draw.PDN.cluster {PDN}

Draw Personalized Disease Network for cluster of patients

Description

Draw Personalized Disease Network based on cluster information

Usage

draw.PDN.cluster(a0, dak)

Arguments

a0

network matrix get from buildnetworks

dak

ranks data for comorbidity data matrix

Examples

#Select a subset of data for toy example
comorbidity_data = comorbidity_data[c(1:10),]
survival_data = survival_data[c(1:10),]
##Clustering Example
k1 = datecut(comorbidity_data,survival_data[,1],survival_data[,2])
a = buildnetworks(comorbidity_data,k1)
datark = t(apply(comorbidity_data,1,rank))
require(survival)
zsq = NULL
for(i in 1:ncol(a)){
  a1 = (summary(coxph(Surv(as.numeric(survival_data[,1]),survival_data[,2]) ~ a[,i],
  data=as.data.frame(a)))$coefficient[,4])^2
  zsq = cbind(zsq,a1)
}
zsq = as.numeric(zsq)
wi=zsq/sum(zsq,na.rm=TRUE)
wi[wi<10^ -3]=10^ -3
wi[is.na(wi)]=10^ -3
#weighted matrix
wa = NULL
for(i in 1:ncol(a)){
 wa = cbind(wa,a[,i]*wi[i])
}
#PCA
pr.out=prcomp(wa)
x.svd=svd(wa)
x.score1 <- wa %*% x.svd$v
x.score2 <- x.svd$u %*% diag(x.svd$d)
##HC cluster using the first 8 PCA scores
dp<-dist(x.score2[,1:8])
hcp<-hclust(dp, method="ward.D")
##Plot Network
s1=rev(sort(apply(a[cutree(hcp,3)==2,],2,mean)))[1:50]
dak = sort(apply(datark[cutree(hcp,3)==2,],2,mean,na.rm=TRUE))
names(dak) = unlist(strsplit(names(dak),"DAT"))
draw.PDN.cluster(s1,dak)