The Library

Granger causality vs. dynamic Bayesian network inference: a comparative study

Tools

Zou, Cunlu, Denby, Katherine J. and Feng, Jianfeng. (2009) Granger causality vs. dynamic Bayesian network inference: a comparative study. BMC Bioinformatics, Vol.10 . Article number 122. ISSN 1471-2105

Preview

PDF
WRAP_Feng_correction.pdf - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (62Kb)

Preview

PDF
WRAP_Zou_Granger_causality.pdf - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (2227Kb)

Official URL: http://dx.doi.org/10.1186/1471-2105-10-122

Abstract

Background
In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. Currently, there are two common approaches used to explore the network structure among elements. One is the Granger causality approach, and the other is the dynamic Bayesian network inference approach. Both have at least a few thousand publications reported in the literature. A key issue is to choose which approach is used to tackle the data, in particular when they give rise to contradictory results.

Results
In this paper, we provide an answer by focusing on a systematic and computationally intensive comparison between the two approaches on both synthesized and experimental data. For synthesized data, a critical point of the data length is found: the dynamic Bayesian network outperforms the Granger causality approach when the data length is short, and vice versa. We then test our results in experimental data of short length which is a common scenario in current biological experiments: it is again confirmed that the dynamic Bayesian network works better.

Conclusion
When the data size is short, the dynamic Bayesian network inference performs better than the Granger causality approach; otherwise the Granger causality approach is better.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QH Natural history > QH301 Biology
Divisions:	Faculty of Science > Computer Science Faculty of Science > Life Sciences (2010- ) Faculty of Science > Centre for Scientific Computing
Library of Congress Subject Headings (LCSH):	Bioinformatics, Bayesian statistical decision theory, Biology -- Data processing, Structural bioinformatics
Journal or Publication Title:	BMC Bioinformatics
Publisher:	BioMed Central Ltd.
ISSN:	1471-2105
Official Date:	24 April 2009
Volume:	Vol.10
Article Number:	Article number 122
Identification Number:	10.1186/1471-2105-10-122
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Funder:	Engineering and Physical Sciences Research Council (EPSRC), Seventh Framework Programme (European Commission) (FP7)
Grant number:	EP/E002331/1 (EPSRC)
Related URLs:	Publisher
References:	Klipp E, Herwig R, Kowald A, Wierling C, Lehrach H: Systems Biology in Practice: Concepts, Implementation and Application. Weinheim: Wiley-VCH Press; 2005. Feng J, Jost J, Qian M: Networks: From Biology to Theory. London: Springer Press; 2007. Alon U: Network motifs: theory and experimental approaches. Nat Rev Genet 2007, 8(6):450-461. Tong AH, Lesage G, Bader GD, Ding H, Xu H, Xin X, Young J, Berriz GF, Brost RL, Chang M, Chen Y, Cheng X, Chua G, Friesen H, Goldberg DS, Haynes J, Humphries C, He G, Hussein S, Ke L, Krogan N, Li Z, Levinson JN, Lu H, Ménard P, Munyana C, Parsons AB, Ryan O, Tonikian R, Roberts T, Sdicu AM, Shapiro J, Sheikh B, Suter B, Wong SL, Zhang LV, Zhu H, Burd CG, Munro S, Sander C, Rine J, Greenblatt J, Peter M, Bretscher A, Bell G, Roth FP, Brown GW, Andrews B, Bussey H, Boone C: Global Mapping of the Yeast Genetic Interaction Network. Science 2004, 303:808. Tsai TY, Choi YS, Ma W, Pomerening JR, Tang C, Ferrell JE Jr: Robust, Tunable Biological Oscillations from Interlinked Positive and Negative Feedback Loops. Science 2008, 321:126. Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I, Zeitlinger J, Jennings EG, Murray HL, Gordon DB, Ren B, Wyrick JJ, Tagne JB, Volkert TL, Fraenkel E, Gifford DK, Young RA: Transcriptional Regulatory Networks in Saccharomyces cerevisiae. Science 2002, 298:799. Pearl J: Causality: Models, Reasoning, and Inference. Cambridge: Cambridge Univ. Press; 2000. Albo Z, Di Prisco GV, Chen Y, Rangarajan G, Truccolo W, Feng J, Vertes RP, Ding M: Is partial coherence a viable technique for identifying generators of neural oscillations. Biological Cybernetics 2004, 90:318. Horton PM, Bonny L, Nicol AU, Kendrick KM, Feng JF: Applications of multi-variate analysis of variances (MANOVA) to multi-electrode array data. Journal of Neuroscience Methods 2005, 146:22. Guo S, Wu J, Ding M, Feng J: Uncovering interactions in the frequence domain. PLoS Computational Biology 2008, 4(5):e1000087. Wu J, Liu X, Feng J: Detecting causality between different frequencies. Journal of Neuroscience Methods 2008, 167:367. Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan NJ, Chung S, Emili A, Snyder M, Greenblatt JF, Gerstein M: A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data. Science 2003, 302:449. Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP: Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data. Science 2005, 308:523. Ghahramani Z: Learning Dynamic Bayesian Networks. Berlin: Springer Press; 2004. Geweke J: Measurement of Conditional Linear Dependence and Feedback Between Time Series. Journal of the American Statistical Association 1982, 79(388):907. Geweke J: Measurement of Linear Dependence and Feedback Between Multiple Time Series. Journal of the American Statistical Association 1982, 77(378):304. Jensen FV: An introduction to Bayesian networks. London: UCL Press; 1996. Bach FR, Jordan MI: Learning Graphical Models for Stationary Time Series. IEEE transactions on signal processing 2004, 52(8):2189. Buntine WL: Operations for Learning with Graphical Models. Journal of Artificial Intelligence Research 1994, 2:159. Friedman N: Inferring Cellular Networks Using Probabilistic Graphical Models. Science 2004, 303:799. Guo S, Seth AK, Kendrick KM, Zhou C, Feng J: Partial Granger Causality-Eliminating Exogenous Inputs and latent Variables. Journal of neuroscience methods 2008, 172(1):79. Chen Y, Rangarajan G, Feng J, Ding M: Analyzing multiple nonlinear time series with extended Granger causality. Physics Letters A 2004, 324:26. Marinazzo D, Pellicoro M, Stramaglia S: Kernel-Granger causality and the analysis of dynamic networks. Physical review E 2008, 77:056215. Locke JC, Kozma-Bognár L, Gould PD, Fehér B, Kevei E, Nagy F, Turner MS, Hall A, Millar AJ: Experimental Validation of a predicted feedback loop in the multi-oscillator clock of Arabidopsis thaliana. Molecular Systems Biology 2006, 2:59. Ueda HR: Systems biology flowering in the plant clock field. Molecular Systems Biology 2006, 2:60. ISI Web of knowledge: [http://www.isiknowledge.com] On 24th July, 2008, a search on ISI tells us that there are 3531 papers on Bayesian network in the area of Comp. Sci., Math., Math + Comp. Biol. and Business + Economics, and 1125 papers on Granger causality. Wang S, Chen Y, Ding M, Feng J, Stein JF, Aziz TZ, Liu X: Revealing the dynamic causal interdenpendence between neural and muscular signals in Parkinsonian tremor. Journal of Franklin Institute-Engineering and Applied Mathematics 2007, 344(3–4):180. Wu J, Kendrick K, Feng J: Detecting Hot-Spots in Multivariates Biological Data. BMC Bioinformatics 2007, 8:331. Zhan Y, Halliday D, Jiang Ping, Liu X, Feng J: Detecting the time-dependent coherence between non-stationary electrophysiological signals-A combined statistical and time-frequency approach. Journal of Neuroscience Methods 2006, 156:322. Akaike H: Fitting Autoregressive Models for Regression. Annals of the Institute of Statistcal Mathmatics 1969, 21:243. Beamish N, Priestley MB: A Study of Autoregressive and Window Spectral Estimation. Applied Statistics 1981, 30(1):41. Morettin PA: Levinson Algorithm and Its Applications in Time Series Analysis. International Statistical Review 1984, 52(1):83. Morf M, Vieira A, Lee DTL, Kailath T: Recursive Multichannel Maximum Entropy Spectral Estimation. IEEE transactions on geosciences electronics 1978, GE-16(2):85. Ancona N, Marinazzo D, Stramaglia S: Radial Basis Function Approach to Nonlinear Granger Causality of Time Series. Physical Review E 2004, 70:056221. Bishop CM: Neural Networks for Pattern Recognition. Oxford: Oxford Univ. Press; 1995. Bishop CM: Pattern Recognition and Machine Learning. New York: Springer Press; 2006. Murphy K: Bayes Net Toolbox for Matlab. It can be downloaded from the website. [http://www.cs.ubc.ca/~murphyk/Software/BNT/bnt.html]
URI:	http://wrap.warwick.ac.uk/id/eprint/796