  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.
