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On reverse engineering of gene interaction networks using time course data with repeated measurements

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Morrissey, Edward R., Juárez, Miguel A., Denby, Katherine J. and Burroughs, Nigel John (2010) On reverse engineering of gene interaction networks using time course data with repeated measurements. Bioinformatics, Vol.26 (No.18). pp. 2305-2312. doi:10.1093/bioinformatics/btq421 ISSN 1367-4803.

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Official URL: http://dx.doi.org/10.1093/bioinformatics/btq421

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Abstract

Motivation: Gene expression measurements are the most common data source for reverse engineering gene interaction networks. When dealing with destructive sampling in time course experiments, it is common to average any available measurements for each time point and to treat this as the actual time series data for fitting the network, neglecting the variability contained in the repeated measurements. Proceeding in such a way can affect the retrieved network topology.

Results: We propose a fully Bayesian method for reverse engineering a gene interaction network, based on time course data with repeated measurements. The observations are treated as surrogate measurements of the underlying gene expression. As these measurements often contain outliers, we use a non-Gaussian specification for dealing with measurement error. The network interactions are assumed linear and an autoregressive model is specified, augmented with indicator variables that allow inference on the topology of the network. We analyse two in silico and one in vivo experiments, the latter dealing with the circadian clock in Arabidopsis thaliana. A systematic attenuation of the estimated regulation strengths and a concomitant overestimation of their precision is demonstrated when measurement error is disregarded. Thus, a clear improvement in the inferred topology for the synthetic datasets is demonstrated when this is included. Also, the influence of outliers in the retrieved network is demonstrated when using the in vivo data.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QD Chemistry
T Technology > TP Chemical technology
Q Science > QH Natural history > QH301 Biology
Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Science > Mathematics
Faculty of Science, Engineering and Medicine > Research Centres > Warwick Systems Biology Centre
Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) > Warwick HRI (2004-2010)
Journal or Publication Title: Bioinformatics
Publisher: Oxford University Press
ISSN: 1367-4803
Official Date: September 2010
Dates:
DateEvent
September 2010Published
Volume: Vol.26
Number: No.18
Number of Pages: 8
Page Range: pp. 2305-2312
DOI: 10.1093/bioinformatics/btq421
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Warwick Systems Biology Doctoral Training Centre, Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), PRESTA
Grant number: BB/F003498/1 (BBSRC), BB/F005806/1 (BBSRC)

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