Wang Duolin, Wang Juexin, Jiang Yuexu, Liang Yanchun, Xu Dong
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.
Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA.
J Mol Biol. 2017 Feb 3;429(3):446-453. doi: 10.1016/j.jmb.2016.10.030. Epub 2016 Oct 27.
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.
比较生物条件之间的基因表达谱有助于理解复杂表型背后的基因调控。沿着这条线，差异共表达（DC）分析近年来受到了关注，其中一种条件下的基因与另一种条件下的基因具有不同的共表达模式。我们开发了一种用于差异共表达分析（BFDCA）的R包贝叶斯因子方法用于DC分析。BFDCA的独特之处在于将DC模式的各个方面（包括移位、交叉和重新布线）整合到一个统一的贝叶斯因子中。我们使用模拟数据和实验数据对BFDCA进行了测试。模拟结果表明，BFDCA在检测DC对和DC模块的准确性和稳健性方面优于现有方法。使用实验数据的结果表明，BFDCA可以将疾病相关基因聚类为功能性DC亚基，并很好地估计疾病相关基因的调控影响。BFDCA通过使用显著的DC基因对作为标记在预测病例对照表型方面也达到了很高的准确性。BFDCA可在http://dx.doi.org/10.17632/jdz4vtvnm3.1上公开获取。
