Dear Ioannis Thank you very much for pointing me to meta-analysis. Although it may not solve my problem with the normalization, it gives me some other options to display the different correlation coefficients. One possibility is the use of Funnel plots, which are even available in library(rmeta). Another possibility is the use of forest-plots, as implemented in rmeta as metaplot. Sorrowly, rmeta does not include the Rosenthal-Rubin method or the Hunter-Schmidt method, as described in "Meta-Analysis of Correlations", see: http://www.sussex.ac.uk/Users/andyf/teaching/pg/meta.pdf Probably, the best solution for me may be to modify metaplot for the Hunter-Schmidt method. BTW, the manual to the program Meta-Analysis 5.3, is also very helpful, see: http://userpage.fu-berlin.de/~health/meta_e.htm Further suggestions in this direction are greatly appreciated. Best regards Christian Stratowa -----Original Message----- From: idimakos@upatras.gr [mailto:idimakos@upatras.gr] Sent: Wednesday, July 21, 2004 18:07 To: Christian.Stratowa@vie.boehringer-ingelheim.com Cc: bioconductor@stat.math.ethz.ch; r-help@stat.math.ethz.ch Subject: Re: [R] RE: Comparison of correlation coefficients - Details That sounds very close to a meta-analytic comparison of two statistics. As a matter of fact, the Rosenthal & Rubin approach transforms all primary statistics into Pearson r and then to Fisher's z and then follows with comparisons. More, comparisons can take into account sample sizes, or the value of some other predictor variable. I believe there is a Rosenthal book on meta-analysis published by Sage publications, as well as a Brian Mullen book published by Lawrence Erlbaum. Brian Mullen's book comes (or used to come) with a meta.exe program to perform meta-analyses. Hope this helps, Ioannis > Dear all > > I apologize for cross-posting, but first it is accepted custom to > thank the repliers and give a summary, and second I have still the > feeling that this problem might be a general statistical problem and > not necessarily related to microarrays only, but I might be wrong. > > First, I want to thank Robert Gentleman, Mark Kimpel and Mark Reiners > for their kind replies. Robert Gentleman kindly pointed me to the > Bioconductor package "MeasurementError.cor" as alternative to > "cor.test". Mark Kimpel suggested that 2-way factorial Anova or the > Bioconductor package "limma", respectively, may be helpful. Mark > Reiners suggested to use the p-value of "cor.test" to test the > significance. > > Maybe, I miss the point, but being not a statistician I am still > unsure if it is possible to compare correlation coefficients from > different sample sets. Both, the p-values from "cor.test" and from > "compcorr", could be used as measure of the significance. However, is > it possible to "normalize" correlation coefficients from different > sample sets? Could an expression such as "corr * (1 - pval)" be used > for normalization? Maybe, it is not possible to normalize correlation > coefficients? Would a barplot comparing the correlation coefficients > between two genes for different tissues be meaningful? (Alternatively, > I have tried to use (1-pval) to calculate the gray-level of the bars.) > > Any further suggestions would be appreciated very much. > > Best regards > Christian Stratowa > > -----Original Message----- > From: Stratowa,Dr.,Christian FEX BIG-AT-V > Sent: Monday, July 19, 2004 15:00 > To: 'bioconductor@stat.math.ethz.ch' > Subject: Comparison of correlation coefficients - Details > > > Dear all > > Maybe, my last mail did not explain my problem correctly: Since we are > interested, which genes have similar expression profiles in a certain > tissue or in different tissues, we have calculated the correlation > coefficients between all 46,000 x 46,000 genes of the HG_U133A/B > chipset for about 70 tissues, where the number of samples per tissue > ranges from 10 to more than 200. > > While writing an R-function to display the correlation coefficients > between gene A and B in the different tissues as bar-graph, I realized > that it may not be correct to compare the different correlation > coefficients directly, since the number of samples per tissue varyies > between 10 and 200. > > Thus, the question is: Is there a way to compare different correlation > coefficients and/or apply some kind of normalization? > > Assuming that this might be a well known statistical problem I was > browsing statistics books and the web for more information, but could > only find the function "compcorr" which gives a p-value how well you > can trust the comparison of two correlation coefficients from > different samples. > > Even though this might currently not be a direct Bioconductor > question, it is certainly a microarray analysis related question. Any > suggestions how to solve this problem would be greatly appreciated. > > Best regards > Christian Stratowa > > > -----Original Message----- > From: Stratowa,Dr.,Christian FEX BIG-AT-V > Sent: Tuesday, July 13, 2004 14:40 > To: 'bioconductor@stat.math.ethz.ch' > Subject: Comparison of correlation coefficients > > > 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 > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html >