EBcoexpress

EBcoexpress applies an empirical Bayesian framework to perform differential co-expression analysis at the gene-pair level, identifying changes in gene–gene correlation across biological conditions.

Key Features:

• Empirical Bayesian Framework: Employs an empirical Bayesian methodology to assess changes in co-expression between gene pairs.
• Pairwise Correlation Analysis: Calculates pairwise correlations between genes across different biological conditions.
• False Discovery Rate Control: Controls the false discovery rate (FDR) when reporting differentially co-expressed gene pairs.
• Gene-pair-level Resolution: Identifies differential co-expression specifically at the gene-pair level.
• Implementation in R: Implemented in R as an analysis package.
• Complementary to Differential Expression: Complements differential expression analysis by detecting changes in gene–gene correlation rather than changes in individual gene expression levels.

Scientific Applications:

• Gene Regulatory Networks: Detects altered gene–gene interactions to reveal regulatory nodes and pathways.
• Disease Mechanisms: Identifies gene pairs with altered co-expression that may indicate biomarkers or therapeutic targets in disease states.
• Developmental Biology: Characterizes changes in gene interaction dynamics during development to study developmental processes and anomalies.

Methodology:

Input gene expression data from different conditions; compute pairwise correlations between genes across conditions; apply an empirical Bayesian adjustment to those correlations to identify significant changes in co-expression; apply FDR control to produce a list of differentially co-expressed gene pairs.