Dear Guillaume, On Mon, 23 May 2011, Guillaume Meurice wrote: > Dear Gordon, > > many thanks for your answer. > >> I am going to assume that the main purpose of your experiment is to >> find genes for which the d7 vs d1 response is different between the two >> groups of patients. > > you're right, this exactly what I'm looking for. > >> Since you have gone to the trouble of dye-swapping, you should also >> include an intercept term in the design matrix, so as to soak up any >> probe-specific dye effects: >> >> design <- cbind(Dye=1,design) > > I'll try this but is cost one degree of freedom (I guess ?). > Is it a could procedure to run the statistical test with the intercept > term, then eventually discard the dye-specific probes, and then > re-perform the test without the intercept ? I suppose that you could do this, but seems a lot of effort and risk for little gain. I think you can well afford the one df. >> Then >> >> cor <- duplicateCorrelation(MAn, design, weights=NULL, >> block=targets$TechnicalReplicat) > > Great !! Could I also use this approach to model my biological > replicates ? I mean, here, patients A, B and C (one side) and D,E,F > (other side) are biological replicates. Could I use something like : > > cor <- duplicateCorrelation(MAn, design, weights=NULL, block=targets$Issu) ? No, you can't. >> fit <- lmFit(MAn, design, weights=NULL, >> block=targets$TechnicalReplicat, correlation=cor$consensus) > > > then are the following line correct ? Yes, all the lines after this are fine. Best wishes Gordon > # computing the contrast matrix > cont.mat = makeContrasts(RvsNR = R_D7vsD1 - NR_D7vsD1, levels = design) > > # Fitting the model to the contrast matrix: > fit2 <- contrasts.fit(fit, cont.mat) > fit2 <- eBayes(fit2) > > # the probe list I want: > res.RvsNR = topTable(fit2,number=nrow(MAm), coef="RvsNR") > > # the dye-specific probes : > res.RvsNR = topTable(fit2,number=nrow(MAm), coef="Dye") > > > many thank again. > > > Best regards, > -- > Guillaume > >> Dear Guillaume, >> >> I am going to assume that the main purpose of your experiment is to find genes for which the d7 vs d1 response is different between the two groups of patients. >> >>> Date: Fri, 20 May 2011 15:44:45 +0200 >>> From: Guillaume Meurice <guillaume.meurice at="" igr.fr=""> >>> To: Guillaume Meurice <guillaume.meurice at="" igr.fr=""> >>> Cc: bioconductor at stat.math.ethz.ch >>> Subject: Re: [BioC] Help with >>> >>> Sorry, I made some mistake in my previous mail, into the contrast matrix (bad copy paste from another project), so here I have corrected them. >>> >>>> I have a question regarding the way to properly design biological replicate and technical replicates. >>>> >>>> In the projet, we have two groups of sample : R (respond to the treatment), NR (no response). For each groups, there is several replicates (3) that come from different patients (so not strictely biological). Each patient have two sample : one at day one(d1), the second after one week (d7) The hybridization are planned to be performed in dual color, with dye-sap. >>>> >>>> Here is the target file : >>>> >>>> Cy3 Cy5 Patient Issu TechnicalReplicat >>>> d1 d7 A R 1 >>>> d7 d1 A R 1 >>>> d1 d7 B R 2 >>>> d7 d1 B R 2 >>>> d1 d7 C R 3 >>>> d7 d1 C R 3 >>>> d1 d7 D NR 4 >>>> d7 d1 D NR 4 >>>> d1 d7 E NR 5 >>>> d7 d1 E NR 5 >>>> d1 d7 F NR 6 >>>> d7 d1 F NR 6 >>>> >>>> >>>> my question is how to properly design the matrix design and the contrast matrix to answer the difference between the two groups ? Which column should I use as biological replicat (so as I can use the duplicateCorrelation function) ? >>>> >>>> here is how I've started, but I can't figure out how to use duplicateCorrelation with this design >>>> >>>> design <- cbind( >>>> R_D7vsD1 = c(-1,1,-1,1,-1,1, 0,0, 0,0, 0,0), >>>> NR_D7vsD1 = c( 0,0,0,0,0,0,-1,1,-1,1,-1,1) >>>> ) >> >> Since you have gone to the trouble of dye-swapping, you should also include an intercept term in the design matrix, so as to soak up any probe-specific dye effects: >> >> design <- cbind(Dye=1,design) >> >> Then >> >> cor <- duplicateCorrelation(MAn, design, weights=NULL, >> block=targets$TechnicalReplicat) >> >> fit <- lmFit(MAn, design, weights=NULL, >> block=targets$TechnicalReplicat, correlation=cor$consensus) >> >> Best wishes >> Gordon >> >>>> fit <- lmFit(MAn, design, weights = NULL) >>>> cont.mat = makeContrasts(NRvsR = NR_D7sD1 - R_D7vsD1, levels = design) >>>> fit2 <- contrasts.fit(fit, cont.mat) >>>> fit2 <- eBayes(fit2) >>>> >>>> >>>> Many thanks by advance for any help. >>>> >>>> -- >>>> Guillaume ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}