# Re: [R] correlatation matrix

From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>
Date: Tue, 31 May 2011 11:09:46 +0100 (BST)

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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```--
Brian D. Ripley,                  ripley_at_stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
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