# American Institute of Mathematical Sciences

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

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

 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.
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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Lenhard, JASPAR: an open-access database for eukaryotic transcription factor binding profiles,, Nucleic Acids Research, 32 (2004). Google Scholar [48] M. Scutari, Learning Bayesian Networks with the bnlearn R Package,, Journal of Statistical Software, 35 (2010), 1. Google Scholar [49] A. Shojaie and G. Michailidis, Analysis of gene sets based on the underlying regulatory network,, Journal of Computational Biology, 16 (2009), 407. doi: 10.1089/cmb.2008.0081. Google Scholar [50] A. Shojaie and G. Michailidis, Discovering graphical Granger causality using the truncating lasso penalty,, Bioinformatics, 26 (2010). doi: 10.1093/bioinformatics/btq377. Google Scholar [51] A. Shojaie, A. Jauhiainen, M. Kallitsis and G. Michailidis, Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles,, PLoS One, 9 (2014). doi: 10.1371/journal.pone.0082393. Google Scholar [52] R. 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##### 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] K. Basso, A. A. Margolin, G. Stolovitzky, U. Klein, R. D. Favera and A. Califano, Reverse engineering of regulatory networks in human B cells,, Nature Genetics, 37 (2005), 382. doi: 10.1038/ng1532. Google Scholar [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 [5] S. Christley, Q. Nie and X. Xie, Incorporating existing network information into gene network inference,, PLoS One, 4 (2009). doi: 10.1371/journal.pone.0006799. Google Scholar [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] P. D'haeseleer, X. Wen, S. Fuhrman and R. Somogyi, Linear modeling of mRNA expression levels during CNS development and injury,, Pacific Symposium on Biocomputing, 4 (1999), 41. doi: 10.1142/9789814447300_0005. Google Scholar [9] C. Ding and H. Peng, Minimum redundancy feature selection from microarray gene expression data,, Bioinformatics Conference, (2003), 523. doi: 10.1109/CSB.2003.1227396. Google Scholar [10] , DREAM4 In Silico Network Challenge, website,, , (). Google Scholar [11] Q. Duan, C. Flynn, M. Niepel, M. Hafner, J. M. Muhlich, N. F. Fernandez, A. D. Rouillard, C. M. Tan, E. Y. Chen, T. R. Golub, P. K. Sorger, A. Subramanian and A. Ma'ayan, LINCS Canvas Browser: Interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures,, Nucleic Acids Research, 42 (2014). doi: 10.1093/nar/gku476. Google Scholar [12] S. A. Dunbar, Applications of Luminex® $xMAP^{TM}$ technology for rapid, high-throughput multiplexed nucleic acid detection,, Clinica Chimica Acta, 363 (2006), 71. Google Scholar [13] J. J. Faith, B. Hayete, J. T. Thaden, I. Mogno, J. Wierzbowski, G. Cottarel, S. Kasif, J. J. Collins and T. S. Gardner, Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles,, PLoS Biol, 5 (2007). doi: 10.1371/journal.pbio.0050008. Google Scholar [14] N. Friedman, M. Linial, I. Nahman and D. Pe'er, Using bayesian networks to analyze expression data,, RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology, (2000), 127. doi: 10.1145/332306.332355. Google Scholar [15] H. Fröhlich, M. Fellmann, H. Sueltmann, A. Poustka and T.Beissbarth, Large scale statistical inference of signaling pathways from RNAi and microarray data,, BMC Bioinformatics, 8 (2007). Google Scholar [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] M. Gustafsson, M. Hörnquist, J. Lundström, J. Björkegren and J. Tegnér, Reverse engineering of gene networks with LASSO and nonlinear basis functions,, Annals of the New York Academy of Sciences, 1158 (2009), 265. doi: 10.1111/j.1749-6632.2008.03764.x. Google Scholar [18] M. Hecker, S. Lambeck, S. Toepfer, E. Someren and R. 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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] M. Sanchez-Castillo, I. Tienda-Luna, D. Blanco, M. C. Carrion-Perez and Y. Huang, Bayesian sparse factor model for transcriptional regulatory networks inference,, Signal Processing Conference (EUSIPCO), (2013), 1. Google Scholar [47] A. Sandelin, W. Alkema, P. Engström, W. W. Wasserman and B. 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