Mixed-effects association of single cells identifies an expanded effector CD4+ T cell subset in rheumatoid arthritis

Science Translational Medicine

Volumn 10, Issue 463, 2018, Pages

  Fonseka, Chamith Y ^a,b,c   Rao, Deepak A ^a   Teslovich, Nikola C ^a   Korsunsky, Ilya ^a   Hannes, Susan K ^a   Slowikowski, Kamil ^a,b,c   Gurish, Michael F ^a   Donlin, Laura T ^d,e   Lederer, James A ^a   Weinblatt, Michael E ^a   Massarotti, Elena M ^a   Coblyn, Jonathan S ^a   Helfgott, Simon M ^a   Todd, Derrick J ^a   Bykerk, Vivian P ^d,e   Karlson, Elizabeth W ^a   Ermann, Joerg ^a   Lee, Yvonne C ^a,f   Brenner, Michael B ^a   Raychaudhuri, Soumya ^a,c,g  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

^b HARVARD UNIVERSITY   (United States)

^c BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^d HOSPITAL FOR SPECIAL SURGERY   (United States)

^e WEILL CORNELL MEDICAL COLLEGE   (United States)

^f NORTHWESTERN UNIVERSITY   (United States)

^g UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMOKINE RECEPTOR CCR5; CHEMOKINE RECEPTOR CCR7; CHEMOKINE RECEPTOR CXCR3; GAMMA INTERFERON; GRANZYME A; TRANSCRIPTION FACTOR T BET; CD27 ANTIGEN; HLA DR ANTIGEN; TRANSCRIPTOME;

ALGORITHM; ARTICLE; BLOOD; BLOOD SAMPLING; CD4+ T LYMPHOCYTE; CELL EXPANSION; CELL POPULATION; CONTROLLED STUDY; CYTOKINE PRODUCTION; FLOW CYTOMETRY; GENE; GENE EXPRESSION; HELPER CELL; HLA GENE; HUMAN; HUMAN CELL; IMMUNOPHENOTYPING; MASS CYTOMETRY; MEMORY CELL; PERIPHERAL BLOOD MONONUCLEAR CELL; PHENOTYPE; PRIORITY JOURNAL; RHEUMATOID ARTHRITIS; RNA SEQUENCE; SYNOVIAL FLUID; UPREGULATION; AGED; CELL PROLIFERATION; CYTOTOXICITY; FEMALE; GENETICS; IMMUNOLOGICAL MEMORY; IMMUNOLOGY; MALE; METABOLISM; MIDDLE AGED; PATHOLOGY; T LYMPHOCYTE SUBPOPULATION; TH1 CELL;

AGED; ARTHRITIS, RHEUMATOID; CD4-POSITIVE T-LYMPHOCYTES; CELL PROLIFERATION; CYTOTOXICITY, IMMUNOLOGIC; FEMALE; HLA-DR ANTIGENS; HUMANS; IMMUNOLOGIC MEMORY; MALE; MIDDLE AGED; T-LYMPHOCYTE SUBSETS; TH1 CELLS; TRANSCRIPTOME; TUMOR NECROSIS FACTOR RECEPTOR SUPERFAMILY, MEMBER 7;

EID: 85055050337     PISSN: 19466234     EISSN: 19466242     Source Type: Journal    
DOI: 10.1126/scitranslmed.aaq0305     Document Type: Article

References (73)

1. K. E. Neu, Q. Tang, P. C. Wilson, A. A. Khan, Single-cell genomics: Approaches and utility in immunology. Trends Immunol. 38, 140–149 (2017).
2. M. Cross, E. Smith, D. Hoy, L. Carmona, F. Wolfe, T. Vos, B. Williams, S. Gabriel, M. Lassere, N. Johns, R. Buchbinder, A. Woolf, L. March, The global burden of rheumatoid arthritis: Estimates from the global burden of disease 2010 study. Ann. Rheum. Dis. 73, 1316–1322 (2014).
3. S. Viatte, D. Plant, S. Raychaudhuri, Genetics and epigenetics of rheumatoid arthritis. Nat. Rev. Rheumatol. 9, 141–153 (2013).
4. J. Kurkó, T. Besenyei, J. Laki, T. T. Glant, K. Mikecz, Z. Szekanecz, Genetics of rheumatoid arthritis—A comprehensive review. Clin. Rev. Allergy Immunol. 45, 170–179 (2013).
5. A. M. Gizinski, D. A. Fox, T cell subsets and their role in the pathogenesis of rheumatic disease. Curr. Opin. Rheumatol. 26, 204–210 (2014).
6. W. Wang, S. Shao, Z. Jiao, M. Guo, H. Xu, S. Wang, The Th17/Treg imbalance and cytokine environment in peripheral blood of patients with rheumatoid arthritis. Rheumatol. Int. 32, 887–893 (2012).
7. S. Raychaudhuri, C. Sandor, E. A. Stahl, J. Freudenberg, H.-S. Lee, X. Jia, L. Alfredsson, L. Padyukov, L. Klareskog, J. Worthington, K. A. Siminovitch, S.-C. Bae, R. M. Plenge, P. K. Gregersen, P. I. W. de Bakker, Five amino acids in three HLA proteins explain most of the association between MHC and seropositive rheumatoid arthritis. Nat. Genet. 44, 291–296 (2012).
8. C. Perricone, F. Ceccarelli, G. Valesini, An overview on the genetic of rheumatoid arthritis: A never-ending story. Autoimmun. Rev. 10, 599–608 (2011).
9. D. Diogo, Y. Okada, R. M. Plenge, Genome-wide association studies to advance our understanding of critical cell types and pathways in rheumatoid arthritis: Recent findings and challenges. Curr. Opin. Rheumatol. 26, 85–92 (2014).
10. S. Eyre, J. Bowes, D. Diogo, A. Lee, A. Barton, P. Martin, A. Zhernakova, E. Stahl, S. Viatte, K. McAllister, C. I. Amos, L. Padyukov, R. E. M. Toes, T. W. J. Huizinga, C. Wijmenga, G. Trynka, L. Franke, H.-J. Westra, L. Alfredsson, X. Hu, C. Sandor, P. I. W. de Bakker, S. Davila, C. C. Khor, K. K. Heng, R. Andrews, S. Edkins, S. E. Hunt, C. Langford, D. Symmons; Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate; Wellcome Trust Case Control Consortium; P. Concannon, S. Onengut-Gumuscu, S. S. Rich, P. Deloukas, M. A. Gonzalez-Gay, L. Rodriguez-Rodriguez, L. Ärlsetig, J. Martin, S. Rantapää-Dahlqvist, R. M. Plenge, S. Raychaudhuri, L. Klareskog, P. K. Gregersen, J. Worthington, High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat. Genet. 44, 1336–1340 (2012).
11. Y. Okada, D. Wu, G. Trynka, T. Raj, C. Terao, K. Ikari, Y. Kochi, K. Ohmura, A. Suzuki, S. Yoshida, R. R. Graham, A. Manoharan, W. Ortmann, T. Bhangale, J. C. Denny, R. J. Carroll, A. E. Eyler, J. D. Greenberg, J. M. Kremer, D. A. Pappas, L. Jiang, J. Yin, L. Ye, D.-F. Su, J. Yang, G. Xie, E. Keystone, H.-J. Westra, T. Esko, A. Metspalu, X. Zhou, N. Gupta, D. Mirel, E. A. Stahl, D. Diogo, J. Cui, K. Liao, M. H. Guo, K. Myouzen, T. Kawaguchi, M. J. H. Coenen, P. L. C. M. van Riel, M. A. F. J. van de Laar, H.-J. Guchelaar, T. W. J. Huizinga, P. Dieudé, X. Mariette, S. L. Bridges Jr., A. Zhernakova, R. E. M. Toes, P. P. Tak, C. Miceli-Richard, S.-Y. Bang, H.-S. Lee, J. Martin, M. A. Gonzalez-Gay, L. Rodriguez-Rodriguez, S. Rantapää-Dahlqvist, L. Ärlestig, H. K. Choi, Y. Kamatani, P. Galan, M. Lathrop; RACI Consortium; GARNET consortium; S. Eyre, J. Bowes, A. Barton, N. de Vries, L. W. Moreland, L. A. Criswell, E. W. Karlson, A. Taniguchi, R. Yamada, M. Kubo, J. S. Liu, S.-C. Bae, J. Worthington, L. Padyukov, L. Klareskog, P. K. Gregersen, S. Raychaudhuri, B. E. Stranger, P. L. De Jager, L. Franke, P. M. Visscher, M. A. Brown, H. Yamanaka, T. Mimori, A. Takahashi, H. Xu, T. W. Behrens, K. A. Siminovitch, S. Momohara, F. Matsuda, K. Yamamoto, R. M. Plenge, Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).
12. X. Hu, H. Kim, E. Stahl, R. Plenge, M. Daly, S. Raychaudhuri, Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets. Am. J. Hum. Genet. 89, 496–506 (2011).
13. D. A. Rao, M. F. Gurish, J. L. Marshall, K. Slowikowski, C. Y. Fonseka, Y. Liu, L. T. Donlin, L. A. Henderson, K. Wei, F. Mizoguchi, N. C. Teslovich, M. E. Weinblatt, E. M. Massarotti, J. S. Coblyn, S. M. Helfgott, Y. C. Lee, D. J. Todd, V. P. Bykerk, S. M. Goodman, A. B. Pernis, L. B. Ivashkiv, E. W. Karlson, P. A. Nigrovic, A. Filer, C. D. Buckley, J. A. Lederer, S. Raychaudhuri, M. B. Brenner, Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature 542, 110–114 (2017).
14. P. B. Martens, J. J. Goronzy, D. Schaid, C. M. Weyand, Expansion of unusual CD4+ T cells in severe rheumatoid arthritis. Arthritis Rheum. 40, 1106–1114 (1997).
15. M. Scarsi, T. Ziglioli, P. Airò, Decreased circulating CD28-negative T cells in patients with rheumatoid arthritis treated with abatacept are correlated with clinical response. J. Rheumatol. 37, 911–916 (2010).
16. K. J. Warrington, S. Takemura, J. J. Goronzy, C. M. Weyand, CD4+,CD28- T cells in rheumatoid arthritis patients combine features of the innate and adaptive immune systems. Arthritis Rheum. 44, 13–20 (2001).
17. C. Y. Fonseka, D. A. Rao, S. Raychaudhuri, Leveraging blood and tissue CD4+ T cell heterogeneity at the single cell level to identify mechanisms of disease in rheumatoid arthritis. Curr. Opin. Immunol. 49, 27–36 (2017).
18. S. C. Bendall, E. F. Simonds, P. Qiu, E.-a. D. Amir, P. O. Krutzik, R. Finck, R. V. Bruggner, R. Melamed, A. Trejo, O. I. Ornatsky, R. S. Balderas, S. K. Plevritis, K. Sachs, D. Pe’er, S. D. Tanner, G. P. Nolan, Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).
19. T. Sörensen, S. Baumgart, P. Durek, A. Grützkau, T. Häupl, immunoClust—An automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets. Cytometry 87, 603–615 (2015).
20. N. Samusik, Z. Good, M. H. Spitzer, K. L. Davis, G. P. Nolan, Automated mapping of phenotype space with single-cell data. Nat. Methods 13, 493–496 (2016).
21. P. Lin, M. Troup, J. W. K. Ho, CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol. 18, 59 (2017).
22. E. Z. Macosko, A. Basu, R. Satija, J. Nemesh, K. Shekhar, M. Goldman, I. Tirosh, A. R. Bialas, N. Kamitaki, E. M. Martersteck, J. J. Trombetta, D. A. Weitz, J. R. Sanes, A. K. Shalek, A. Regev, S. A. McCarroll, Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
23. J. H. Levine, E. F. Simonds, S. C. Bendall, K. L. Davis, E.-a. D. Amir, M. D. Tadmor, O. Litvin, H. G. Fienberg, A. Jager, E. R. Zunder, R. Finck, A. L. Gedman, I. Radtke, J. R. Downing, D. Pe’er, G. P. Nolan, Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
24. O. Ornatsky, D. Bandura, V. Baranov, M. Nitz, M. A. Winnik, S. Tanner, Highly multiparametric analysis by mass cytometry. J. Immunol. Methods 361, 1–20 (2010).
25. J. Ermann, D. A. Rao, N. C. Teslovich, M. B. Brenner, S. Raychaudhuri, Immune cell profiling to guide therapeutic decisions in rheumatic diseases. Nat. Rev. Rheumatol. 11, 541–551 (2015).
26. B. Becher, A. Schlitzer, J. Chen, F. Mair, H. R. Sumatoh, K. W. W. Teng, D. Low, C. Ruedl, P. Riccardi-Castagnoli, M. Poidinger, M. Greter, F. Ginhoux, E. W. Newell, High-dimensional analysis of the murine myeloid cell system. Nat. Immunol. 15, 1181–1189 (2014).
27. L. van der Maaten, Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014).
28. S. Van Gassen, B. Callebaut, M. J. Van Helden, B. N. Lambrecht, P. Demeester, T. Dhaene, Y. Saeys, FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636–645 (2015).
29. L. M. Weber, M. D. Robinson, Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytometry A 89, 1084–1096 (2016).
30. M. T. Wong, D. E. H. Ong, F. S. H. Lim, K. W. W. Teng, N. McGovern, S. Narayanan, W. Q. Ho, D. Cerny, H. K. K. Tan, R. Anicete, B. K. Tan, T. K. H. Lim, C. Y. Chan, P. C. Cheow, S. Y. Lee, A. M. P. Takano, E. H. Tan, J. K. C. Tam, E. C. Y. Tan, J. K. Y. Chan, K. Fink, A. Bertoletti, F. Ginhoux, M. A. Curotto de Lafaille, E. W. Newell, A high-dimensional atlas of human T cell diversity reveals tissue-specific trafficking and cytokine signatures. Immunity 45, 442–456 (2016).
31. Y. Cheng, M. T. Wong, L. van der Maaten, E. W. Newell, Categorical analysis of human T cell heterogeneity with one-dimensional soli-expression by nonlinear stochastic embedding. J. Immunol. 196, 924–932 (2016).
32. M. T. Wong, J. Chen, S. Narayanan, W. Lin, R. Anicete, H. T. K. Kiaang, M. A. Curotto de Lafaille, M. Poidinger, E. W. Newell, Mapping the diversity of follicular helper T cells in human blood and tonsils using high-dimensional mass cytometry analysis. Cell Rep. 11, 1822–1833 (2015).
33. C. Baecher-Allan, E. Wolf, D. A. Hafler, MHC class II expression identifies functionally distinct human regulatory T cells. J. Immunol. 176, 4622–4631 (2006).
34. R. V. Bruggner, B. Bodenmiller, D. L. Dill, R. J. Tibshirani, G. P. Nolan, Automated identification of stratifying signatures in cellular subpopulations. Proc. Natl. Acad. Sci. U.S.A. 111, E2770–E2777 (2014).
35. S. Oshima, D. D. Eckels, Selective expression of class II MHC isotypes by MLC-activated human T lymphocytes. Hum. Immunol. 27, 208–219 (1990).
36. D. Aletaha, V. P. K. Nell, T. Stamm, M. Uffmann, S. Pflugbeil, K. Machold, J. S. Smolen, Acute phase reactants add little to composite disease activity indices for rheumatoid arthritis: Validation of a clinical activity score. Arthritis Res. Ther. 7, R796–R806 (2005).
37. N. L. Bray, H. Pimentel, P. Melsted, L. Pachter, Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
38. G. M. Mason, K. Lowe, R. Melchiotti, R. Ellis, E. de Rinaldis, M. Peakman, S. Heck, G. Lombardi, T. I. M. Tree, Phenotypic complexity of the human regulatory T cell compartment revealed by mass cytometry. J. Immunol. 195, 2030–2037 (2015).
39. S. C. Bendall, K. L. Davis, E.-a. D. Amir, M. D. Tadmor, E. F. Simonds, T. J. Chen, D. K. Shenfeld, G. P. Nolan, D. Pe’er, Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).
40. P. B. Ferrell Jr., K. E. Diggins, H. G. Polikowsky, S. R. Mohan, A. C. Seegmiller, J. M. Irish, High-dimensional analysis of acute myeloid leukemia reveals phenotypic changes in persistent cells during induction therapy. PLOS ONE 11, e0153207 (2016).
41. M. Setty, M. D. Tadmor, S. Reich-Zeliger, O. Angel, T. M. Salame, P. Kathail, K. Choi, S. Bendall, N. Friedman, D. Pe’er, Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).
42. M. Mingueneau, S. Boudaoud, S. Haskett, T. L. Reynolds, G. Nocturne, E. C. Norton, X. Zhang, M. Constant, D. Park, W. Wang, T. Lazure, C. Le Pajolec, A. Ergun, X. Mariette, Cytometry by time-of-flight immunophenotyping identifies a blood Sjögren’s signature correlating with disease activity and glandular inflammation. J. Allergy Clin. Immunol. 137, 1809–1821.e12 (2016).
43. L. Hansmann, L. Blum, C.-H. Ju, M. Liedtke, W. H. Robinson, M. M. Davis, Mass cytometry analysis shows that a novel memory phenotype B cell is expanded in multiple myeloma. Cancer Immunol. Res. 3, 650–660 (2015).
44. D. M. Strauss-Albee, J. Fukuyama, E. C. Liang, Y. Yao, J. A. Jarrell, A. L. Drake, J. Kinuthia, R. R. Montgomery, G. John-Stewart, S. Holmes, C. A. Blish, Human NK cell repertoire diversity reflects immune experience and correlates with viral susceptibility. Sci. Transl. Med. 7, 297ra115 (2015).
45. B. Gaudillière, G. K. Fragiadakis, R. V. Bruggner, M. Nicolau, R. Finck, M. Tingle, J. Silva, E. A. Ganio, C. G. Yeh, W. J. Maloney, J. I. Huddleston, S. B. Goodman, M. M. Davis, S. C. Bendall, W. J. Fantl, M. S. Angst, G. P. Nolan, Clinical recovery from surgery correlates with single-cell immune signatures. Sci. Transl. Med. 6, 255ra131 (2014).
46. Y. Saeys, S. Van Gassen, B. N. Lambrecht, Computational flow cytometry: Helping to make sense of high-dimensional immunology data. Nat. Rev. Immunol. 16, 449–462 (2016).
47. A. Wagner, A. Regev, N. Yosef, Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016).
48. C. Chester, H. T. Maecker, Algorithmic tools for mining high-dimensional cytometry data. J. Immunol. 195, 773–779 (2015).
49. D. Y. Orlova, L. A. Herzenberg, G. Walther, Science not art: Statistically sound methods for identifying subsets in multi-dimensional flow and mass cytometry data sets. Nat. Rev. Immunol. 18, 77 (2017).
50. Y. Saeys, S. Van Gassen, B. Lambrecht, Response to Orlova et al. “Science not art: Statistically sound methods for identifying subsets in multi-dimensional flow and mass cytometry data sets”. Nat. Rev. Immunol. 18, 78 (2017).
51. X. Qiu, A. Hill, J. Packer, D. Lin, Y.-A. Ma, C. Trapnell, Single-cell mRNA quantification and differential analysis with Census. Nat. Methods 14, 309–315 (2017).
52. A. Larbi, T. Fulop, From “truly naïve” to “exhausted senescent” T cells: When markers predict functionality. Cytometry A 85, 25–35 (2014).
53. + lymphocytes contains functionally differentiated T lymphocytes that develop by persistent antigenic stimulation in vivo. Eur. J. Immunol. 22, 993–999 (1992).
54. + memory T cells define a differentiated memory population at both the functional and transcriptional levels. Immunology 113, 363–370 (2004).
55. + CTLs ex vivo. J. Immunol. 168, 5954–5958 (2002).
56. G. M. Griffiths, S. Alpert, E. Lambert, J. McGuire, I. L. Weissman, Perforin and granzyme A expression identifying cytolytic lymphocytes in rheumatoid arthritis. Proc. Natl. Acad. Sci. U.S.A. 89, 549–553 (1992).
57. T. Namekawa, U. G. Wagner, J. J. Goronzy, C. M. Weyand, Functional subsets of CD4 T cells in rheumatoid synovitis. Arthritis Rheum. 41, 2108–2116 (1998).
58. + cytotoxic T lymphocytes in patients with IgG4-related disease. J. Allergy Clin. Immunol. 138, 825–838 (2016).
59. dim, CD27 - memory T cells in the peripheral blood, synovial fluid, and synovial tissue of patients with rheumatoid arthritis. Arthritis Rheum. 39, 844–854 (1996).
60. + cytotoxic T lymphocytes identified by single-cell transcriptome analysis. Sci. Immunol. 3, eaan8664 (2018).
61. M. Stoeckius, C. Hafemeister, W. Stephenson, B. Houck-Loomis, P. K. Chattopadhyay, H. Swerdlow, R. Satija, P. Simbert, Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
62. V. M. Peterson, K. X. Zhang, N. Kumar, J. Wong, L. Li, D. C. Wilson, R. Moore, T. K. McClanahan, S. Sadekova, J. A. Klappenbach, Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).
63. Y. C. Lee, C. O. Bingham III, R. R. Edwards, W. Marder, K. Phillips, M. B. Bolster, D. J. Clauw, L. W. Moreland, B. Lu, A. Wohlfahrt, Z. Zhang, T. Neogi, Association between pain sensitization and disease activity in patients with rheumatoid arthritis: A cross-sectional study. Arthritis Care Res. 70, 197–204 (2018).
64. R. Finck, E. F. Simonds, A. Jager, S. Krishnaswamy, K. Sachs, W. Fantl, D. Pe’er, G. P. Nolan, S. C. Bendall, Normalization of mass cytometry data with bead standards. Cytometry A 83A, 483–494 (2013).
65. B. Ellis, P. Haaland, F. Hahne, N. Le Meur, N. Gopalakrishnan, J. Spidlen, M. Jiang, flowCore: Basic structures for flow cytometry data. R package version 1.46.1 (2018).
66. D. Bates, M. Mächler, B. M. Bolker, S. C. Walker, Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
67. H. Wickham, ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).
68. R. Kolde, pheatmap: Pretty Heatmaps. R package version 1.0.10 (2018).
69. H. Chen, M. C. Lau, M. T. Wong, E. W. Newell, M. Poidinger, J. Chen, Cytofkit: A bioconductor package for an integrated mass cytometry data analysis pipeline. PLOS Comput. Biol. 12, e1005112 (2016).
70. W. Luo, M. S. Freidman, K. Shedden, K. D. Hankenson, P. J. Woolf, GAGE: Generally applicable gene set enrichment for pathway analysis. BMC Bioinformatics 10, 161 (2009).
71. J. Fan, P. Kharchenko, liger: Lightweight Iterative Geneset Enrichment. R package version 0.1 (2018).
72. E. LeDell, N. Gill, S. Aiello, A. Fu, A. Candel, C. Click, T. Kraljevic, T. Nykodym, P. Aboyoun, M. Kurka, M. Malohlava, h2o: R Interface for ‘H2O’. R package version 3.20.0.8 (2018).
73. L. Scrucca, M. Fop, T. B. Murphy, A. E. Raftery, mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models. R J. 8, 289–317 (2016).