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.