Hi, I have 4 replicate slides. Within each slide, there are 3 replicate spots for each gene. After doing the within Array normalization, I ran lmFit(limma) with two ways and get different correlation values as following: (1)lmFit1 > ADlmFit1 <- lmFit(ADraw.NormWA, ADdesign, ndups=3) > ADlmFit1$correlation [1] 0.75 (2)lmFit2 > cor <- duplicateCorrelation(ADraw.NormWA,ADdesign, ndups=3) > cor$consensus.correlation [1] 0.5548806 > ADlmFit2 <- lmFit(ADraw.NormWA,ADdesign, ndups=3, correlation=cor$consensus.correlation) > ADlmFit2$correlation [1] 0.5548806 Questions: (1) Why I got different correlation value if I ran "duplicateCorrelation" firstly? For my slides with diplicate spots, Should I always calculate the correlation value using "duplicateCorrelation" before running "lmFit"? If I should use "duplicateCorrelation" only when there are duplicate spots within array? (2) If I ran "lmFit" only I always got correlation 0.75, I also ran other slides and got the same correlation 0.75, It looks that 0.75 is the default correlation value if I didn't give specific value to correlation. Why the "lmFit" always give correlation 0.75 for different data analysis? It looks not reasonable. I'm very confused. Thanks yi __________________________________________________________________ Switch to Netscape Internet Service. New! Netscape Toolbar for Internet Explorer Search from anywhere on the Web and block those annoying pop-ups. Download now at http://channels.netscape.com/ns/search/install.jsp

At 08:01 PM 8/04/2005, bioconductor-request@stat.math.ethz.ch wrote: >Date: Thu, 07 Apr 2005 12:28:58 -0400 >From: yzou1971@netscape.net >Subject: [BioC] Question about Correlation (Limma package) >To: bioconductor@stat.math.ethz.ch > >Hi, > >I have 4 replicate slides. Within each slide, there are 3 replicate spots >for each gene. After doing the within Array normalization, I ran >lmFit(limma) with two ways and get different correlation values as following: > >(1)lmFit1 > > > ADlmFit1 <- lmFit(ADraw.NormWA, ADdesign, ndups=3) > > ADlmFit1$correlation >[1] 0.75 > > >(2)lmFit2 > > > cor <- duplicateCorrelation(ADraw.NormWA,ADdesign, ndups=3) > > cor$consensus.correlation >[1] 0.5548806 > > ADlmFit2 <- lmFit(ADraw.NormWA,ADdesign, ndups=3, > correlation=cor$consensus.correlation) > > ADlmFit2$correlation >[1] 0.5548806 > >Questions: > >(1) Why I got different correlation value if I ran "duplicateCorrelation" >firstly? For my slides with diplicate spots, Should I always calculate the >correlation value using "duplicateCorrelation" before running "lmFit"? Yes, of course, that is what duplicateCorrelation() is provided for. Unless you want to just use the default correlation value. I prefer to separate the correlation estimation and the linear model fit into two separate functions because (i) both steps can be quite time consuming and (ii) I want you to look at the correlation value for reasonablenes before entering it into the linear model fit. >If I should use "duplicateCorrelation" only when there are duplicate spots >within array? Yes, naturally. If there are no duplicates, there is no correlation to estimate. >(2) If I ran "lmFit" only I always got correlation 0.75, I also ran other >slides and got the same correlation 0.75, It looks that 0.75 is the >default correlation value if I didn't give specific value to correlation. >Why the "lmFit" always give correlation 0.75 for different data analysis? >It looks not reasonable. I'm very confused. So you've done a lot of experiments and have decided that 0.75 must be the default value for 'correlation'. An alternate and less circuitious path might have been to read the help page. Typing args(lmFit) or ?lmFit would have told you immediately that 0.75 is the default value. Gordon >Thanks > >yi