Exploring disease similarity in terms of dysfunctional regulation mechanisms

Description

DSviaDRM package contains five functions which are DCEA, DCpathway, DS, comDCGL and comDCGLplot. In DCEA function, differentially co-expressed genes (DCGs) and differentially co-expressed links (DCLs) are extracted from disease vs. health samples. Then DCpathway function assigns differential co-expression values (dCs) of pathways to be the average dC of their component genes. Then DS employs partial correlation coefficient as the disease similarity for each disease pairs. And DS contains a permutation process for evaluating the statistical significant of observed disease partial correlation coefficients. At last, comDCGL and comDCGLplot sort out shared DCGs and DCLs with regulation information and visualize them.

Details

Package: DSviaDRM
Type: Package
Version: 1.0
Date: 2015-05-07
License: GPL (>2)

~~ An overview of how to use the package, including the most important ~~ ~~ functions ~~

Author(s)

Jing Yang

Maintainer: Jing Yang

References

Yang J, Wu S-J, Li Y-Y, Li Y-X. The human disease network in terms of dysfunctional regulatory mechanisms. (2015)

Examples

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##################################################################
## computate DCEA results for three disease, Allergic asthma (AA), 
## Chronic kidney disease (CKD) and Type 2 Diabetes (T2D).
##################################################################
#data(exprs1)
#data(exprs2)
#data(exprs3)

##########################################################################
## the default value of cutoff in DCEA is 0.25, 
## here cutoff is set to 1 for saving time when demonstrating the examples.
#DCEA.AA.res<-DCEA(exprs1[1:200,1:5],exprs1[1:200,6:10],link.method="percent",
#	cutoff=1,N=0,nbins=20,p=0.1) 
#DCEA.CKD.res<-DCEA(exprs2[1:300,1:25],exprs2[1:300,26:31],link.method="percent",
#	cutoff=1,N=0,nbins=20,p=0.1) 
#DCEA.T2D.res<-DCEA(exprs3[1:200,1:12],exprs3[1:200,13:35],link.method="percent",
#	cutoff=1,N=0,nbins=20,p=0.1) 
##########################################################################

##########################################################################
## computate dCs of pathways for each disease
#data(pathways)
#DCpathway.AA.res<-DCpathway(DCEA.res=DCEA.AA.res, DisName="AA",pathways)
#DCpathway.CKD.res<-DCpathway(DCEA.res=DCEA.CKD.res, DisName="CKD",pathways)
#DCpathway.T2D.res<-DCpathway(DCEA.res=DCEA.T2D.res, DisName="T2D",pathways)
##########################################################################

##########################################################################
## computate disease similarities
#DCpathway.disn = cbind(DCpathway.AA.res, DCpathway.CKD.res, DCpathway.T2D.res)
#DCEA.disn = list(dis1 = DCEA.AA.res, dis2 = DCEA.CKD.res, dis3 = DCEA.T2D.res)
#DS.res<-DS(DCpathway.disn, Ndis = 3, DCEA.disn, 
#	DisNames = c("AA", "CKD", "T2D"), pathways, cutoff = 0.05,
#	Nper = 0, 
#	FigName = "DisNetwork.pdf", vsize = 5, lcex = 0.3, ewidth = 1.5)
#DS.res[1:3,]
##########################################################################

##########################################################################
## sort out common DCGs and DCLs in AA, CKD and T2D.
#data(tf2target)
#comDCGL.res<-comDCGL(Ndis = 3, DCEA.disn , 
#	DisNames = c("AA", "CKD", "T2D"), 
#	cutoff = 0.25, tf2target)
#comDCGL.res$comDCGs[1:3,]
#comDCGL.res$comDCLs[1:3,]
##########################################################################

##########################################################################
## plot common DCGs and common DCLs with regulation information.
#comDCGLplot.res<-comDCGLplot(comDCGL.res,FigName="comDCGL.pdf",tf2target,
#	vsize=5,asize=0.25,lcex=0.3,ewidth=1.5)
##########################################################################