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Bioinformatics Advance Access published online on July 14, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl391
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 19, 2006
Revised July 7, 2006
Accepted July 10, 2006

Article

Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks

Adriano V. Werhli 1 *, Marco Grzegorczyk 2, and Dirk Husmeier 1

1 Biomathematics and Statistics Scotland, Edinburgh, United Kingdom
2 Department of Statistics, University of Dortmund, Germany

* To whom correspondence should be addressed.
Adriano V. Werhli, E-mail: adriano{at}bioss.ac.uk


  Abstract

Motivation: An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. Various reverse engineering methods have been proposed in the literature, and it is important to understand their relative merits and shortcomings. In the present paper, we compare the accuracy of reconstructing gene regulatory networks with three different modelling and inference paradigms: (1) Relevance networks (RNs): pairwise association scores independent of the remaining network; (2) graphical Gaussian models (GGMs): undirected graphical models with constraint-based inference, and (3) Bayesian networks (BNs): directed graphical models with score-based inference. The evaluation is carried out on the Raf pathway, a cellular signalling network describing the interaction of eleven phosphorylated proteins and phospholipids in human immune system cells. We use both laboratory data from cytometry experiments as well as data simulated from the gold-standard network. We also compare passive observations with active interventions.

Results: On Gaussian observational data, BNs and GGMs were found to outperform RNs. The difference in performance was not significant for the nonlinear simulated data and the cytoflow data, though. Also, we did not observe a significant difference between BNs and GGMs on observational data in general. However, for interventional data, BNs outperform GGMs and RNs, especially when taking the edge directions rather than just the skeletons of the graphs into account. This suggests that the higher computational costs of inference with BNs over GGMs and RNs are not justified when using only passive observations, but that active interventions in the form of gene knockouts and over-expressions are required to exploit the full potential of BNs.

Availability: Data, software and supplementary material are available from http://www.bioss.sari.ac.uk/staff/adriano/research.html.

Associate Editor: John Quackenbush
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