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Detecting Differentially Coexpressed Genes from Labeled Expression Data: A Brief Review.

Mitsunori Kayano, Motoki Shiga, Hiroshi Mamitsuka

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|September 11, 2015

Summary

This review categorizes methods for analyzing differential gene coexpression in two scenarios: paired genes and multiple genes. Current methods, especially for multiple genes, show room for improvement in efficiency and performance.

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Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Differential gene coexpression analysis is crucial for understanding gene function and regulatory networks.
Existing methods often focus on either pairs of genes or larger gene sets, with distinct analytical challenges.

Purpose of the Study:

To systematically review and categorize existing methods for differential coexpression analysis.
To compare the performance of these methods using both synthetic and real biological data.

Main Methods:

Categorization of methods into two main cases: analysis of two paired genes and analysis of multiple genes.
Technical description of four approaches for paired genes and three for multiple genes.
Comparative experimental evaluation using synthetic and real gene expression datasets.

Main Results:

Performance evaluation revealed limitations in current differential coexpression methods, particularly for multiple gene sets.
The best-performing methods for multiple genes were relatively simple and intuitive, suggesting potential for optimization.
Comparative experiments highlighted areas where method efficiency can be significantly improved.

Conclusions:

The study identifies a need for more efficient and robust methods for differential coexpression analysis, especially for larger gene sets.
Current approaches may not fully capture complex coexpression patterns in biological systems.
Further research into novel algorithms is warranted to enhance the accuracy and efficiency of multi-gene coexpression analysis.