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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

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Published on: July 29, 2022

DCGL: an R package for identifying differentially coexpressed genes and links from gene expression microarray data.

Bao-Hong Liu¹, Hui Yu, Kang Tu

¹School of Life Science and Technology, Tongji University, Shanghai 200092, P R China.

Bioinformatics (Oxford, England)

|August 31, 2010

Summary

A new R package, DCGL, offers five methods for differential coexpression analysis (DCEA) to identify changes in gene correlations. This tool addresses the need for user-friendly software to complement traditional differential expression analysis (DEA).

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Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Differential coexpression analysis (DCEA) complements differential expression analysis (DEA) for studying gene interconnections.
Existing DCEA tools are limited, creating a need for accessible computational methods.

Purpose of the Study:

To implement and provide an R package, DCGL, containing five DCEA methods.
To facilitate the identification of differentially coexpressed genes and links.

Main Methods:

Implementation of three established DCEA methods.
Inclusion of two novel algorithms for DCEA.
Development of the 'DCGL' R package for user-friendly analysis.

Main Results:

DCGL integrates multiple DCEA approaches into a single package.
The package enables the identification of differentially coexpressed genes and links.

Conclusions:

DCGL serves as a valuable and easy-to-use tool for researchers conducting differential coexpression analyses.
The package enhances systems biology approaches by providing robust DCEA capabilities.