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 exp

SUBMITTER: Wang D 

PROVIDER: S-EPMC5272861 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature