Hi Nadia, Aubin-Horth Nadia wrote: > Hi James > > Thank you for your response. > > We do have a dye-swap, which I represented with the "=" symbol between > my biological replicates. Sorry for the confusion. > > Detailed experimental design (first sample is cy3, second sample is cy5): > slide 1: A1-D1 > slide 2: D1-A1 > slide 3: A2-D2 > slide 4: D2-A2 > slide 5: A3-D3 > slide 6: D3-A3 > slide 7: A4-D4 > slide 8: D4-A4 > slide 9: A5-D5 > slide 10: D5-A5 > slide 11: A6-D6 > slide 12: D6-A6 > > The comparison of interest is the average difference in expression > between group A and group D, taking into account technical and > biological replicates. A1 to A6 are different individuals of the group > A, and D1 to D6 are different individuals of the group D, and these > individuals (fish actually) are wild caught and may vary in gene > expression even within the group. > > In your message you say: > >> All you can do is compare the two sample types. Since >> the comparison is intrinsic to the data, all you want to do is compute >> the mean of the ratios (or log ratios, actually) and then test to see if >> the mean is different from zero. >> >> In this very simple case, you don't have to create a design matrix, as >> limma will produce one for you. So you just do: >> >> fit <- lmFit(MAptip.nba.scale) >> fit2 <- eBayes(fit) >> topTable(fit2) > > Reading the LIMMA User guide (section on technical replication, p.36), I > was under the impression that since I wanted to keep information for > each of the 6 biological replicates within a group in the test, as well > as include technical replicates which are not independent, I needed to > fit a linear model and then use a contrast. This is why we wanted to use > one of the individuals as a reference in the design matrix and carry on > with fitting the linear model and creating a contrast matrix that > represents the average difference between A and D. When I test > is.fullrank(design), I get "FALSE" as an answer. I suspect that our > experimental design is the cause, but I may be wrong. Yes, it is your design matrix. You don't show exactly what it looks like, but if it isn't of full rank it won't work regardless. I think you want to emulate the analysis on p37 (at least that's the page in my version of the Limma User's Guide). You have dye swaps and unlike the analysis I think you are following (which doesn't have equal technical replication), you have equal technical replication so can use duplicateCorrelation. The relevant section of the user's guide is this: "If the technical replicates were in dye-swap pairs as FileName Cy3 Cy5 File1 wt1 mu1 File2 mu1 wt1 File3 wt2 mu2 File4 mu2 wt2 then one might use > design <- c(1, -1, 1, -1) > corfit <- duplicateCorrelation(MA, design, ndups = 1, block = biolrep) > fit <- lmFit(MA, design, block = biolrep, cor = corfit$consensus) > fit <- eBayes(fit) > topTable(fit, adjust = "BH")" Where biolrep in this case is c(1,1,2,2). If you are concerned about dye-bias, you could augment the design matrix by adding a vector of all 1s as the first column. Best, Jim > > Thank you > > Nadia Aubin-Horth > Laval University > > On Dec 10, 2009, at 2:05 PM, James W. MacDonald wrote: > >> Hi Nadia, >> >> Aubin-Horth Nadia wrote: >>> We are analysing a cDNA microarray dataset in LIMMA with the following >>> design and we run into "Coefficients not estimable" comments. >>> >>> R = 2.9.0 >>> limma=2.18 >>> >>> We have two groups, "A" and "D" with 6 biological replicates each >>> (indicated in the targets file). We are interested in significant gene >>> expression differences between A and D on average. A given biological >>> replicate of "A" was compared to a biological replicate of "D", with a >>> dye-swap. >>> A1=D1 >>> A2=D2 >>> A3=D3 >>> A4=D4 >>> A5=D5 >>> A6=D6 >> >> I don't see a dye-swap here. >> >>> >>> Therefore, we have six mini-experiments that are not connected one to >>> the other. >>> >>> After normalisation, we use teh following design with the goal of doing >>> a contrast as shown below >>> >>> design <- modelMatrix(targets, ref="D1") >>> design <- cbind(Dye=1, design) >> >> Without a dye-swap (and you don't indicate one above), you cannot >> estimate dye bias. All you can do is compare the two sample types. Since >> the comparison is intrinsic to the data, all you want to do is compute >> the mean of the ratios (or log ratios, actually) and then test to see if >> the mean is different from zero. >> >> In this very simple case, you don't have to create a design matrix, as >> limma will produce one for you. So you just do: >> >> fit <- lmFit(MAptip.nba.scale) >> fit2 <- eBayes(fit) >> topTable(fit2) >> >> FYI, what you are doing is to treat each sample as if it isn't >> replicated, which is why you are running into problems. >> >> Best, >> >> Jim >> >> >>> >>> fit<-lmFit(MAptip.nba.scale,design) >>> >>> here we get: >>> Coefficients not estimable: D5 A4 D2 D6 D3 >>> >>> And we checked with >>>> is.fullrank(design) >>> and we get: >>> [1] FALSE >>> >>> Our question is, is our experimental design (non loop, non reference >>> design) with samples not being directly compared on a microarray causing >>> these non estimable coefficients? If so, is there a way that we can keep >>> the information on biological replicates and still use LIMMA? >>> >>> This is the contrast we were planning to use (which of course does not >>> work) >>> cont.matrix <- makeContrasts(AvsD = (A1 + A2 + A3 + A4 + A5 + A6 >>> - D2 - D3 - D4 - D5 - D6)/6, levels=design) >>> fit2 <- contrasts.fit(fit, cont.matrix) >>> Error in contrasts.fit(fit, cont.matrix) : >>> trying to take contrast of non-estimable coefficient >>> In addition: Warning message: >>> In any(contrasts[-est, ]) : coercing argument of type 'double' to >>> logical >>> fit3 <- eBayes(fit2) >>> >>> >>> Thank you >>> >>> Nadia Aubin-Horth >>> Assistant professor >>> Biology Department >>> Institut de Biologie Int?grative et des Syst?mes >>> Universit? Laval >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at stat.math.ethz.ch >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> -- >> James W. MacDonald, M.S. >> Biostatistician >> Douglas Lab >> University of Michigan >> Department of Human Genetics >> 5912 Buhl >> 1241 E. Catherine St. >> Ann Arbor MI 48109-5618 >> 734-615-7826 >> ********************************************************** >> Electronic Mail is not secure, may not be read every day, and should >> not be used for urgent or sensitive issues >> > -- James W. MacDonald, M.S. Biostatistician Douglas Lab University of Michigan Department of Human Genetics 5912 Buhl 1241 E. Catherine St. Ann Arbor MI 48109-5618 734-615-7826 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues