholar]
  109. 109. 
    Lotfollahi M, Wolf FA, Theis FJ 2019. scGen predicts single-cell perturbation responses. Nat. Methods 16:715–21
    [Google Scholar]
  110. 110. 
    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]
  111. 111. 
    Petegrosso R, Li Z, Kuang R 2019. Machine learning and statistical methods for clustering single-cell RNA-sequencing data. Brief. Bioinform. 2019:bbz063
    [Google Scholar]
  112. 112. 
    Zeng T, Dai H 2019. Single-cell RNA sequencing-based computational analysis to describe disease heterogeneity. Front. Genet. 10:629
    [Google Scholar]
  113. 113. 
    Duò A, Robinson MD, Soneson C 2018. A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000Research 7:1141
    [Google Scholar]
  114. 114. 
    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. 20:294
    [Google Scholar]
  115. 115. 
    Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E et al. 2018. Comprehensive integration of single cell data. Cell 177:71888–902.e21
    [Google Scholar]
  116. 116. 
    Traag VA, Waltman L, van Eck NJ 2019. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9:5233
    [Google Scholar]
  117. 117. 
    žurauskienė J, Yau C 2016. pcaReduce: hierarchical clustering of single cell transcriptional profiles. BMC Bioinform. 17:140
    [Google Scholar]
  118. 118. 
    Lin P, Troup M, Ho JWK 2017. CIDR: ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol. 18:59
    [Google Scholar]
  119. 119. 
    Kim T, Chen IR, Lin Y, Wang AYY, Yang JYH, Yang P 2018. Impact of similarity metrics on single-cell RNA-seq data clustering. Brief. Bioinform. 20:62316–26
    [Google Scholar]
  120. 120. 
    Campbell JN, Macosko EZ, Fenselau H, Pers TH, Lyubetskaya A et al. 2017. A molecular census of arcuate hypothalamus and median eminence cell types. Nat. Neurosci. 20:484–96
    [Google Scholar]
  121. 121. 
    Tasic B, Menon V, Nguyen TN, Kim TK, Jarsky T et al. 2016. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19:335–46
    [Google Scholar]
  122. 122. 
    Ding H, Wang W, Califano A 2018. iterClust: a statistical framework for iterative clustering analysis. Bioinformatics 34:2865–66
    [Google Scholar]
  123. 123. 
    Hu MW, Kim DW, Liu S, Zack DJ, Blackshaw S, Qian J 2019. PanoView: an iterative clustering for single-cell RNA sequencing data. PLOS Comput. Biol. 15:8e1007040
    [Google Scholar]
  124. 124. 
    Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G et al. 2015. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347:1138–42
    [Google Scholar]
  125. 125. 
    Jindal A, Gupta P,Jayadeva, Sengupta D 2018. Discovery of rare cells from voluminous single cell expression data. Nat. Commun. 9:4719
    [Google Scholar]
  126. 126. 
    Tsoucas D, Yuan GC 2018. GiniClust2: a cluster-aware, weighted ensemble clustering method for cell-type detection. Genome Biol. 19:58
    [Google Scholar]
  127. 127. 
    Wegmann R, Neri M, Schuierer S, Bilican B, Hartkopf H et al. 2019. CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq data. Genome Biol. 20:142
    [Google Scholar]
  128. 128. 
    Sinha D, Kumar A, Kumar H, Bandyopadhyay S, Sengupta D 2018. dropClust: efficient clustering of ultra-large scRNA-seq data. Nucleic Acids Res. 46:e36
    [Google Scholar]
  129. 129. 
    Grün D, Muraro MJ, Boisset JC, Wiebrands K, Lyubimova A et al. 2016. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19:266–77
    [Google Scholar]
  130. 130. 
    Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C et al. 2018. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174:1293–308
    [Google Scholar]
  131. 131. 
    Sun Z, Chen L, Xin H, Jiang Y, Huang Q et al. 2019. A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies. Nat. Commun. 10:1649
    [Google Scholar]
  132. 132. 
    Duan T, Pinto JP, Xie X 2019. Parallel clustering of single cell transcriptomic data with split-merge sampling on Dirichlet process mixtures. Bioinformatics 35:953–61
    [Google Scholar]
  133. 133. 
    Yang Y, Huh R, Culpepper HW, Lin Y, Love MI, Li Y 2019. SAFE-clustering: single-cell aggregated (from ensemble) clustering for single-cell RNA-seq data. Bioinformatics 35:1269–77
    [Google Scholar]
  134. 134. 
    Kiselev VY, Kirschner K, Schaub MT, Andrews T, Yiu A et al. 2017. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14:483–86
    [Google Scholar]
  135. 135. 
    Tian T, Wan J, Song Q, Wei Z 2019. Clustering single-cell RNA-seq data with a model-based deep learning approach. Nat. Mach. Intell. 1:191–98
    [Google Scholar]
  136. 136. 
    Rashid S, Shah S, Bar-Joseph Z, Pandya R 2019. Dhaka: variational autoencoder for unmasking tumor heterogeneity from single cell genomic data. Bioinformatics 2019:btz095
    [Google Scholar]
  137. 137. 
    Srinivasan S, Johnson NT, Korkin D 2019.A hybrid deep clustering approach for robust cell type profiling using single-cell RNA-seq data. bioRxiv 511626. https://doi.org/10.1101/511626
    [Crossref]
  138. 138. 
    Tritschler S, Büttner M, Fischer DS, Lange M, Bergen V et al. 2019. Concepts and limitations for learning developmental trajectories from single cell genomics. Development 146:dev170506
    [Google Scholar]
  139. 139. 
    Norman TM, Horlbeck MA, Replogle JM, Ge AY, Xu A et al. 2019. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science 4438:eaax4438
    [Google Scholar]
  140. 140. 
    Saelens W, Cannoodt R, Todorov H, Saeys Y 2019. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37:5547–54
    [Google Scholar]
  141. 141. 
    Weinreb C, Wolock S, Tusi BK, Socolovsky M, Klein AM 2018. Fundamental limits on dynamic inference from single-cell snapshots. PNAS 115:E2467–76
    [Google Scholar]
  142. 142. 
    Rashid S, Kotton DN, Bar-Joseph Z 2017. TASIC: determining branching models from time series single cell data. Bioinformatics 33:2504–12
    [Google Scholar]
  143. 143. 
    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
    [Google Scholar]
  144. 144. 
    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]
  145. 145. 
    Soneson C, Robinson MD 2018. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15:255–61
    [Google Scholar]
  146. 146. 
    Amezquita RA, Lun ATL, Becht E, Carey VJ, Carpp LN et al. 2019. Orchestrating single-cell analysis with Bioconductor. Nat. Methods 17:137–45
    [Google Scholar]
  147. 147. 
    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]
  148. 148. 
    Finak G, McDavid A, Yajima M, Deng J, Gersuk V et al. 2015. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16:278
    [Google Scholar]
  149. 149. 
    Luecken MD, Theis FJ 2019. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15:e8746
    [Google Scholar]
  150. 150. 
    Wang T, Li B, Nelson CE, Nabavi S 2019. Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. BMC Bioinform. 20:40
    [Google Scholar]
  151. 151. 
    Jaakkola MK, Seyednasrollah F, Mehmood A, Elo LL 2017. Comparison of methods to detect differentially expressed genes between single-cell populations. Brief. Bioinform. 18:735–43
    [Google Scholar]
  152. 152. 
    Dal Molin A, Baruzzo G, Di Camillo B 2017. Single-cell RNA-sequencing: assessment of differential expression analysis methods. Front. Genet. 8:62
    [Google Scholar]
  153. 153. 
    Miao Z, Zhang X 2016. Differential expression analyses for single-cell RNA-seq: old questions on new data. Quant. Biol. 4:243–60
    [Google Scholar]
  154. 154. 
    Vieth B, Parekh S, Ziegenhain C, Enard W, Hellmann I 2019. A systematic evaluation of single cell RNA-seq analysis pipelines. Nat. Commun. 10:4667
    [Google Scholar]
  155. 155. 
    Zhang JM, Kamath GM, Tse DN 2019. Valid postclustering differential analysis for single-cell RNA-seq. Cell 9:4383–92.e6
    [Google Scholar]
  156. 156. 
    Ntranos V, Yi L, Melsted P, Pachter L 2019. A discriminative learning approach to differential expression analysis for single-cell RNA-seq. Nat. Methods 16:163–66
    [Google Scholar]
  157. 157. 
    Crowell HL, Soneson C, Germain PL, Calini D, Collin L et al. 2019.On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data. bioRxiv 713412. https://doi.org/10.1101/713412
    [Crossref]
  158. 158. 
    Robinson MD, McCarthy DJ, Smyth GK 2009. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139–40
    [Google Scholar]
  159. 159. 
    Van den Berge K, de Bézieux HR, Street K, Saelens W, Cannoodt R et al. 2019. Trajectory-based differential expression analysis for single-cell sequencing data. bioRxiv 623397. https://doi.org/10.1101/623397
    [Crossref] [Google Scholar]
  160. 160. 
    Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL et al. 2005. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. PNAS 102:15545–50
    [Google Scholar]
  161. 161. 
    Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H et al. 2017. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14:1083–86
    [Google Scholar]
  162. 162. 
    Benjamini Y, Hochberg Y 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57:289–300
    [Google Scholar]
  163. 163. 
    Blencowe M, Arneson D, Ding J, Chen YW, Saleem Z, Yang X 2019. Network modeling of single-cell omics data: challenges, opportunities, and progresses. Emerg. Top. Life Sci. 3:4379–98
    [Google Scholar]
  164. 164. 
    Chen S, Mar JC 2018. Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data. BMC Bioinform. 19:232
    [Google Scholar]
  165. 165. 
    Todorov H, Cannoodt R, Saelens W, Saeys Y 2019. Network inference from single-cell transcriptomic data. In Gene Regulatory Networks: Methods and Protocolsed. G Sanguinetti, VA Huynh-Thupp235–49 New York: Humana Press
    [Google Scholar]
  166. 166. 
    Iacono G, Massoni-Badosa R, Heyn H 2019. Single-cell transcriptomics unveils gene regulatory network plasticity. Genome Biol. 20:110
    [Google Scholar]
  167. 167. 
    Fiers MWEJ, Minnoye L, Aibar S, Bravo González-Blas C, Kalender Atak Z, Aerts S 2018. Mapping gene regulatory networks from single-cell omics data. Brief. Funct. Genom. 17:246–54
    [Google Scholar]
  168. 168. 
    Papili Gao N, Ud-Dean SMM, Gandrillon O, Gunawan R 2018. SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles. Bioinformatics 34:258–66
    [Google Scholar]
  169. 169. 
    Sanchez-Castillo M, Blanco D, Tienda-Luna IM, Carrion MC, Huang Y 2018. A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data. Bioinformatics 34:964–70
    [Google Scholar]
  170. 170. 
    Ocone A, Haghverdi L, Mueller NS, Theis FJ 2015. Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data. Bioinformatics 31:i89–96
    [Google Scholar]
  171. 171. 
    Matsumoto H, Kiryu H, Furusawa C, Ko MSH, Ko SBH et al. 2017. SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. Bioinformatics 33:152314–21
    [Google Scholar]
  172. 172. 
    Pratapa A, Jalihal AP, Law JN, Bharadwaj A, Murali TM 2019. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat. Methods 17:147–54
    [Google Scholar]
  173. 173. 
    Liu H, Li P, Zhu M, Wang X, Lu J, Yu T 2016. Nonlinear network reconstruction from gene expression data using marginal dependencies measured by DCOL. PLOS ONE 11:e0158247
    [Google Scholar]
  174. 174. 
    Peng H, Zeng X, Zhou Y, Zhang D, Nussinov R, Cheng F 2019. A component overlapping attribute clustering (COAC) algorithm for single-cell RNA sequencing data analysis and potential pathobiological implications. PLOS Comput. Biol. 15:e1006772
    [Google Scholar]
  175. 175. 
    Mohammadi S, Ravindra V, Gleich DF, Grama A 2018. A geometric approach to characterize the functional identity of single cells. Nat. Commun. 9:1516
    [Google Scholar]
  176. 176. 
    Skinnider MA, Squair JW, Foster LJ 2019. Evaluating measures of association for single-cell transcriptomics. Nat. Methods 16:381–86
    [Google Scholar]
  177. 177. 
    Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R 2019. CellPhoneDB v2.0: inferring cell-cell communication from combined expression of multi-subunit receptor-ligand complexes. bioRxiv 680926. https://doi.org/10.1101/680926
    [Crossref] [Google Scholar]
  178. 178. 
    Angelidis I, Simon LM, Fernandez IE, Strunz M, Mayr CH et al. 2019. An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics. Nat. Commun. 10:963
    [Google Scholar]
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