title: Assessing the utility of gene co-expression stability in combination with correlation in the analysis of protein-protein interaction networks.
abstract: BACKGROUND
  Gene co-expression, in the form of a correlation coefficient, has been valuable in the analysis, classification and prediction of protein-protein interactions. However, it is susceptible to bias from a few samples having a large effect on the correlation coefficient. Gene co-expression stability is a means of quantifying this bias, with high stability indicating robust, unbiased co-expression correlation coefficients. We assess the utility of gene co-expression stability as an additional measure to support the co-expression correlation in the analysis of protein-protein interaction networks.
  
  
  RESULTS
  We studied the patterns of co-expression correlation and stability in interacting proteins with respect to their interaction promiscuity, levels of intrinsic disorder, and essentiality or disease-relatedness. Co-expression stability, along with co-expression correlation, acts as a better classifier of hub proteins in interaction networks, than co-expression correlation alone, enabling the identification of a class of hubs that are functionally distinct from the widely accepted transient (date) and obligate (party) hubs. Proteins with high levels of intrinsic disorder have low co-expression correlation and high stability with their interaction partners suggesting their involvement in transient interactions, except for a small group that have high co-expression correlation and are typically subunits of stable complexes. Similar behavior was seen for disease-related and essential genes. Interacting proteins that are both disordered have higher co-expression stability than ordered protein pairs. Using co-expression correlation and stability, we found that transient interactions are more likely to occur between an ordered and a disordered protein while obligate interactions primarily occur between proteins that are either both ordered, or disordered.
  
  
  CONCLUSIONS
  We observe that co-expression stability shows distinct patterns in structurally and functionally different groups of proteins and interactions. We conclude that it is a useful and important measure to be used in concert with gene co-expression correlation for further insights into the characteristics of proteins in the context of their interaction network.
authors.affiliation: Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-8639, Japan. ashwini@hgc.jp
authors.forename: Ashwini
authors.initials: A
authors.lastname: Patil
authors.forename: Kenta
authors.initials: K
authors.lastname: Nakai
authors.forename: Kengo
authors.initials: K
authors.lastname: Kinoshita
mesh_terms: D000465:Algorithms; D030562:Databases, Protein; D005786:Gene Expression Regulation; D006801:Humans; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*
chemical_list: D011506:Proteins