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Related Experiment Video

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

R/EBcoexpress: an empirical Bayesian framework for discovering differential co-expression.

John A Dawson¹, Shuyun Ye, Christina Kendziorski

¹Statistics and Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA. DrJADawson@hotmail.com

Bioinformatics (Oxford, England)

|May 19, 2012

Summary

This summary is machine-generated.

R/EBcoexpress identifies differential gene co-expression (DC) using an empirical Bayesian method in R. This approach complements differential expression analysis for understanding gene regulation across conditions.

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Area of Science:

Bioinformatics
Computational Biology
Statistical Genetics

Background:

Understanding gene regulation across different biological conditions is crucial in molecular biology.
Differential expression analysis identifies genes with altered abundance but does not capture coordinated changes in gene pairs.
Differential co-expression analysis offers complementary insights into regulatory mechanisms.

Purpose of the Study:

To introduce R/EBcoexpress, an R package for identifying differential co-expression (DC) between gene pairs.
To implement the empirical Bayesian approach by Dawson and Kendziorski for DC analysis.
To provide a tool that complements differential expression analysis for a comprehensive understanding of gene regulation.

Main Methods:

Utilizes R, a free, open-source statistical programming language.
Employs an empirical Bayesian approach to examine correlations among gene pairs.
Identifies differential co-expression (DC) and controls the false discovery rate (FDR).

Main Results:

Generates a list of statistically significant differentially co-expressed gene pairs.
Provides a false discovery rate (FDR)-controlled output for robust biological interpretation.
The method is distinct from and complements traditional differential expression analyses.

Conclusions:

R/EBcoexpress offers a valuable tool for identifying differential gene co-expression.
This approach enhances the understanding of differential gene regulation across biological conditions.
The package is freely available on Bioconductor for broad accessibility in biological research.