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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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An empirical Bayesian approach for identifying differential coexpression in high-throughput experiments.

John A Dawson¹, Christina Kendziorski

¹Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706, USA.

|October 19, 2011

Summary

This study introduces a new empirical Bayesian method to identify differentially coexpressed (DC) gene pairs in genomic studies. This approach enhances power and controls false discoveries, offering a valuable complement to existing differential expression (DE) methods.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

High-throughput genomic experiments commonly identify differentially expressed (DE) genes.
Existing methods for DE gene identification do not fully address other regulatory patterns like differential coexpression (DC).
Investigating DC gene pairs is challenging due to large search spaces, outliers, and limitations of current methods (underpowered, prone to false discoveries, computationally intensive).

Purpose of the Study:

To develop an empirical Bayesian approach for identifying differentially coexpressed (DC) gene pairs.
To provide a method that controls the false discovery rate (FDR) without sacrificing statistical power.
To offer a computationally efficient and broadly applicable tool for genomic studies.

Main Methods:

An empirical Bayesian framework was developed for DC gene pair identification.
Modifications to the expectation-maximization algorithm and a procedural heuristic were employed to enhance computational efficiency.
The method was validated through simulations and case studies.

Main Results:

The proposed empirical Bayesian approach identifies significant DC gene pairs with controlled FDR and improved power compared to existing methods.
The method demonstrates superior performance in simulations, achieving better results in significantly less computational time.
Case studies indicate the approach is a practical complement to DE gene analysis in high-throughput genomics.

Conclusions:

The developed empirical Bayesian approach offers a powerful and computationally efficient solution for identifying differentially coexpressed gene pairs.
This method addresses key limitations of existing DC analysis techniques, improving reliability and tractability.
The approach is suitable for single or multiple study analyses and complements current differential expression analysis in genomics.