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

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• 作者：Duolin Wang¹ ; ² ; Juexin Wang² ; Yuexu Jiang¹ ; ² ; Yanchun Liang¹ ; ² ; Dong Xu¹ ; ² ; ^{xudong@missouri.edu}
• 关键词：DC ; differential co-expression ; DE ; differential expression ; BFDCA ; Bayes Factor approach for Differential Co-expression Analysis ; WGCNA ; Weighted Gene Coexpression Network Analysis ; ALL ; acute lymphoblastic leukemia ; LOOCV ; leave-one-out cross-validation ; GO ; gene ontology ; ER ; estrogen receptors ; GEO ; gene expression omnibus
• 刊名：Journal of Molecular Biology
• 出版年：2017
• 出版时间：3 February 2017
• 年：2017
• 卷：429
• 期：3
• 页码：446-453
• 全文大小：1812 K
• 卷排序：429

文摘

Differential co-expression (DC) analysis is useful in understanding functional and regulatory relationships among genes, but few tools are available for effective DC analysis. By integrating different aspects of DC patterns into a uniform Bayes factor, Bayes Factor approach for Differential Co-expression Analysis (BFDCA) can estimate DC between two conditions with high sensitivity. BFDCA clusters condition-specific genes into functional DC subunits and quantitatively characterizes their regulatory impact on genes. BFDCA identifies significant DC gene pairs and achieves high accuracy in predicting case/control phenotypes by using these gene pairs as markers. BFDCA is implemented in a general R package, which can be easily used by biological researchers and generate biologically meaningful hypotheses.