I really hesitate to explain how to average of duplicate spots using limma, because it is not something I generally recommend. It is however quite easy: Start with a normalized MAList object, 'MA', possibly containing spot quality weights. Suppose that there are 'ndups' duplicates at spacing 'spacing'. You can average over duplicates by fit1 <- lmFit(MA, design=diag(ncol(MA)), ndups=ndups, spacing=spacing, correlation=0) Now the averaged log-ratios are in Y <- fit1$coef and the consolidated spot quality weights are in w <- 1/fit1$stdev.unscaled^2 Now you can fit any model you like to the averaged log-ratios, e.g., fit <- lmFit(Y, design, weights=w) etc At 05:22 PM 19/04/2005, Ron Ophir wrote: > >>>> "Gordon K Smyth" <smyth@wehi.edu.au> 04/18/05 2:24 PM >>> > >> Date: Sun, 17 Apr 2005 17:24:14 +0300 > >> From: "Ron Ophir" <ron.ophir@weizmann.ac.il> > >> Subject: [BioC] technical replicates and spots in limma > >> To: <bioconductor@stat.math.ethz.ch> > >> > >> Dear limma experts, > >> I have direct experiments with two biological replicates and two > >> technical replicates. In each array sots are printted in 4 >replicates. > >> In duplicateCorrelation help it is written that "At this time it is >not > >> possible to estimate correlations between duplicate spots and >between > >> technical replicates simultaneously." > >> The question is it possible to average on both technical and spot > >> replicates but not simultaneously and if yes then how? > >> If not which least-squares analysis should I drop technical sample > >> replicates or spots replicates? > > > >The between spot correlation is usually in the range 0.5-0.9. >Correlations between technical > >replicates are usually not so strong, seldom higher than around >0.2-0.3 and often less. > > > > >If you're going to ignore one of these correlations, it should be the >technical replication. If > >you're going to average over one of the replicate structures, it >should be over the replicate > >spots. > >Thanks. Averaging over spot replicates using duplicateCorrelation() >assuming equal space between replicates coordinates or I can give a >vector of spots location like in block for technical replicates. >If the latter is not possible, does the following commands are what >should be done: >spotRep<-as.factor(c(1,1,2,2,3,1,1,3,3,2,2,3,...)) >vvRaw$R<-unlist(by(vvRaw$R,spotRep,mean)) >vvRaw$G<-unlist(by(vvRaw$G,spotRep,mean)) >vvRaw$Rb<-unlist(by(vvRaw$Rb,spotRep,mean)) >vvRaw$Gb<-unlist(by(vvRaw$Gb,spotRep,mean)) No, this won't work. Apart from anything else, the averaging should occur on the M-values rather than raw R, G, Rb and Gb intensities. See above. Gordon >Ron > > > > >The measurement error is often larger than the biological variation, >so that treating the > >technical replicates as biological replicates is often not as bad as >it sounds. This is what I > >would usually do, having checked the between technical rep correlation >is not large. > > > >Gordon > > > >> Thanks in advance > >> Ron