DCGL v2.0: An R Package for Unveiling Differential Regulation from Differential Co-expression

Jing Yang,Yuan-Yuan Li,Yi-Xue Li,Zhongming Zhao,Lei Liu,Liang-Xiao Ma,Bao-Hong Liu,Hui Yu,Yi Xing

doi:10.1371/journal.pone.0079729

Abstract

MotivationDifferential co-expression analysis (DCEA) has emerged in recent years as a novel, systematic investigation into gene expression data. While most DCEA studies or tools focus on the co-expression relationships among genes, some are developing a potentially more promising research domain, differential regulation analysis (DRA). In our previously proposed R package DCGL v1.0, we provided functions to facilitate basic differential co-expression analyses; however, the output from DCGL v1.0 could not be translated into differential regulation mechanisms in a straightforward manner.ResultsTo advance from DCEA to DRA, we upgraded the DCGL package from v1.0 to v2.0. A new module named “Differential Regulation Analysis” (DRA) was designed, which consists of three major functions: DRsort, DRplot, and DRrank. DRsort selects differentially regulated genes (DRGs) and differentially regulated links (DRLs) according to the transcription factor (TF)-to-target information. DRrank prioritizes the TFs in terms of their potential relevance to the phenotype of interest. DRplot graphically visualizes differentially co-expressed links (DCLs) and/or TF-to-target links in a network context. In addition to these new modules, we streamlined the codes from v1.0. The evaluation results proved that our differential regulation analysis is able to capture the regulators relevant to the biological subject.ConclusionsWith ample functions to facilitate differential regulation analysis, DCGL v2.0 was upgraded from a DCEA tool to a DRA tool, which may unveil the underlying differential regulation from the observed differential co-expression. DCGL v2.0 can be applied to a wide range of gene expression data in order to systematically identify novel regulators that have not yet been documented as critical.AvailabilityDCGL v2.0 package is available at http://cran.r-project.org/web/packages/DCGL/index.html or at our project home page http://lifecenter.sgst.cn/main/en/dcgl.jsp.

Highlights

In the transcriptome analysis domain, differential co-expression analysis (DCEA) is emerging as a unique complement to traditional differential expression analysis
Availability: DCGL v2.0 package is available at http://cran.r-project.org/web/packages/DCGL/index.html or at our project home page http://lifecenter.sgst.cn/main/en/dcgl.jsp
Among the many growing directions of DCEA, there is the so-called ‘‘differential regulation analysis’’ (DRA), which integrates the transcription factor (TF)-to-target information to probe upstream regulatory events that account for the observed co-expression changes

Summary

Introduction

In the transcriptome analysis domain, differential co-expression analysis (DCEA) is emerging as a unique complement to traditional differential expression analysis. Network (WGCNA) [3,4], Differential Co-expression profile (DCp) [5,6], Differential Co-expression enrichment (DCe) [5,6], ROS-DET [7], Gene Set Co-expression Analysis [8], and others. These methods vary in how they specify expression correlations and quantify differential co-expression; they differ in the levels they address: genes or gene sets. Researchers have begun to perform differential co-expression analyses of microRNAs [11,12] These studies are expected to lead to DRA at the post-transcription level. A software tool that implements the frontier DRA methods would fill this gap and propagate DRA methods to many more biomedical research fields

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Discussion

Conclusion