title: The discordant method: a novel approach for differential correlation.
abstract: Current differential correlation methods are designed to determine molecular feature pairs that have the largest magnitude of difference between correlation coefficients. These methods do not easily capture molecular feature pairs that experience no correlation in one group but correlation in another, which may reflect certain types of biological interactions. We have developed a tool, the Discordant method, which categorizes the correlation types for each group to make this possible.
  
  
  
  We compare the Discordant method to existing approaches using simulations and two biological datasets with different types of -omics data. In contrast to other methods, Discordant identifies phenotype-related features at a similar or higher rate while maintaining reasonable computational tractability and usability.
  
  
  
  R code and sample data are available at https://github.com/siskac/discordant
  
  
  
  katerina.kechris@ucdenver.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Computational Bioscience Program, Department of Pharmacology, University of Colorado Denver.
authors.forename: Charlotte
authors.initials: C
authors.lastname: Siska
authors.affiliation: Department of Medicine, National Jewish, Denver, CO and.
authors.forename: Russell
authors.initials: R
authors.lastname: Bowler
authors.affiliation: Department of Biostatistics and Informatics, University of Colorado Denver, Denver, CO, USA.
authors.forename: Katerina
authors.initials: K
authors.lastname: Kechris
mesh_terms: D000465:Algorithms; D019295:Computational Biology