Charlotte, It looks like it might work ok, but I don't have time to check it out properly. Best wishes Gordon At 02:28 AM 21/09/2007, Charlotte Schjerling wrote: >Dear Gordon > >I find my set-up a little more complicated than described in the >Limma manual so I have to ask whether the following is correct. > >A text file with target information is read by: > >targets <- readTargets() > > > targets > SampleName Patient Treatment >1 AM92a A M.early >2 AM92b A M.early >3 AnM92a A nM.early >4 AnM92b A nM.early >5 AM01a A M.late >6 AM01b A M.late >7 AnM01a A nM.late >8 AnM01b A nM.late >9 BM92a B M.early >10 BM92b B M.early >11 BnM92a B nM.early >12 BnM92b B nM.early >13 BM02a B M.late >14 BM02b B M.late >15 BnM02a B nM.late >16 BnM02b B nM.late >17 DM88a C M.early >18 DM88b C M.early >19 DnM88a C nM.early >20 DnM88b C nM.early >21 DM97a C M.late >22 DM97b C M.late >23 DnM97a C nM.late >24 DnM97b C nM.late > >Patient <- factor(targets$Patient) >Treat <- factor(targets$Treatment) > >I do not want an intercept in my design matrix so I can either choose: > >design <- model.matrix(~0+Patient+Treat) > > design > PatientA PatientB PatientC TreatM.late TreatnM.early TreatnM.late >1 1 0 0 0 0 0 >2 1 0 0 0 0 0 >3 1 0 0 0 1 0 >4 1 0 0 0 1 0 >5 1 0 0 1 0 0 >6 1 0 0 1 0 0 >7 1 0 0 0 0 1 >8 1 0 0 0 0 1 >9 0 1 0 0 0 0 >10 0 1 0 0 0 0 >11 0 1 0 0 1 0 >12 0 1 0 0 1 0 >13 0 1 0 1 0 0 >14 0 1 0 1 0 0 >15 0 1 0 0 0 1 >16 0 1 0 0 0 1 >17 0 0 1 0 0 0 >18 0 0 1 0 0 0 >19 0 0 1 0 1 0 >20 0 0 1 0 1 0 >21 0 0 1 1 0 0 >22 0 0 1 1 0 0 >23 0 0 1 0 0 1 >24 0 0 1 0 0 1 >attr(,"assign") >[1] 1 1 1 2 2 2 >attr(,"contrasts") >attr(,"contrasts")$Patient >[1] "contr.treatment" > >attr(,"contrasts")$Treat >[1] "contr.treatment" > >or > >design <- model.matrix(~0+Treat+Patient) > > > design > TreatM.early TreatM.late TreatnM.early TreatnM.late PatientB PatientC >1 1 0 0 0 0 0 >2 1 0 0 0 0 0 >3 0 0 1 0 0 0 >4 0 0 1 0 0 0 >5 0 1 0 0 0 0 >6 0 1 0 0 0 0 >7 0 0 0 1 0 0 >8 0 0 0 1 0 0 >9 1 0 0 0 1 0 >10 1 0 0 0 1 0 >11 0 0 1 0 1 0 >12 0 0 1 0 1 0 >13 0 1 0 0 1 0 >14 0 1 0 0 1 0 >15 0 0 0 1 1 0 >16 0 0 0 1 1 0 >17 1 0 0 0 0 1 >18 1 0 0 0 0 1 >19 0 0 1 0 0 1 >20 0 0 1 0 0 1 >21 0 1 0 0 0 1 >22 0 1 0 0 0 1 >23 0 0 0 1 0 1 >24 0 0 0 1 0 1 >attr(,"assign") >[1] 1 1 1 1 2 2 >attr(,"contrasts") >attr(,"contrasts")$Treat >[1] "contr.treatment" > >attr(,"contrasts")$Patient >[1] "contr.treatment" > > >However, I think that only the latter can give me the contrasts that I want: > > > contrasts > Contrasts >Levels TreatM.early - TreatnM.early TreatM.late - >TreatnM.late TreatM.late - TreatM.early TreatnM.late - TreatnM.early > TreatM.early 1 0 > -1 0 > TreatM.late 0 1 > 1 0 > TreatnM.early -1 0 > 0 -1 > TreatnM.late 0 > -1 0 1 > PatientB 0 0 > 0 0 > PatientC 0 0 > 0 0 > > > >Will it be correct to use: design <- model.matrix(~0+Treat+Patient)? > >And then I continue with: > >biolrep <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12) >corfit <- duplicateCorrelation(eset, design, ndups=1, block=biolrep) >fit <- lmFit(eset, design, ndups=1, block=biolrep, cor=corfit$consensus) >contrasts <- makeContrasts(TreatM.early-TreatnM.early, >TreatM.late-TreatnM.late, TreatM.early-TreatM.late, >TreatnM.early-TreatnM.late, levels=design) >fit2 <- contrasts.fit(fit, contrasts) >fit2 <- eBayes(fit2) > >Will that give me the paired set-up so each of the 4 conditions are >treated as originating from the 3 patients (A, B, D)? > >Thanks, >Charlotte > >At 01:14 AM 9/7/2007, you wrote: >>Dear Charlotte, >> >>Paired samples are normally handled as described in Section 8.3 >>"Paired Samples" of the limma User's Guide. There shouldn't be any >>conflict between this and your technical replicates. >> >>Best wishes >>Gordon >> >>>Date: Wed, 05 Sep 2007 17:54:42 +0200 >>>From: Charlotte Schjerling <araneus at="" mrna.dk=""> >>>Subject: [BioC] LIMMA: paired samples AND technical replicates >>>To: bioconductor at stat.math.ethz.ch >>>Message-ID: <20070905155442.ADC4E5C29 at ns1-int.rh.dk> >>>Content-Type: text/plain; charset=us-ascii; format=flowed >>> >>>I have tried to search the BioC archives but have not found posts >>>dealing with both paired samples and technical replicates in a setup >>>like mine. >>> >>>I have 3 patients A, B, and C from which I have 4 samples: M.early, >>>nM.early, M.late, and nM.late. Each sample has a technical >>>replicate resulting in 24 arrays (Affymetrix). >>> >>>biolrep Patient M.early nM.early M.late nM.late >>>1 A 1 0 0 0 >>>1 A 1 0 0 0 >>>2 A 0 1 0 0 >>>2 A 0 1 0 0 >>>3 A 0 0 1 0 >>>3 A 0 0 1 0 >>>4 A 0 0 0 1 >>>4 A 0 0 0 1 >>>5 B 1 0 0 0 >>>5 B 1 0 0 0 >>>6 B 0 1 0 0 >>>6 B 0 1 0 0 >>>7 B 0 0 1 0 >>>7 B 0 0 1 0 >>>8 B 0 0 0 1 >>>8 B 0 0 0 1 >>>9 C 1 0 0 0 >>>9 C 1 0 0 0 >>>10 C 0 1 0 0 >>>10 C 0 1 0 0 >>>11 C 0 0 1 0 >>>11 C 0 0 1 0 >>>12 C 0 0 0 1 >>>12 C 0 0 0 1 >>> >>>I would like to find the following 4 contrasts: >>> >>>1) M.early - nM.early >>>2) M.late - nM.late >>>3) M.early - M.late >>>4) nM.early - nM.late >>> >>>biolrep <- c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12) >>>corfit <- duplicateCorrelation(eset, design, ndups=1, block=biolrep) >>> >>>If I ignore the pairing part I have constructed the following design matrix: >>> >>> > design >>> M.early nM.early M.late nM.late >>>1 1 0 0 0 >>>2 1 0 0 0 >>>3 0 1 0 0 >>>4 0 1 0 0 >>>5 0 0 1 0 >>>6 0 0 1 0 >>>7 0 0 0 1 >>>8 0 0 0 1 >>>9 1 0 0 0 >>>10 1 0 0 0 >>>11 0 1 0 0 >>>12 0 1 0 0 >>>13 0 0 1 0 >>>14 0 0 1 0 >>>15 0 0 0 1 >>>16 0 0 0 1 >>>17 1 0 0 0 >>>18 1 0 0 0 >>>19 0 1 0 0 >>>20 0 1 0 0 >>>21 0 0 1 0 >>>22 0 0 1 0 >>>23 0 0 0 1 >>>24 0 0 0 1 >>>attr(,"assign") >>>[1] 1 1 1 1 >>>attr(,"contrasts") >>>attr(,"contrasts")$f >>>[1] "contr.treatment" >>> >>> >>>fit <- lmFit(eset, design, ndups=1, block=biolrep, cor=corfit$consensus) >>>contrasts <- makeContrasts(M.early-nM.early, M.late-nM.late, >>>M.early-M.late, nM.early-nM.late, levels=design) >>>fit2 <- contrasts.fit(fit, contrasts) >>>fit2 <- eBayes(fit2) >>> >>>However, I would like to include the pairing by patient into the >>>analyses and that I cannot figure out how to do. >>> >>>Hope someone can help, >>> >>>Charlotte