American Institute of Mathematical Sciences

Advanced
2016, 13(6): 1241-1251. doi: 10.3934/mbe.2016041

A posterior probability approach for gene regulatory network inference in genetic perturbation data

William Chad Young 1, , Adrian E. Raftery 1, and  Ka Yee Yeung 2,

1. 

University of Washington, Department of Statistics, Box 354322, Seattle, WA 98195-4322, United States, United States
2. 

University of Washington, Institute of Technology, Box 358426, 1900 Commerce Street, Tacoma, WA 98402-3100, United States

Received  September 2015 Revised  May 2016 Published  August 2016

Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. We show that the method is able to find previously identified edges from TRANSFAC and JASPAR and discuss the merits and limitations of this approach.
Keywords: statistics, gene regulatory network, statistical computation., Bayesian analysis.
Mathematics Subject Classification: Primary: 62P10, 92D10; Secondary: 92C4.
Citation: William Chad Young, Adrian E. Raftery, Ka Yee Yeung. A posterior probability approach for gene regulatory network inference in genetic perturbation data. Mathematical Biosciences & Engineering, 2016, 13 (6) : 1241-1251. doi: 10.3934/mbe.2016041
References:
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show all references

References:
[1]

M. Bansal, G. Della Gatta and D. Di Bernardo, Inference of gene regulatory networks and compound mode of action from time course gene expression profiles,, Bioinformatics, 22 (2006), 815.  doi: 10.1093/bioinformatics/btl003.  Google Scholar

[2]

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[3]

P. Bühlmann, M. Kalisch and L. Meier, High-dimensional statistics with a view towards applications in biology,, Annual Review of Statistics and Its Application, 1 (2014), 255.   Google Scholar

[4]

E. Y. Chen, C. M. Tan, Y. Kou, Q. Duan, Z. Wang, G. V. Meirelles, N. R. Clark and A. Ma'ayan, Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool,, BMC Bioinformatics, 14 (2013), 128.  doi: 10.1186/1471-2105-14-128.  Google Scholar

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[6]

M. Clyde and E. I. George, Model uncertainty,, Statistical Science, 19 (2004), 81.  doi: 10.1214/088342304000000035.  Google Scholar

[7]

A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm,, Journal of the Royal Statistical Society. Series B (Methodological), 39 (1977), 1.   Google Scholar

[8]

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[10]

, DREAM4 In Silico Network Challenge, website,, , ().   Google Scholar

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[12]

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[13]

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[14]

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[15]

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[16]

N. Guelzim, S. Bottani, P. Bourgine and F. Kèpés, Topological and causal structure of the yeast transcriptional regulatory network,, Nature Genetics, 31 (2002), 60.  doi: 10.1038/ng873.  Google Scholar

[17]

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[18]

M. Hecker, S. Lambeck, S. Toepfer, E. Someren and R. Guthke, Gene regulatory network inference: Data integration in dynamic models, A review,, Biosystems, 96 (2009), 86.  doi: 10.1016/j.biosystems.2008.12.004.  Google Scholar

[19]

J. A. Hoeting, D. Madigan, A. E. Raftery and C. T. Volinsky, Bayesian model averaging: A tutorial,, Statistical Science, 14 (1999), 382.  doi: 10.1214/ss/1009212519.  Google Scholar

[20]

R. E. Kass and A. E. Raftery, Bayes factors,, Journal of the American Statistical Association, 90 (1995), 773.  doi: 10.1080/01621459.1995.10476572.  Google Scholar

[21]

S. Y. Kim, S. Imoto and S. Miyano, Inferring gene networks from time series microarray data using dynamic Bayesian networks,, Briefings in Bioinformatics, 4 (2003), 228.  doi: 10.1093/bib/4.3.228.  Google Scholar

[22]

S. Y. Kim, S. Imoto and S. Miyano, Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data,, Computational Methods in Systems Biology, 2602 (2003), 104.  doi: 10.1007/3-540-36481-1_9.  Google Scholar

[23]

S. Klamt, R. J. Flassig and K. Sundmacher, TRANSWESD: inferring cellular networks with transitive reduction,, Bioinformatics, 26 (2010), 2160.  doi: 10.1093/bioinformatics/btq342.  Google Scholar

[24]

S. Lèbre, J. Becq, F. Devaus, M. Stumpf and G. Lelandais, Statistical inference of the time-varying structure of gene-regulation networks,, BMC Systems Biology, 4 (2010).   Google Scholar

[25]

W. Lee and W. Tzou, Computational methods for discovering gene networks from expression data,, Briefings in Bioinformatics, 10 (2009), 408.  doi: 10.1093/bib/bbp028.  Google Scholar

[26]

J. Li and R. Tibshirani, Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data,, Statistical Methods in Medical Research, 22 (2013), 519.  doi: 10.1177/0962280211428386.  Google Scholar

[27]

Z. Li, P. Li, A. Krishnan and J. Liu, Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic Bayesian network analysis,, Bioinformatics, 27 (2011), 2686.  doi: 10.1093/bioinformatics/btr454.  Google Scholar

[28]

, Library of Integrated Network-based Cellular Signatures (LINCS), website,, , ().   Google Scholar

[29]

K. Lo, A. E. Raftery, K. M. Dombeck, J. Zhu, E. E. Schadt, R. E. Bumgarner and K. Y. Yeung, Integrating external biological knowledge in the construction of regulatory networks from time-series expression data,, BMC Systems Biology, 6 (2012).  doi: 10.1186/1752-0509-6-101.  Google Scholar

[30]

F. M. Lopes, E. A. de Oliveira and R. M. Cesar, Inference of gene regulatory networks from time series by Tsallis entropy,, BMC Systems Biology, 5 (2011).  doi: 10.1186/1752-0509-5-61.  Google Scholar

[31]

M. J. McGeachie, H. Chang and S. T. Weiss, CGBayesNets: Conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data,, PLoS Computational Biology, 10 (2014).  doi: 10.1371/journal.pcbi.1003676.  Google Scholar

[32]

D. Marbach, T. Schaffter, C. Mattiussi and D. Floreano, Generating realistic in silico gene networks for performance assessment of reverse engineering methods,, Journal of Computational Biology, 16 (2009), 229.   Google Scholar

[33]

D. Marbach, R. J. Prill, T. Schaffter, C. Mattiussi, D. Floreano and G. Stolovitzky, Revealing strengths and weaknesses of methods for gene network inference,, Proceedings of the National Academy of Sciences, 107 (2010), 6286.  doi: 10.1073/pnas.0913357107.  Google Scholar

[34]

A. A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky, R. D. Favera and A. Califano, ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context,, BMC Bioinformatics, 7 (2006).  doi: 10.1186/1471-2105-7-S1-S7.  Google Scholar

[35]

F. Markowetz and R. Spang, Inferring cellular networks: A review,, BMC Bioinformatics, 8 (2007).  doi: 10.1186/1471-2105-8-S6-S5.  Google Scholar

[36]

P. Menéndez, Y. Kourmpetis, C. J. ter Braak and F. A. van Eeuwijk, Gene regulatory networks from multifactorial perturbations using Graphical Lasso: Application to the DREAM4 challenge,, PLoS One, 5 (2010).   Google Scholar

[37]

P. E. Meyer, K. Kontos, F. Lafitte and G. Bontempi, Information-theoretic inference of large transcriptional regulatory networks,, EURASIP Journal on Bioinformatics and Systems Biology, 2007 (2007).  doi: 10.1155/2007/79879.  Google Scholar

[38]

P. E. Meyer, F. Lafitte and G. Bontempi, minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information,, BMC Bioinformatics, 9 (2008).  doi: 10.1186/1471-2105-9-461.  Google Scholar

[39]

G. Michailidis and F. d'Alché-Buc, Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues,, Mathematical Biosciences, 246 (2013), 326.  doi: 10.1016/j.mbs.2013.10.003.  Google Scholar

[40]

K. Murphy and S. Mian, Modelling Gene Expression Data Using Dynamic Bayesian Networks,, Vol. 104. Technical report, (1999).   Google Scholar

[41]

A. Pinna, N. Soranzo and A. De La Fuente, From knockouts to networks: Establishing direct cause-effect relationships through graph analysis,, PLoS One, 5 (2010).  doi: 10.1371/journal.pone.0012912.  Google Scholar

[42]

A. E. Raftery, D. Madigan and J. A. Hoeting, Bayesian model averaging for linear regression models,, Journal of the American Statistical Association, 92 (1997), 179.  doi: 10.1080/01621459.1997.10473615.  Google Scholar

[43]

A. E. Raftery, Bayes factors and BIC,, Sociological Methods & Research, 27 (1999), 411.   Google Scholar

[44]

S. Rogers and M. Girolami, A Bayesian regression approach to the inference of regulatory networks from gene expression data,, Bioinformatics, 21 (2005), 3131.  doi: 10.1093/bioinformatics/bti487.  Google Scholar

[45]

F. H. M. Salleh, M. A. Arif, S. Zainudin and M. Firdaus-Raih, Reconstructing gene regulatory networks from knockout data using Gaussian Noise Model and Pearson Correlation Coefficient,, Computational Biology and Chemistry, 59 (2015), 3.   Google Scholar

[46]

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