Dear Gordon, I have another question about this analysis. Previously I performed an analysis on the same data but without incorporating effects of patient. My design matrix had columns: "Disease1.Treat", "Disease1.Control", "Healthy.Treat", "Healthy.Control", and I then tested for genes showing a significant interaction between disease and treatment using the contrast ((Disease1.Treat - Disease1.Control) - (Healthy.Treat - Healthy.Control)). I think this is what is explained on pages 25-26 of the edgeR users guide (Oct 27 2012 version). Now I want to take into account patient effects as well, so I have my design matrix with columns: [1] "(Intercept)" "DiseaseDisease1" [3] "DiseaseHealthy:Patient2" "DiseaseDisease1:Patient2" [5] "DiseaseHealthy:Patient3" "DiseaseDisease1:Patient3" [7] "DiseaseDisease1:Patient4" "DiseaseHealthy:TreatmentTreat" [9] "DiseaseDisease1:TreatmentTreat" Reading the explanation on pages 32-33 of the users guide, to do the equivalent contrast to find genes showing significant interaction between disease and treatment, should I simply use: lrt <- glmLRT(fit, contrast=c(0,0,0,0,0,0,0,-1,1)) ? I think this is what the guide is saying, but I just want to make sure... Thanks and best wishes, Maria On 07/11/2012 22:55, Gordon K Smyth wrote: > Dear Maria, > > Sounds ok from what you say not to collapse libraries. However, if > the three treated cultures and three untreated cultures for one > patient are truly three pairs, then this pairing should be reflected > in the analysis. You can handle this by numbering the samples by > paired culture from 1 to 7 instead of numbering by patient. > > An MDS plot could guide you in judging whether there are baseline > differences between the different pairs for one patient, and hence > whether your pairing should be by culture instead of by patient. > > Best wishes > Gordon > > --------------------------------------------- > Professor Gordon K Smyth, > Bioinformatics Division, > Walter and Eliza Hall Institute of Medical Research, > 1G Royal Parade, Parkville, Vic 3052, Australia. > http://www.statsci.org/smyth > > On Wed, 7 Nov 2012, Maria Keays wrote: > >> Dear Gordon, >> >> Thanks very much for the helpful advice. I'm treating them as >> biological replicates -- they are cell cultures and it's just that I >> have multiple separately treated/untreated pairs of cultures from >> some patients and only one treated/untreated pair for others. So >> although some cultures came from the same patient, they were all >> treated separately and then RNA was extracted from each culture. >> Would you say that's the right thing to do? >> >> Thanks and best wishes, >> Maria >> >> >> On 07/11/2012 00:01, Gordon K Smyth wrote: >>> Dear Maria, >>> >>> Thanks for the specific reference to the documentation that you've >>> followed. >>> >>> Yes, you are correct, the error is arising because there is no 4th >>> patient in the healthy group. If you have a look at your design >>> matrix, you will see that there is a column called >>> DiseaseHealthy:Patient4 that consists entirely of zeros. It should >>> be column 8, but check: >>> >>> design[,8] >>> >>> The easiest way to proceed is simply to remove that column manually >>> from the design matrix: >>> >>> design2 <- design[,-8] >>> >>> Your experiment has another issue, in that you have repeat samples >>> on several of the patients. Are these biological replicates? If >>> not, if they are just technical replicates, then they should be >>> collapsed into one library before analysis. >>> >>> Best wishes >>> Gordon >>> >>>> Date: Tue, 06 Nov 2012 09:19:08 +0000 >>>> From: Maria Keays <mkeays at="" ebi.ac.uk=""> >>>> To: bioconductor at r-project.org >>>> Subject: Re: [BioC] EdgeR: paired samples together with independant >>>> samples >>>> >>>> Hello, >>>> >>>> I read this thread and related user guide material with interest >>>> because >>>> I am working with a very similar data set with paired samples. >>>> However, >>>> I'm having trouble which I think stems from my data being >>>> unbalanced? I >>>> have four patients with a disease and three without, and within >>>> that for >>>> some patients I have replicates but for others I do not. I've >>>> created a >>>> design matrix as described on p32 of the 27 October 2012 edgeR user's >>>> guide, but when I try to estimate the common dispersion using >>>> estimateGLMCommonDisp() it tells me: >>>> >>>> "Error in glmFit.default(y, design = design, dispersion = dispersion, >>>> offset = offset) : >>>> Design matrix not of full rank. The following coefficients not >>>> estimable: >>>> DiseaseHealthy:Patient4" >>>> >>>> I guess because I have 4 patients in the diseased set and only 3 in >>>> the >>>> healthy set? If I remove Patient4 and try again, I'm able to continue >>>> the analysis successfully, but I'd obviously like to be able to >>>> include >>>> all the data -- is that possible? If so, could you explain how to >>>> do it? >>>> >>>> The original annotations for my data are below: >>>> >>>> Disease Patient Treatment >>>> disease1 1 control >>>> disease1 1 control >>>> disease1 1 control >>>> disease1 2 control >>>> disease1 3 control >>>> disease1 3 control >>>> disease1 4 control >>>> disease1 1 treat >>>> disease1 1 treat >>>> disease1 1 treat >>>> disease1 2 treat >>>> disease1 3 treat >>>> disease1 3 treat >>>> disease1 4 treat >>>> healthy 5 control >>>> healthy 6 control >>>> healthy 6 control >>>> healthy 6 control >>>> healthy 7 control >>>> healthy 7 control >>>> healthy 5 treat >>>> healthy 6 treat >>>> healthy 6 treat >>>> healthy 6 treat >>>> healthy 7 treat >>>> healthy 7 treat >>>> >>>> As I was following the user's guide I amended the "Patient" labels >>>> so it >>>> looked like this when I created the design matrix: >>>> >>>> Disease Patient Treatment >>>> disease1 1 control >>>> disease1 1 control >>>> disease1 1 control >>>> disease1 2 control >>>> disease1 3 control >>>> disease1 3 control >>>> disease1 4 control >>>> disease1 1 treat >>>> disease1 1 treat >>>> disease1 1 treat >>>> disease1 2 treat >>>> disease1 3 treat >>>> disease1 3 treat >>>> disease1 4 treat >>>> healthy 1 control >>>> healthy 2 control >>>> healthy 2 control >>>> healthy 2 control >>>> healthy 3 control >>>> healthy 3 control >>>> healthy 1 treat >>>> healthy 2 treat >>>> healthy 2 treat >>>> healthy 2 treat >>>> healthy 3 treat >>>> healthy 3 treat >>>> >>>> Thanks! >>>> Maria >>>> >>>> >>>> On 25/10/2012 06:18, Gordon K Smyth wrote: >>>>> Dear Anna, >>>>> >>>>> You are right to recognise that the analysis of this sort of >>>>> design is >>>>> more complex than many other experiments, because it includes >>>>> comparisons both within and between patients. I have included a new >>>>> section in the edgeR User's Guide based on your experiment that >>>>> describes the analysis. This will appear in the official release of >>>>> edgeR in a couple of days. In the meantime, see pages 31-33 of: >>>>> >>>>> http://bioinf.wehi.edu.au/software/edgeR/edgeRUsersGuide.pdf >>>>> >>>>> Best wishes >>>>> Gordon >>>>> >>>>>> Date: Tue, 23 Oct 2012 06:37:44 -0700 (PDT) >>>>>> From: "anna [guest]" <guest at="" bioconductor.org=""> >>>>>> To: bioconductor at r-project.org, m.nadira at yahoo.fr >>>>>> Subject: [BioC] EdgeR: paired samples together with independant >>>>>> samples >>>>>> >>>>>> >>>>>> Hello, >>>>>> I am using EdgeR to analyse my RNAseq data. >>>>>> >>>>>> I have: >>>>>> >>>>>> cells from 3 healthy patients , either treated or not with a >>>>>> hormone . >>>>>> >>>>>> cells from 3 patients with disease D1, either treated or not with >>>>>> the >>>>>> hormone >>>>>> >>>>>> cells from 3 patients with disease D2, either treated or not with >>>>>> the >>>>>> hormone. >>>>>> >>>>>> I would like to know what is wrong in the response to the hormone in >>>>>> patients with disease D1 and D2. >>>>>> >>>>>> I don't know how to combine paired comparisons, with pairwise >>>>>> comparisons, in a unique glm analysis. >>>>>> >>>>>> thank you very much, >>>>>> anna >>>>>> >>>>>> -- output of sessionInfo(): >>>>>> >>>>>> R version 2.15.1 (2012-06-22) >>>>>> Platform: i386-pc-mingw32/i386 (32-bit) >>>>>> >>>>>> locale: >>>>>> [1] LC_COLLATE=French_France.1252 LC_CTYPE=French_France.1252 >>>>>> [3] LC_MONETARY=French_France.1252 LC_NUMERIC=C >>>>>> [5] LC_TIME=French_France.1252 >>>>>> >>>>>> attached base packages: >>>>>> [1] stats graphics grDevices utils datasets methods base >>>>>> >>>>>> loaded via a namespace (and not attached): >>>>>> [1] tools_2.15.1 >>>>>> > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:6}}