Abstract
BackgroundGene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis.ResultsWe propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches. This method is based on a special relationship between sign of correlation (positive/negative) and directionality (up/down) of gene regulation, and allows for the identification and removal of approximately half of all erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we establish a mathematical foundation for our method. Analyzing existing biological datasets, we find a strong correlation between the results of our method and false discovery rate (FDR). Furthermore, simulation analysis demonstrates that our method provides a more accurate estimate of network error than FDR.ConclusionsThus, our study provides a new robust approach for improving reconstruction of covariation networks.ReviewersThis article was reviewed by Eugene Koonin, Sergei Maslov, Daniel Yasumasa Takahashi.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0155-0) contains supplementary material, which is available to authorized users.
Highlights
Gene covariation networks are commonly used to study biological processes
In case of gene expression, these alterations represent a consequence of the two key factors: first, the original stimulus that underlies the transition of a biological system from one state to another; and the second factor, a biological process that drives regulatory relations between individual genes independently on the presence of the
We demonstrated the presence of this inter-dependence in different types of data, found that a departure from this relation reflects a proportion of erroneous edges in the regulatory networks, and developed a mathematical theory of this phenomenon
Summary
The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis It is quite common, especially in biology, that in order to understand how systems transition from one state to another (e.g. from health to disease) scientists compare how parameters such as gene expressions, protein levels, or metabolite abundances differ between these states. Gene expression networks have been widely used to advance global understanding of principles that govern regulatory processes in biology [6, 7], to disclose molecular mechanisms of diseases [8], and even helping with finding better drugs [9]. Cancer is a very good example of applications of gene expression networks because
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