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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

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1544-6115
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In This Section
Volume 14, Issue 3 (Jun 2015)

Issues

CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data

Christopher A. Penfold
  • Systems Biology Centre, University of Warwick, Coventry, UK, CV4 7AL
/ Ahmed Shifaz
  • Faculty of Information Technology, Monash University, VIC, 3800, Australia
/ Paul E. Brown
  • Systems Biology Centre, University of Warwick, Coventry, UK, CV4 7AL
/ Ann Nicholson
  • Faculty of Information Technology, Monash University, VIC, 3800, Australia
/ David L. Wild
  • Corresponding author
  • Systems Biology Centre, University of Warwick, Coventry, UK, CV4 7AL
  • Email:
Published Online: 2015-05-30 | DOI: https://doi.org/10.1515/sagmb-2014-0082

Abstract

Here we introduce the causal structure identification (CSI) package, a Gaussian process based approach to inferring gene regulatory networks (GRNs) from multiple time series data. The standard CSI approach infers a single GRN via joint learning from multiple time series datasets; the hierarchical approach (HCSI) infers a separate GRN for each dataset, albeit with the networks constrained to favor similar structures, allowing for the identification of context specific networks. The software is implemented in MATLAB and includes a graphical user interface (GUI) for user friendly inference. Finally the GUI can be connected to high performance computer clusters to facilitate analysis of large genomic datasets.

Keywords: Bayesian; Gaussian process; gene regulatory networks

References

  • Greenfield, A., A. Madar, H. Ostrer and R. Bonneau (2010): “DREAM4: combining genetic and dynamic information to identify biological networks and dynamical models,” PLoS One, 5, e13397.

  • Hickman, R., C. Hill, C. A. Penfold, E. Breeze, L. Bowden, J. Moore, P. Zhang, A. Jackson, E. Cooke, F. Bewicke-Copley, A. Mead, J. Beynon, D. L. Wild, K. Denby, S. Ott and V. Buchanan-Wollaston (2013): “A local regulatory network around three NAC transcription factors in stress responses and senescence in Arabidopsis leaves,” Plant J., 75, 26–39. [Crossref] [PubMed]

  • Kent, N., S. Adams, A. Moorhouse and K. Paszkiewicz (2011): “Chromatin particle spectrum analysis: a method for comparative chromatin structure analysis using paired-end mode next-generation dna sequencing,” Nucleic Acid. Res., 39, e26.

  • Klemm, S. L. (2008): Causal structure identification in nonlinear dynamical systems, MPhil thesis, Department of Engineering, University of Cambridge, UK.

  • Penfold, C. A. and D. L. Wild (2011): “How to infer gene networks from expression profiles, revisited,” J. R. Soc. Interface. Focus, 1, 857–870.

  • Penfold, C. A., V. Buchanan-Wollaston, K. Denby and D. L. Wild (2012): “Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks,” Bioinformatics, 28, i233–i241. [Web of Science]

  • Penfold, C, Millar, J, and Wild, D (2015). Inferring orthologous gene regulatory networks using interspecies data fusion. Doi 10.1093/bioinformatics/btv267. [Crossref]

  • Prill, R. J., D. Marbach, J. Saez-Rodriguez, P. K. Sorger, L. G. Alexopoulos, X. Xue, N. D. Clarke, G. Altan-Bonnet and G. Stolovitzky (2010): “Towards a rigorous assessment of systems biology models: the DREAM3 challenges,” PLoS One, 5, e9202.

  • Quinonero-Candela, J., C. E. Ramussen and C. K. I. Williams (2005): “Approximation methods for Gaussian process regression,” J. Mach. Learn. Res., 6, 1939–1959.

  • Snelson, E. and Z. Ghahramani (2006): Sparse Gaussian processes using pseudo-inputs. In: Weiss, Y., Schölkopf, B. and Platt, J. (Eds.), Advances in neural information processing systems 18, Cambridge, MA: MIT Press, pp. 1257–1264.

About the article

Corresponding author: David L. Wild, Systems Biology Centre, University of Warwick, Coventry, UK, CV4 7AL, e-mail:


Published Online: 2015-05-30

Published in Print: 2015-06-01



Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.1515/sagmb-2014-0082. Export Citation

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