Dear Bioconductor expeRts Is it possible to compare correlation coefficients or to normalize different correlation coefficients? Concretely, we have the following situation: We have gene expression profiles for different tissues, where the number of samples per tissue are different, ranging from 10 to 250. We are able to determine the correlation between two genes A and B for each tissue separately, using "cor.test". However, the question arises if the correlation coefficients between different tissues can be compared or if they must somehow be "normalized", since the number of samples per tissue varyies. Searching the web I found the function "compcorr", see: http://www.fon.hum.uva.nl/Service/Statistics/Two_Correlations.html http://ftp.sas.com/techsup/download/stat/compcorr.html and implemented it in R: compcorr <- function(n1, r1, n2, r2){ # compare two correlation coefficients # return difference and p-value as list(diff, pval) # Fisher Z-transform zf1 <- 0.5*log((1 + r1)/(1 - r1)) zf2 <- 0.5*log((1 + r2)/(1 - r2)) # difference dz <- (zf1 - zf2)/sqrt(1/(n1 - 3) + (1/(n2 - 3))) # p-value pv <- 2*(1 - pnorm(abs(dz))) return(list(diff=dz, pval=pv)) } Would it make sense to use the resultant p-value to "normalize" the correlation coefficients, using: corr <- corr * compcorr()$pval Is there a better way or an alternative to "normalize" the correlation coefficients obtained for different tissues? Thank you in advance for your help. Since in the company I am not subscribed to bioconductor-help, could you please reply to me (in addition to bioconductor-help) P.S.: I have posted this first at r-help and it was suggested to me to post it here, too. Best regards Christian Stratowa ============================================== Christian Stratowa, PhD Boehringer Ingelheim Austria Dept NCE Lead Discovery - Bioinformatics Dr. Boehringergasse 5-11 A-1121 Vienna, Austria Tel.: ++43-1-80105-2470 Fax: ++43-1-80105-2782 email: christian.stratowa@vie.boehringer-ingelheim.com