BFDCA: A Comprehensive Tool of Using Bayes Factor for Differential Co-Expression Analysis

JOURNAL OF MOLECULAR BIOLOGY

卷:429

Article

Wang, Duolin^1,2,3  Wang, Juexin^2,3  Jiang, Yuexu^1,2,3  Liang, Yanchun^1,2,3  Xu, Dong^1,2,3 

[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China

[2] Univ Missouri, Dept Comp Sci, Columbia, MO 65211 USA

[3] Univ Missouri, Christopher S Bond Life Sci Ctr, Columbia, MO 65211 USA

关键词: gene expression;    gene regulation;    Bayes factor;    R package;    multivariate normal distribution;   

DOI  :  10.1016/j.jmb.2016.10.030

来源: Elsevier

【 摘 要 】

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. (C) 2016 Elsevier Ltd. All rights reserved.

【 授权许可】

Free