DCGL (version 2.1.2)

DCGL-package: Differential Co-expression Analysis and Differential Regulation Analysis of Gene Expression Microarray Data

Description

DCGL package contains four modules which are Gene filtration module, Link filtration module, Differential Co-expression Analysis (DCEA) module and Differential Regulation Analysis (DRA) module. In Gene filtration module, there are expressionBasedfilter and varianceBasedfilter functions to filter genes on expression microarray data. In Link filtration module, there are rLinkfilter, percentLinkfilter and qLinkfilter functions to filter gene coexpression links in coexpression networks. DCp, DCe, WGCNA, LRC and ASC functions were implemented in DCEA module for extracting differentially coexpressed genes (DCGs) and differentially coexpressed links (DCLs). The final step of DCEA module is DCsum to determine DCGs and DCLs which come from DCe and DCp or only DCe method(s). In DRA module, there are DRsort, DRplot and DRrank functions to identify differentially regulated genes (DRGs) and differentially regulated links (DRLs), to visualize DRLs and DRL-related TF-to-target networks and to rank regulators in terms of their potential relevance to the biological phenotype, respectively.

Details

Package:

DCGL

Type:

Package

Version:

2.1.2

Date:

2014-12-18

License:

GPL (>2)

LazyLoad:

yes

References

Yang J, Yu H, Liu B-H, Zhao Z, Liu L, Ma L-X, Li Y-X and Li Y-Y. (2013) DCGL v2.0: An R Package for Unveiling Differential Regulation from Differential Co-expression. PLoS ONE 8(11): e79729. doi:10.1371/journal.pone.0079729 Friedrich Leisch. (2008) Creating R Packages: A Tutorial

Examples

data(exprs) 

## divide exprs into two parts corresponding to condition 1 
##(exprs.1) and condition 2 (exprs.2) respectively
expGenes<-rownames(exprs)
exprs<-exprs[1:100,]
exprs.1<-exprs[1:100,1:16] 
exprs.2<-exprs[1:100,17:63]

DCp.res<-DCp(exprs.1,exprs.2,
	link.method='qth',cutoff=0.25,N=0)

DCe.res<-DCe(exprs.1,exprs.2,
	link.method='qth',
	cutoff=0.25,
	nbins=10,p=0.1)


## combine two Differential Co-expression Analysis results
DCsum.res<-DCsum(DCp.res,DCe.res,
	DCpcutoff=0.25,DCecutoff=0.4)
DCsum.res$DCGs[1:3,]
DCsum.res$DCLs[1:3,]


## sort out differentially regulated genes and differentially regulated links
data(tf2target) ## TF-to-target relationships
DRsort.res<-DRsort(DCsum.res$DCGs,DCsum.res$DCLs,tf2target,expGenes)
## or
DRsort.res<-DRsort(DCe.res$DCGs,DCe.res$DCLs,tf2target,expGenes)

## plot differentially regulated links
DRplot.res<-DRplot(DCsum.res$DCGs,DCsum.res$DCLs,
	tf2target,
	expGenes,
	type='TF_bridged_DCL',
	vsize=5,asize=0.25,lcex=0.3,ewidth=1,
	figname=c('TF2target_DCL.pdf','TF_bridged_DCL.pdf'))


## rank regulators by TED or TDD
DRrank.res<-DRrank(DCsum.res$DCGs,DCsum.res$DCLs,
	tf2target,
	expGenes,
	rank.method=c('TED','TDD')[1],
	Nperm=0)


## rank regulators by RIF\
data(exprs_design)
RIF.res<-RIF(exprs,exprs.1,exprs.2,
	tf2target,
	exprs_design,
	p.value=0.05)