####pca analysis
exDat=read.table("./demo/QN_PPP1.txt", as.is = T, header = T)
PPP1=read.table("./demo/ID_PPP1.txt", as.is = T, header = T)
## Warning: package 'Rcpp' was built under R version 3.4.4
print(paste("Rcpp start: ", date()))
## [1] "Rcpp start: Sun Jul 15 17:21:39 2018"
pCorMc=proCorMatrix_c(exDat)
print(paste("Rcpp end: ", date()))
## [1] "Rcpp end: Sun Jul 15 17:21:47 2018"
print(paste("R start: ", date()))
## [1] "R start: Sun Jul 15 17:21:47 2018"
pCorM=proCorMatrix(exDat)
print(paste("R end: ", date()))
## [1] "R end: Sun Jul 15 17:25:43 2018"
layout(matrix(1:2, 1, 2))
plot(pCorMc[col(pCorMc)<row(pCorMc)], pCorM[col(pCorM)<row(pCorM)], main="Rcpp vs R", bty='l')
eg=eigen(pCorMc)
plot(eg$vectors[,1], eg$vectors[,2], xlab="PC 1", ylab="PC 2", bty='l', main = "PCA", pch=16, cex=0.5)
idx=which(PPP1$Tissue=="A")
points(eg$vectors[idx,1], eg$vectors[idx,2], cex=2, pch=16, col="pink")
#misPro=proMissing(exDat) #missing per protein (column)
#misInd=indMissing(exDat) #missing per individual (row)