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

Published: 28 June 2016| Version 1 | DOI: 10.17632/jdz4vtvnm3.1
Duolin Wang,
Juexin Wang,
Yuexu Jiang


BFDCA is a comprehensive tool of using Bayes factor for Differential Co-expression (DC) analysis. This package contains three main functions: (1) clustering condition-specific genes into functional DC subunits; (2) quantitatively characterizing the regulatory impact of genes based on their differential connectivity within DC structures; (3) providing a DC-based prediction model to predict case/control phenotypes by taking DC significant gene pairs as markers.


Steps to reproduce

Users could read the BFDCAVignette.html for quick start of this R package. More details about BFDCA are in the BFDCA-manual.pdf.