Charlotte Siska1, Russell Bowler2, Katerina Kechris3. 1. Computational Bioscience Program, Department of Pharmacology, University of Colorado Denver. 2. Department of Medicine, National Jewish, Denver, CO and. 3. Department of Biostatistics and Informatics, University of Colorado Denver, Denver, CO, USA.
Abstract
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: R code and sample data are available at https://github.com/siskac/discordant CONTACT: katerina.kechris@ucdenver.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: R code and sample data are available at https://github.com/siskac/discordant CONTACT: katerina.kechris@ucdenver.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Elizabeth A Regan; Craig P Hersh; Peter J Castaldi; Dawn L DeMeo; Edwin K Silverman; James D Crapo; Russell P Bowler Journal: Am J Respir Cell Mol Biol Date: 2019-08 Impact factor: 6.914
Authors: Jalal K Siddiqui; Elizabeth Baskin; Mingrui Liu; Carmen Z Cantemir-Stone; Bofei Zhang; Russell Bonneville; Joseph P McElroy; Kevin R Coombes; Ewy A Mathé Journal: BMC Bioinformatics Date: 2018-03-05 Impact factor: 3.169