title: BFDCA: A Comprehensive Tool of Using Bayes Factor for Differential Co-Expression Analysis.
abstract: Comparing the gene-expression profiles between biological conditions is useful for understanding gene regulation underlying complex phenotypes. Along this line, analysis of differential co-expression (DC) has gained attention in the recent years, where genes under one condition have different co-expression patterns compared with another. We developed an R package Bayes Factor approach for Differential Co-expression Analysis (BFDCA) for DC analysis. BFDCA is unique in integrating various aspects of DC patterns (including Shift, Cross, and Re-wiring) into one uniform Bayes factor. We tested BFDCA using simulation data and experimental data. Simulation results indicate that BFDCA outperforms existing methods in accuracy and robustness of detecting DC pairs and DC modules. Results of using experimental data suggest that BFDCA can cluster disease-related genes into functional DC subunits and estimate the regulatory impact of disease-related genes well. BFDCA also achieves high accuracy in predicting case-control phenotypes by using significant DC gene pairs as markers. BFDCA is publicly available at http://dx.doi.org/10.17632/jdz4vtvnm3.1.
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, China 130012; Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Duolin
authors.initials: D
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Juexin
authors.initials: J
authors.lastname: Wang
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, China 130012; Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Yuexu
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, China 130012; Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.
authors.forename: Yanchun
authors.initials: Y
authors.lastname: Liang
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun, China 130012; Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA. Electronic address: xudong@missouri.edu.
authors.forename: Dong
authors.initials: D
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics*; D019295:Computational Biology; D003198:Computer Simulation; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D006801:Humans; D008962:Models, Theoretical; D012984:Software
keywords: Bayes factor; R package; gene expression; gene regulation; multivariate normal distribution