title: Unexpected links reflect the noise in networks.
abstract: 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.
authors.affiliation: Department of Statistics, Institute of Mathematics and Statistics, University of Sao Paulo, Sao Paulo, SP, Brazil.
authors.forename: Anatoly
authors.initials: A
authors.lastname: Yambartsev
authors.affiliation: College of Pharmacy, Oregon State University, Corvallis, OR, USA.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Perlin
authors.affiliation: Department of Mathematics, College of Science, Oregon State University, Corvallis, OR, USA.
authors.forename: Yevgeniy
authors.initials: Y
authors.lastname: Kovchegov
authors.affiliation: College of Veterinary Medicine, Oregon State University, Corvallis, OR, USA.
authors.forename: Natalia
authors.initials: N
authors.lastname: Shulzhenko
authors.affiliation: Instituto de Imunogenética - Associação Fundo de Incentivo à Pesquisa (IGEN-AFIP), São Paulo, SP, Brazil.
authors.forename: Karina L
authors.initials: KL
authors.lastname: Mine
authors.affiliation: College of Pharmacy, Oregon State University, Corvallis, OR, USA.
authors.forename: Xiaoxi
authors.initials: X
authors.lastname: Dong
authors.affiliation: College of Pharmacy, Oregon State University, Corvallis, OR, USA. anemorgun@hotmail.com.
authors.forename: Andrey
authors.initials: A
authors.lastname: Morgun
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks