Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter May 30, 2015

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

  • Christopher A. Penfold , Ahmed Shifaz , Paul E. Brown , Ann Nicholson and David L. Wild EMAIL logo

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.


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

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, e13397.10.1371/journal.pone.0013397Search in Google Scholar PubMed PubMed Central

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.Search in Google Scholar

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.Search in Google Scholar

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

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.Search in Google Scholar

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–i241.10.1093/bioinformatics/bts222Search in Google Scholar PubMed PubMed Central

Penfold, C, Millar, J, and Wild, D (2015). Inferring orthologous gene regulatory networks using interspecies data fusion. Doi 10.1093/bioinformatics/btv267.10.1093/bioinformatics/btv267Search in Google Scholar PubMed PubMed Central

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, e9202.10.1371/journal.pone.0009202Search in Google Scholar PubMed PubMed Central

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.Search in Google Scholar

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.Search in Google Scholar

Published Online: 2015-5-30
Published in Print: 2015-6-1

©2015 by De Gruyter

Downloaded on 20.4.2024 from https://www.degruyter.com/document/doi/10.1515/sagmb-2014-0082/html
Scroll to top button