Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures

Cell Systems

Volumn 5, Issue 3, 2017, Pages 251-267.e3

  Chan, Thalia E ^a   Stumpf, Michael P H ^a   Babtie, Ann C ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

gene regulation; mutual information; network reconstruction; single cell PCR; single cell RNA seq

Indexed keywords

MESSENGER RNA; TRANSCRIPTOME;

ALGORITHM; ARTICLE; COMPUTER LANGUAGE; COMPUTER MODEL; DATA ANALYSIS SOFTWARE; DATA SYNTHESIS; GENE EXPRESSION; GENE INTERACTION; GENE REGULATORY NETWORK; INFORMATION SCIENCE; MACHINE LEARNING; MAXIMUM LIKELIHOOD METHOD; MULTIVARIATE ANALYSIS; NONHUMAN; PARTIAL INFORMATION DECOMPOSITION; PRIORITY JOURNAL; PROBABILITY; RECEIVER OPERATING CHARACTERISTIC; SIMULATION; SINGLE CELL ANALYSIS; STATISTICAL CONCEPTS; SYSTEMS BIOLOGY; TRANSCRIPTOMICS; ANIMAL; BIOLOGY; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; HUMAN; PROCEDURES; SOFTWARE;

ALGORITHMS; ANIMALS; COMPUTATIONAL BIOLOGY; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORKS; HUMANS; MULTIVARIATE ANALYSIS; SINGLE-CELL ANALYSIS; SOFTWARE; TRANSCRIPTOME;

EID: 85031092991     PISSN: 24054712     EISSN: 24054720     Source Type: Journal    
DOI: 10.1016/j.cels.2017.08.014     Document Type: Article

References (99)

1. Agresti, A., Hitchcock, D.B., Bayesian inference for categorical data analysis. Stat. Methods Appt. 14 (2005), 297–330.
2. Bacher, R., Kendziorski, C., Design and computational analysis of single-cell RNA- sequencing experiments. Genome Biol. 17 (2016), 1–14.
3. Beal, M.J., Falciani, F., Ghahramani, Z., Rangel, C., Wild, D.L., A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics 21 (2005), 349–356.
4. Bendall, S.C., Davis, K.L., Amir, E.A.D., Tadmor, M.D., Simonds, E.F., Chen, T.J., Shenfeld, D.K., Nolan, G.P., Pe'er, D., Single-cell Trajectory detection Uncovers progression and regulatory coordination in human B cell development. Cell 157 (2014), 714–725.
5. Bezanson, J., Edelman, A., Karpinski, S., Shah, V.B., Julia: a fresh approach to numerical computing. arXiv, 2014 1411.1607.
6. Bonneau, R., Reiss, D.J., Shannon, P., Facciotti, M., Hood, L., Baliga, N.S., Thorsson, V., The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol., 7, 2006, R36.
7. Boyle, E.A., Li, Y.I., Pritchard, J.K., An expanded view of complex traits: from polygenic to omnigenic. Cell 169 (2017), 1177–1186.
8. Brennecke, P., Anders, S., Kim, J.K., Kolodziejczyk, A.A., Zhang, X., Proserpio, V., Baying, B., Benes, V., Teichmann, S.A., Marioni, J.C., et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10 (2013), 1093–1095.
9. Buettner, F., Natarajan, K.N., Casale, F.P., Proserpio, V., Scialdone, A., Theis, F.J., Teichmann, S.A., Marioni, J.C., Stegle, O., Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33 (2015), 155–160.
10. Butte, A.J., Tamayo, P., Slonim, D., Golub, T.R., Kohane, I.S., Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc. Natl. Acad. Sci. USA 97 (2000), 12182–12186.
11. Clevers, H., Rafelski, S., Elowitz, M.B., Klein, A.M., Shendure, J., Trapnell, C., Lein, E., Lundberg, E., Uhlen, M., Martinez Arias, A., et al. What is your conceptual definition of “cell type” in the context of a mature organism?. Cell Syst. 4 (2017), 255–259.
12. Cover, T.M., Thomas, J.A., Elements of Information Theory. 2012, John Wiley ISBN 1118585771.
13. Davis, J., Goadrich, M., The relationship between precision-recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning. 2006, ACM.
14. De Smet, R., Marchal, K., Advantages and limitations of current network inference methods. Nat. Rev. Microbiol. 8 (2010), 717–729.
15. Deweese, M.R., Meister, M., How to measure the information gained from one symbol. Network 10 (1999), 325–340.
16. Faith, J.J., Hayete, B., Thaden, J.T., Mogno, I., Wierzbowski, J., Cottarel, G., Kasif, S., Collins, J.J., Gardner, T.S., Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 5 (2007), e8–e13.
17. Filippi, S., Holmes, C.C., A Bayesian nonparametric approach to testing for dependence between random variables. Bayesian Anal., 2017, 10.1214/16-BA1027.
18. Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A.K., Slichter, C.K., Miller, H.W., McElrath, M.J., Prlic, M., et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol., 16, 2015, 278.
19. Fu, A.Q., Pachter, L., Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Stat. Appl. Genet. Mol. Biol. 15 (2016), 447–471.
20. Gillespie, D., Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81 (1977), 2340–2361.
21. Göttgens, B., Regulatory network control of blood stem cells. Blood 125 (2015), 2614–2620.
22. Gouti, M., Metzis, V., Briscoe, J., The route to spinal cord cell types: a tale of signals and switches. Trends Genet. 31 (2015), 282–289.
23. Grün, D., van Oudenaarden, A., Design and analysis of single-cell sequencing experiments. Cell 163 (2015), 799–810.
24. Guo, G., Huss, M., Tong, G.Q., Wang, C., Sun, L.L., Clarke, N.D., Robson, P., Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. Dev. Cell 18 (2010), 675–685.
25. Haghverdi, L., Büttner, M., Wolf, F.A., Buettner, F., Theis, F.J., Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods, 13, 2016, 845.
26. Harrington, H.A., Azogui, H.H., Yahalom-Ronen, Y., Plotnikov, A., Stumpf, M.P.H., Stumpf, M.P.H., Nuclear to cytoplasmic shuttling of ERK promotes differentiation of muscle stem/progenitor cells. Development 141 (2014), 2611–2620.
27. Hausser, J., Strimmer, K., Entropy inference and the James-Stein estimator, with application to nonlinear Gene Association Networks. J. Machine Learn. Res. 10 (2009), 1469–1484.
28. Hill, S.M., Heiser, L.M., Cokelaer, T., Unger, M., Nesser, N.K., Carlin, D.E., Zhang, Y., Sokolov, A., Paull, E.O., Wong, C.K., et al. Inferring causal molecular networks: empirical assessment through a community-based effort. Nat. Methods 13 (2016), 310–318.
29. Hill, S.M., Lu, Y., Molina, J., Heiser, L.M., Spellman, P.T., Speed, T.P., Gray, J.W., Mills, G.B., Mukherjee, S., Bayesian inference of signaling network topology in a cancer cell line. Bioinformatics 28 (2012), 2804–2810.
30. Huang, S., Cell lineage determination in state space: a systems view brings flexibility to dogmatic canonical rules. PLoS Biol., 8, 2010, e1000380.
31. Huang, X., Zi, Z., Inferring cellular regulatory networks with Bayesian model averaging for linear regression (BMALR). Mol. Biosyst. 10 (2014), 2023–2030.
32. Ingram, P.J., Stumpf, M.P.H., Stark, J., Network motifs: structure does not determine function. BMC Genomics, 7, 2006, 108.
33. Kharchenko, P.V., Silberstein, L., Scadden, D.T., Bayesian approach to single-cell differential expression analysis. Nat. Methods 11 (2014), 740–742.
34. Kinney, J., Atwal, G.S., Equitability, mutual information, and the maximal information coefficient. Proc. Natl. Acad. Sci. USA 111 (2014), 3354–3359.
35. Klein, A.M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D.A., Kirschner, M.W., Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161 (2015), 1187–1201.
36. Kolodziejczyk, A.A., Kim, J.K., Tsang, J.C.H., Ilicic, T., Henriksson, J., Natarajan, K.N., Tuck, A.C., Gao, X., Bühler, M., Liu, P., et al. Single cell RNA-sequencing of pluripotent states unlocks modular transcriptional variation. Cell Stem Cell 17 (2015), 471–485.
37. Korthauer, K.D., Chu, L.F., Newton, M.A., Li, Y., Thomson, J., Stewart, R., Kendziorski, C., A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biol., 17, 2016, 222.
38. Kraskov, A., Stögbauer, H., Grassberger, P., Estimating mutual information. Phys. Rev. E, 69, 2004, 066138.
39. Lebre, S., Becq, J., Devaux, F., Stumpf, M.P.H., Lelandais, G., Statistical inference of the time-varying structure of gene-regulation networks. BMC Syst. Biol., 4, 2010, 130.
40. Liang, K.C., Wang, X., Gene Regulatory Network reconstruction using conditional mutual information. EURASIP J. Bioinform Syst. Biol., 2008, 2008, 253894.
41. Liu, S., Trapnell, C., Single-cell transcriptome sequencing: recent advances and remaining challenges. F1000Res., 5, 2016, 182.
42. Macosko, E.Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A.R., Kamitaki, N., Martersteck, E.M., et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161 (2015), 1202–1214.
43. Madar, A., Greenfield, A., Vanden-Eijnden, E., Bonneau, R., DREAM3: network inference using dynamic context likelihood of relatedness and the inferelator. PLoS One 5 (2010), e9803–e9813.
44. Marbach, D., Costello, J.C., Küffner, R., Vega, N.M., Prill, R.J., Camacho, D.M., Allison, K.R. DREAM5 Consortium, Kellis, M., Collins, J.J., et al. Wisdom of crowds for robust gene network inference. Nat. Methods 9 (2012), 796–804.
45. Marbach, D., Prill, R.J., Schaffter, T., Mattiussi, C., Floreano, D., Stolovitzky, G., Revealing strengths and weaknesses of methods for gene network inference. Proc. Natl. Acad. Sci. USA 107 (2010), 6286–6291.
46. Margolin, A.A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Favera, R., Califano, A., ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7:Suppl 1 (2006), S7–S15.
47. Margolin, A.A., Wang, K., Lim, W.K., Kustagi, M., Nemenman, I., Califano, A., Reverse engineering cellular networks. Nat. Protoc. 1 (2006), 662–671.
48. Mc Mahon, S.S., Lenive, O., Filippi, S., Stumpf, M.P.H., Information processing by simple molecular motifs and susceptibility to noise. J. R. Soc. Interf., 12, 2015, 20150597.
49. Mc Mahon, S.S., Sim, A., Johnson, R., Liepe, J., Stumpf, M.P.H., Information theory and signal transduction systems: from molecular information processing to network inference. Semin. Cell Dev. Biol. 35 (2014), 98–108.
50. McDavid, A., Dennis, L., Danaher, P., Finak, G., Krouse, M., Wang, A., Webster, P., Beechem, J., Gottardo, R., Modeling bBi-modality improves characterization of cell cycle on gene expression in single cells. PLoS Comput. Biol., 10, 2014, e1003696.
51. McGill, W.J., Multivariate Information Transmission, 19, 1954, Psychometrika, 97–116 ISSN 1860-0980.
52. Meyer, P.E., Kontos, K., Lafitte, F., Bontempi, G., Information-theoretic inference of large transcriptional regulatory Networks. EURASIP J. Bioinform Syst. Biol. 2007 (2007), 1–9.
53. Meyer, P.E., Lafitte, F., Bontempi, G., minet: a R/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinformatics, 9, 2008, 461.
54. Moignard, V., Macaulay, I.C., Swiers, G., Buettner, F., Schiitte, J., Calero-Nieto, F.J., Kinston, S., Joshi, A., Hannah, R., Theis, F.J., et al. Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis. Nat. Cell Biol. 15 (2013), 363–372.
55. Moignard, V., Woodhouse, S., Haghverdi, L., Lilly, A.J., Tanaka, Y., Wilkinson, A.C., Buettner, F., Macaulay, I.C., Jawaid, W., Diamanti, E., et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. 33 (2015), 269–276.
56. Moris, N., Pina, C., Martinez Arias, A., Transition states and cell fate decisions in epigenetic landscapes. Nat. Rev. Genet., 17, 2016, 693.
57. Mosteller, F., Tukey, J.W., Data Analysis and Regression: A Second Course in Statistics. 1977, Addison-Wesley.
58. Murphy, K.P., Machine Learning. A Probabilistic Perspective. 2012, MIT Press ISBN 0262018020.
59. Oates, C.J., Amos, R., Spencer, S.E.F., Quantifying the multi-scale performance of network inference algorithms. Stat. Appl. Genet. Mol. Biol. 13 (2014), 611–631.
60. Oates, C.J., Mukherjee, S., Network inference and biological dynamics. Ann. Appl. Stat. 6 (2012), 1209–1235.
61. Ocone, A., Haghverdi, L., Mueller, N.S., Theis, F.J., Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data. Bioinformatics 31 (2015), i89–i96.
62. Olsen, C., Meyer, P.E., Bontempi, G., On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information. EURASIP J. Bioinform Syst. Biol. 2009 (2009), 1–9.
63. Opgen-Rhein, R., Strimmer, K., From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst. Biol., 1, 2007, 37.
64. Paninski, L., Estimation of entropy and mutual information. Neural Comput. 15 (2003), 1191–1253.
65. Penfold, C.A., Shifaz, A., Brown, P.E., Nicholson, A., Wild, D.L., CSI: a nonpara- metric Bayesian approach to network inference from multiple perturbed time series gene expression data. Stat. Appl. Genet. Mol. Biol. 14 (2015), 307–310.
66. Penfold, C.A., Wild, D.L., How to infer gene networks from expression profiles, revisited. Interf. Focus 1 (2011), 857–870.
67. Pierson, E., Yau, C., ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol., 16, 2015, 241.
68. Pina, C., Teles, J., Fugazza, C., May, G., Wang, D., Guo, Y., Soneji, S., Brown, J., Eden, P., Ohlsson, M., et al. Single-cell network analysis identifies DDIT3 as a nodal lineage regulator in hematopoiesis. Cell Rep. 11 (2015), 1503–1510.
69. Psaila, B., Barkas, N., Iskander, D., Roy, A., Anderson, S., Ashley, N., Caputo, V.S., Lichtenberg, J., Loaiza, S., Bodine, D.M., et al. Single-cell profiling of human megakaryocyte-erythroid progenitors identifies distinct megakaryocyte and erythroid differentiation pathways. Genome Biol., 17, 2016, 1.
70. Reid, J.E., Wernisch, L., Pseudotime estimation: deconfounding single cell time series. Bioinformatics 32 (2016), 2973–2980.
71. Rue, P., Martinez Arias, A., Cell dynamics and gene expression control in tissue homeostasis and development. Mol. Syst. Biol., 11, 2015, 792.
72. Scargle, J.D., Norris, J.P., Jackson, B., Chiang, J., Studies in astronomical time series analysis. VI. Bayesian block representations. Astrophys. J., 764, 2013, 167.
73. Schafer, J., Strimmer, K., An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21 (2005), 754–764.
74. Schaffter, T., Marbach, D., Floreano, D., GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27 (2011), 2263–2270.
75. Scialdone, A., Natarajan, K.N., Saraiva, L.R., Proserpio, V., Teichmann, S.A., Stegle, O., Marioni, J.C., Buettner, F., Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85 (2015), 54–61.
76. Setty, M., Tadmor, M.D., Reich-Zeliger, S., Angel, O., Salame, T.M., Kathail, P., Choi, K., Bendall, S., Friedman, N., Pe'er, D., Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34 (2016), 637–645.
77. Siegenthaler, C., Gunawan, R., Assessment of network inference methods: how to cope with an underdetermined problem. PLoS One, 9, 2014, e90481.
78. Simoes, R.d.M., Emmert-Streib, F., Influence of statistical estimators of mutual information and data heterogeneity on the inference of gene regulatory networks. PLoS One, 6, 2011, e29279.
79. Stegle, O., Teichmann, S.A., Marioni, J.C., Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16 (2015), 133–145.
80. Steuer, R., Kurths, J., Daub, C.O., Weise, J., Selbig, J., The mutual information: detecting and evaluating dependencies between variables. Bioinformatics 18:Suppl 2 (2002), S231–S240.
81. Stumpf, P.S., Smith, R.C.G., Lenz, M., Schuppert, A., Müller, F.J., Babtie, A., Chan, T.E., Stumpf, M.P.H., Please, C.P., Howison, S.D., et al. Stem cell differentiation is a stochastic process with memory. Cell Syst. 5 (2017), 268–282 this issue.
82. Thorne, T.W., Fratta, P., Hanna, M.G., Cortese, A., Plagnol, V., Fisher, E.M., Stumpf, M.P.H., Graphical modelling of molecular networks underlying sporadic inclusion body myositis. Mol. Biosyst. 9 (2013), 1736–1742.
83. Thorne, T.W., Stumpf, M.P.H., Inference of temporally varying Bayesian networks. Bioinformatics 28 (2012), 3298–3305.
84. Timme, N., Alford, W., Flecker, B., Beggs, J.M., Synergy, redundancy, and multivariate information measures: an experimentalist's perspective. J. Comput. Neurosci. 36 (2014), 119–140.
85. Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N.J., Livak, K.J., Mikkelsen, T.S., Rinn, J.L., The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol., 32, 2014, 381.
86. Uda, S., Saito, T.H., Kudo, T., Kokaji, T., Tsuchiya, T., Kubota, H., Komori, Y., Ozaki, Y.i., Kuroda, S., Robustness and compensation of information transmission of signaling pathways. Science 341 (2013), 558–561.
87. Vallejos, C., Beyond comparisons of means: understanding changes in gene expression at the single-cell level. Genome Biol., 17, 2016, 70.
88. van Dijk, D., Nainys, J., Sharma, R., Kathail, P., Carr, A.J., Moon, K.R., Mazutis, L., Wolf, G., Krishnaswamy, S., Pe'er, D., MAGIC: a diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data. bioRxiv, 2017, 1–61, 10.1101/111591.
89. Vanderplas, J., Connolly, A., IveziC, Z., Gray, A., Introduction to astroML: Machine Learning for Astrophysics. In Conference on Intelligent Data Understanding (CIDU). 2012, IEEE, 47–54, 10.1109/CIDU.2012.6382200.
90. Villaverde, A., Ross, J., Banga, J., Reverse engineering cellular networks with information theoretic methods. Cells 2 (2013), 306–329.
91. Villaverde, A.F., Banga, J.R., Reverse engineering and identification in systems biology: strategies, perspectives and challenges. J. R. Soc. Interf., 11, 2013, 20130505.
92. Villaverde, A.F., Ross, J., Moran, F., Banga, J.R., MIDER: network inference with mutual information distance and entropy reduction. PLoS One, 9, 2014, e96732.
93. Vinciotti, V., Augugliaro, L., Abbruzzo, A., Wit, E.C., Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks. Stat. Appl. Genet. Mol. Biol. 15 (2016), 193–212.
94. Watkinson, J., Liang, K.C., Wang, X., Zheng, T., Anastassiou, D., Inference of regulatory gene interactions from expression data using three-way mutual information. Ann. N. Y. Acad. Sci. 1158 (2009), 302–313.
95. Williams, P.L., Beer, R.D., Nonnegative decomposition of multivariate information. arXiv, 2010 1004.2515v1.
96. Young, W.C., Raftery, A.E., Yeung, K.Y., Fast Bayesian inference for gene regulatory networks using ScanBMA. BMC Syst. Biol., 8, 2014, 47.
97. Zhang, Z., Zheng, L., A mutual information estimator with exponentially decaying bias. Stat. Appl. Genet. Mol. Biol. 14 (2015), 243–252.
98. Zhao, J., Zhou, Y., Zhang, X., Chen, L., Part mutual information for quantifying direct associations in networks. Proc. Natl. Acad. Sci. USA 113 (2016), 5130–5135.
99. Zoppoli, P., Morganella, S., Ceccarelli, M., TimeDelay-ARACNE: reverse engineering of gene networks from time-course data by an information theoretic approach. BMC Bioinformatics, 11, 2010, 154.