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Question: paired experiments in limma
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gravatar for Pedro Jares
12.7 years ago by
Pedro Jares10
Pedro Jares10 wrote:
I am using affymetrix arrays to study primary and metastic tumors from the same patients (five patients, so five primary and five metastatic tumors). As we have two classes and paired experiments I have been using a paired t-test. However, I would like to use limma with my arrays. Somewhere, I found that I could introduce a block in order to get the lmFit. I have been doing that for a while and getting pretty nice results. However, recently in the new limma manual appears that the block option should be used for technical replicates, and you need to calculate duplicateCorrelation. As far as I understood using block argument alone in lmFit you assume a default correlation of 0.75. However, if I do duplicateCorrelation, the correlation is 0.5. When I compare the top 50 genes that I got doing block without dupCorr and with dupCorr, I see that the p values and B values are worst for block with dupCorr, moreover if I do a hierarchical cluster of the samples using the two list of genes the one obtained by doing block without dupCorr seems to performs much better. I would like to know what to do in order to analyze paired experiments (this will allow us to remove inter-person variability!!!,) to compare two classes with single color arrays in limma. I have the impression that paired experiments are clearly different to technical replicates. So, I would appreciate if anybody can tell if I should use duplicateCorrelation or something else. The commands I have been using are: fit<-lmFit(eset,design,block=c(1,1,2,2)) or corfit<-duplicateCorrelation(eset,design,block=c(1,1,2,2)) fit<-lmFit(eset,design,block=c(1,1,2,2),correlation=corfit$consensus) Thank you very much for your help and your time, Best wishes, Pedro
ADD COMMENTlink modified 12.7 years ago by Gordon Smyth32k • written 12.7 years ago by Pedro Jares10
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gravatar for Gordon Smyth
12.7 years ago by
Gordon Smyth32k
Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
Gordon Smyth32k wrote:
> Date: Tue, 1 Mar 2005 07:07:16 -0800 (PST) > From: Pedro Jares <pjares@yahoo.com> > Subject: [BioC] paired experiments in limma > To: bioconductor@stat.math.ethz.ch > > I am using affymetrix arrays to study primary and > metastic tumors from the same patients (five patients, > so five primary and five metastatic tumors). As we > have two classes and paired experiments I have been > using a paired t-test. However, I would like to use > limma with my arrays. Somewhere, I found that I could > introduce a block in order to get the lmFit. I have > been doing that for a while and getting pretty nice > results. However, recently in the new limma manual > appears that the block option should be used for > technical replicates, and you need to calculate > duplicateCorrelation. As far as I understood using > block argument alone in lmFit you assume a default > correlation of 0.75. > However, if I do duplicateCorrelation, the correlation > is 0.5. When I compare the top 50 genes that I got > doing block without dupCorr and with dupCorr, I see > that the p values and B values are worst for block > with dupCorr, moreover if I do a hierarchical cluster > of the samples using the two list of genes the one > obtained by doing block without dupCorr seems to > performs much better. > I would like to know what to do in order to analyze > paired experiments (this will allow us to remove > inter-person variability!!!,) to compare two classes > with single color arrays in limma. I have the > impression that paired experiments are clearly > different to technical replicates. So, I would > appreciate if anybody can tell if I should use > duplicateCorrelation or something else. All forms of blocking are much the same from a mathematical point of view, whether they are technical replicates or blocking on patient. The direct extension of a paired t-test would actually arise from fitting patient as a fixed effect. If you did that, limma would compute for you paired t-tests with moderated denominators. If you use the 'block' argument instead of using a fixed effect, I would generally recommend that you estimate the common correction rather than take a preset value. However I suspect that, in this case, if you use a large correlation like 0.75 or higher the results might be very similar to using a fixed patient effect. Gordon > The commands I have been using are: > > fit<-lmFit(eset,design,block=c(1,1,2,2)) > > > or > > corfit<-duplicateCorrelation(eset,design,block=c(1,1,2,2)) > > fit<-lmFit(eset,design,block=c(1,1,2,2),correlation=corfit$consensus) > > > Thank you very much for your help and your time, > > Best wishes, > > Pedro
ADD COMMENTlink written 12.7 years ago by Gordon Smyth32k
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