Unexpected links reflect the noise in networks

Anatoly Yambartsev, Michael A. Perlin, Yevgeniy Kovchegov, Natalia Shulzhenko, Karina L. Mine, Xiaoxi Dong, Andrey Morgun

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. Thus, our study provides a new robust approach for improving reconstruction of covariation networks. This article was reviewed by Eugene Koonin, Sergei Maslov, Daniel Yasumasa Takahashi.