Abstract
The Common topological features of related species gene regulatory networks suggest reconstruction of the network of one species by using the further information from gene expressions profile of related species. We present an algorithm to reconstruct the gene regulatory network named; F-MAP, which applies the knowledge about gene interactions from related species. Our algorithm sets a Bayesian framework to estimate the precision matrix of one species microarray gene expressions dataset to infer the Gaussian Graphical model of the network. The conjugate Wishart prior is used and the information from related species is applied to estimate the hyperparameters of the prior distribution by using the factor analysis. Applying the proposed algorithm on six related species of drosophila shows that the precision of reconstructed networks is improved considerably compared to the precision of networks constructed by other Bayesian approaches.
Highlights
Constructing gene regulatory networks (GRNs) using microarray gene expressions data is one of the most challenging issues in bioinformatics
The results demonstrate the effectiveness of F-MAP to exploit external hints of other species gene expressions and the improvement in the precision of the reconstructed network considerably
Non-zero elements in the precision matrix indicate the presence of direct interaction between two genes
Summary
Constructing gene regulatory networks (GRNs) using microarray gene expressions data is one of the most challenging issues in bioinformatics. The noisy nature and high-dimensionality of microarray data make it difficult to find appropriate measures for characterizing gene relationships. There are various algorithm introduced for constructing gene networks. Most of them infer edges in the network by using the marginal or partial correlations between pair of genes [1,2,3,4,5]. The empirical sample covariance or correlation matrix is a standard tool for estimation of gene associations. These estimations often have poor behaviors in high-dimensional settings such as microarray datasets where the number of observations is much smaller than the number of genes [3]
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