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

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.

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

“…Differential co-expression profile (DCp) and differential co-expression enrichment (DCe) are involved in the DCEA module for extracting DCGs and DCLs. DCp worked on the filtered set of gene co-expression value pairs, where each pair was composed of two co-expression values worked out under two different conditions separately (24,26). The present study used a length-normalized Euclidean distance to measure differential co-expression (dC) of the co-expression value pairs associated with a particular gene.…”

Algorithms for network-based identification of differential regulators from transcriptome data: a systematic evaluation

“…Of note, here we changed the original base-2 logarithm [12] to the more intuitive base-10 logarithm. It should be noted that all targets are restricted to those contained in the expression data.…”

“…Almost the same time, two other algorithms, RIF1 and RIF2, were introduced to integrate differential expression (DE) and differential co-expression (DCE) features [10,11]; they successfully identified myostatin as the main cause of muscle growth divergence between two cattle breeds [11]. Last year, we developed two algorithms, TED and TDD, for identifying differential regulators [12]. TED is engineered towards the enrichment of differential co-expression genes (DCGs) within targets, while TDD is directed towards the density of targets’ differential co-expression links (DCLs).…”

Molecular-level effects of eribulin and paclitaxel on breast cancer based on differential co-expression network analysis

“…DCGL 2.0 (Yang et al, 2013) is an R package for identifying differentially co-expressed genes and links (DCGs and DCLs, respectively) from gene expression microarray data. It examines the expression correlation based on the exact co-expression changes of gene pairs between two conditions, and thus can distinguish between significant co-expression changes and relatively trivial ones (Yu et al, 2011).…”