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Identifying interactions in the time and frequency domains in local and global networks : a Granger causality approach

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Zou, Cunlu, Ladroue, Christophe, Guo, Shuixia and Feng, Jianfeng. (2010) Identifying interactions in the time and frequency domains in local and global networks : a Granger causality approach. BMC Bioinformatics, Vol.11 . Article 337. ISSN 1471-2105

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Official URL: http://dx.doi.org/10.1186/1471-2105-11-337

Abstract

Background
Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality.

Results
Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered.

Conclusions
The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history > QH426 Genetics Q Science > QH Natural history > QH301 Biology
Divisions:	Faculty of Science > Centre for Scientific Computing Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Time-series analysis, Computational biology, Causality (Physics)
Journal or Publication Title:	BMC Bioinformatics
Publisher:	BioMed Central Ltd.
ISSN:	1471-2105
Official Date:	21 June 2010
Volume:	Vol.11
Page Range:	Article 337
Identification Number:	10.1186/1471-2105-11-337
Status:	Peer Reviewed
Access rights to Published version:	Open Access
Funder:	Engineering and Physical Sciences Research Council (EPSRC), Guo jia zi ran ke xue ji jin wei yuan hui (China) [National Natural Science Foundation of China] (NSFC), Hunan Provincial Education Department (HPED)
Grant number:	EP/E002331/1 (EPSRC), 10901049 (NSFC), 09C636 (HPED)
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URI:	http://wrap.warwick.ac.uk/id/eprint/3317