IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v15y2016i4p273-290n1.html
   My bibliography  Save this article

Bayesian state space models for dynamic genetic network construction across multiple tissues

Author

Listed:
  • Liang Yulan

    (Department of Family and Community Health, University of Maryland, Baltimore, MD 21201, USA)

  • Kelemen Arpad

    (Department of Organizational Systems and Adult Health, University of Maryland, Baltimore, MD 21201, USA)

Abstract

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Suggested Citation

  • Liang Yulan & Kelemen Arpad, 2016. "Bayesian state space models for dynamic genetic network construction across multiple tissues," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 273-290, August.
    • Handle: RePEc:bpj:sagmbi:v:15:y:2016:i:4:p:273-290:n:1
    DOI: 10.1515/sagmb-2014-0055
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/sagmb-2014-0055
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/sagmb-2014-0055?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. J. Durbin & S. J. Koopman, 2000. "Time series analysis of non‐Gaussian observations based on state space models from both classical and Bayesian perspectives," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 3-56.
    2. Tomohiro Ando, 2007. "Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models," Biometrika, Biometrika Trust, vol. 94(2), pages 443-458.
    3. Liang Yulan & Kelemen Arpad G, 2004. "Hierarchical Bayesian Neural Network for Gene Expression Temporal Patterns," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-25, September.
    4. Grzegorczyk Marco & Husmeier Dirk, 2012. "A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-62, July.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    6. Oates Chris J. & Amos Richard & Spencer Simon E.F., 2014. "Quantifying the multi-scale performance of network inference algorithms," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(5), pages 611-631, October.
    7. Riten Mitra & Peter Müller & Shoudan Liang & Lu Yue & Yuan Ji, 2013. "A Bayesian Graphical Model for ChIP-Seq Data on Histone Modifications," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 69-80, March.
    8. Ibrahim J. G. & Chen M-H. & Gray R. J., 2002. "Bayesian Models for Gene Expression With DNA Microarray Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 88-99, March.
    9. C. M. Carvalho & J. G. Scott, 2009. "Objective Bayesian model selection in Gaussian graphical models," Biometrika, Biometrika Trust, vol. 96(3), pages 497-512.
    10. Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December.
    11. Francis S. Collins & Eric D. Green & Alan E. Guttmacher & Mark S. Guyer, 2003. "A vision for the future of genomics research," Nature, Nature, vol. 422(6934), pages 835-847, April.
    12. Beatrix Jones & Mike West, 2005. "Covariance decomposition in undirected Gaussian graphical models," Biometrika, Biometrika Trust, vol. 92(4), pages 779-786, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. E. M. Conlon & B. L. Postier & B. A. Methe & K. P. Nevin & D. R. Lovley, 2009. "Hierarchical Bayesian meta-analysis models for cross-platform microarray studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(10), pages 1067-1085.
    2. HyungJun Cho & Jaewoo Kang & Jae Lee, 2009. "Empirical Bayes analysis of unreplicated microarray data," Computational Statistics, Springer, vol. 24(3), pages 393-408, August.
    3. Zellner, Arnold & Ando, Tomohiro, 2010. "A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model," Journal of Econometrics, Elsevier, vol. 159(1), pages 33-45, November.
    4. Erin M Conlon & Bradley L Postier & Barbara A Methé & Kelly P Nevin & Derek R Lovley, 2012. "A Bayesian Model for Pooling Gene Expression Studies That Incorporates Co-Regulation Information," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.
    5. Izmirlian, Grant, 2020. "Strong consistency and asymptotic normality for quantities related to the Benjamini–Hochberg false discovery rate procedure," Statistics & Probability Letters, Elsevier, vol. 160(C).
    6. Yuki Kawakubo & Tatsuya Kubokawa & Muni S. Srivastava, 2015. "A Variant of AIC Using Bayesian Marginal Likelihood," CIRJE F-Series CIRJE-F-971, CIRJE, Faculty of Economics, University of Tokyo.
    7. Stefano Grassi & Francesco Ravazzolo & Joaquin Vespignani & Giorgio Vocalelli, 2023. "Global Money Supply and Energy and Non-Energy Commodity Prices: A MS-TV-VAR Approach," CAMA Working Papers 2023-13, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Yu, Jun & Yang, Zhenlin & Zhang, Xibin, 2006. "A class of nonlinear stochastic volatility models and its implications for pricing currency options," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2218-2231, December.
    9. Tsay, Ruey S. & Ando, Tomohiro, 2012. "Bayesian panel data analysis for exploring the impact of subprime financial crisis on the US stock market," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3345-3365.
    10. Wang, Yixin & So, Mike K.P., 2016. "A Bayesian hierarchical model for spatial extremes with multiple durations," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 39-56.
    11. Alzahrani, Naif & Neal, Peter & Spencer, Simon E.F. & McKinley, Trevelyan J. & Touloupou, Panayiota, 2018. "Model selection for time series of count data," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 33-44.
    12. Chiranjit Mukherjee & Prasad Kasibhatla & Mike West, 2014. "Spatially varying SAR models and Bayesian inference for high-resolution lattice data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 473-494, June.
    13. Ji, Yuan & Tsui, Kam-Wah & Kim, KyungMann, 2006. "A two-stage empirical Bayes method for identifying differentially expressed genes," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3592-3604, August.
    14. Ruiz-Cárdenas, Ramiro & Krainski, Elias T. & Rue, Håvard, 2012. "Direct fitting of dynamic models using integrated nested Laplace approximations — INLA," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1808-1828.
    15. Filidor Vilca & Caio L. N. Azevedo & N. Balakrishnan, 2017. "Bayesian inference for sinh-normal/independent nonlinear regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 2052-2074, August.
    16. Debashis Ghosh & Arul Chinnaiyan, 2004. "Covariate adjustment in the analysis of microarray data from clinical studies," The University of Michigan Department of Biostatistics Working Paper Series 1030, Berkeley Electronic Press.
    17. Elisa C. J. Maria & Isabel Salazar & Luis Sanz & Miguel A. Gómez-Villegas, 2020. "Using Copula to Model Dependence When Testing Multiple Hypotheses in DNA Microarray Experiments: A Bayesian Approximation," Mathematics, MDPI, vol. 8(9), pages 1-22, September.
    18. Yuki Kawakubo & Tatsuya Kubokawa & Muni S. Srivastava, 2018. "A Variant of AIC Based on the Bayesian Marginal Likelihood," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 60-84, May.
    19. David I. Ohlssen & Linda D. Sharples & David J. Spiegelhalter, 2007. "A hierarchical modelling framework for identifying unusual performance in health care providers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 865-890, October.
    20. Ando, Tomohiro, 2009. "Bayesian portfolio selection using a multifactor model," International Journal of Forecasting, Elsevier, vol. 25(3), pages 550-566, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sagmbi:v:15:y:2016:i:4:p:273-290:n:1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.