See ?cor for an efficient vectorized way to find the correlation
matrix.
You could compute p-values from the matrix (read the code for
stats:::cor.test.default for the formulae), but note that with
169x20043 p-values, they don't really have much meaning.
On Tue, 31 May 2011, Unger, Kristian, Dr. wrote:
Hi there,
I wonder if there is a way of efficiently generating a correlation matrix of
two expression matrices. I want to correlate miRNA and mRNA expression and used
the following code:
##dat.mi miRNA expression matrix, dat.m mRNA expression matrix
nc <- nrow(dat.mi)
cor.mat <- data.frame(rep(NA,nrow(dat.m)))
pval.mat <- data.frame(rep(NA,nrow(dat.m)))
for(i in 1:nc)
{
cr <- vector()
pv <- vector()
print(paste(i," mirs out of ", nc,sep=""))
for (k in 1:nrow(dat.m))
{
#print(paste(k," genes out of ", nrow(dat.m),sep=""))
cr2 <- cor.test(as.numeric(dat.mi[i,]),as.numeric(dat.m[k,]))
cr <- c(cr,cr2$estimate)
pv <- c(pv,cr2$p.value)
}
cor.mat <- cbind(cor.mat,as.vector(cr))
pval.mat <- cbind(pval.mat,as.vector(pv))
}
The columns in the output represent miRNAs and the rows mRNAs. The
code works fine but takes ages (about an 1.5h for generating a
169x20043 matrix) so I wonder if there is a more efficient way of
doing this calculation?
Best wishes
Kristian
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