get_full_cormat <-
function(data_m_fc_withfeats,targeted.index=NA,cor.method="spearman",write.files=TRUE){
Sys.sleep(0.2)
allsig_pcornetwork<-{}
cormat<-{}
allsig_pcornetwork_fdr0.05<-{}
metab_names<-paste(data_m_fc_withfeats$mz,data_m_fc_withfeats$time,sep="_")
rnames<-paste("mzid_",seq(1,dim(data_m_fc_withfeats)[1]),sep="")
if(dim(data_m_fc_withfeats)[1]>1)
{
m1<-apply(data_m_fc_withfeats[,-c(1:2)],2,as.numeric)
rownames(m1)=rnames
data_mt<-t(m1)
}else{
m1<-as.numeric(data_m_fc_withfeats[,-c(1:2)])
data_mt<-(m1)
}
data_mt<-as.matrix((data_mt))
print(paste("Computing ",cor.method," correlation matrix",sep=""))
cormat<-WGCNA::cor(data_mt,use="pairwise.complete.obs",method=cor.method)
colnames(cormat)<-as.character(metab_names)
rownames(cormat)<-as.character(metab_names)
num_samp<-dim(data_m_fc_withfeats[,-c(1:2)])[2]
pearson_resvec<-as.vector(cormat)
cl<-makeCluster(3)
pearson_pvalue<-parLapply(cl,1:length(pearson_resvec),function(x){
return(WGCNA::corPvalueStudent(pearson_resvec[x],num_samp))
})
stopCluster(cl)
complete_pearsonpvalue_mat<-unlist(pearson_pvalue)
dim(complete_pearsonpvalue_mat)<-dim(cormat)
cormat<-as.data.frame(cormat)
cormat<-as.matrix(cormat)
pearson_Res_all<-{}
cormat<-round(cormat,2)
complete_pearsonpvalue_mat<-as.data.frame(complete_pearsonpvalue_mat)
complete_pearsonpvalue_mat<-round(complete_pearsonpvalue_mat,2)
if(write.files==TRUE)
{
write.table(cormat,file="correlationmatrix.txt",sep="\t",row.names=TRUE)
write.table(complete_pearsonpvalue_mat,file="pvalue_matrix.txt",sep="\t",row.names=TRUE)
####savecormat,file="full_correlation.Rda")
if(is.na(targeted.index)==FALSE){
cormat<-cormat[targeted.index,]
complete_pearsonpvalue_mat<-complete_pearsonpvalue_mat[targeted.index,]
write.table(cormat,file="targetcorrelationmatrix.txt",sep="\t",row.names=TRUE)
write.table(complete_pearsonpvalue_mat,file="targetpvalue_matrix.txt",sep="\t",row.names=TRUE)
}
}
return(cormat)
}
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