A Bayesian regression approach to the inference of regulatory networks from gene expression data

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Abstract
Motivation: There is currently much interest in reverse-engineering regulatory relationships between genes from microarray expression data. We propose a new algorithmic method for inferring such interactions between genes using data from gene knockout experiments. The algorithm we use is the Sparse Bayesian regression algorithm of Tipping and Faul. This method is highly suited to this problem as it does not require the data to be discretized, overcomes the need for an explicit topology search and, most importantly, requires no heuristic thresholding of the discovered connections. Results: Using simulated expression data, we are able to show that this algorithm outperforms a recently published correlation-based approach. Crucially, it does this without the need to set any ad hoc threshold on possible connections. Availability: Matlab code which allows all experimental results to be reproduced is available at http://www.dcs.gla.ac.uk/~srogers/reg_nets.html Contact:srogers@dcs.gla.ac.uk Supplementary information: Appendices and supplementary figures mentioned in the text can be found at http://www.dcs.gla.ac.uk/~srogers/reg_nets.html