Gene regulatory networks are commonly used for modeling biological processesand revealing underlying molecular mechanisms. The reconstruction of gene regulatory networks from observational data is a challengingtask , especially considering the large number of players (e.g. genes) involved and the small number of biological replicates available for analysis. Herein, we 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 correlation and causality , and allows for the identification and to 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 the commonly used false discovery rate (FDR) technique . Furthermore, simulation analysis demonstrates that with large networks our new method provides a more accurate estimate of network error than FDR.
Gene 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. We 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.
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