Hi Naomi, Thanks for your answer, well my question is in such design NS.control NS.H1 NS.H2 S.control2 S.H1 S.H2 >> > > 1 1 0 0 0 0 0 >> > > 2 0 1 0 0 0 0 >> > > 3 0 0 1 0 0 0 >> > > 4 0 0 0 1 0 0 >> > > 5 0 0 0 0 1 0 >> > > 6 0 0 0 0 0 1 how can I test the differences between NS.H1 and S.H1 and the differences between NS.H2 and S.H2 if I cannot fit the interaction? I found a package of empirical Bayes analysis for unreplicated microarray data (Cho et al. 2009), do you think it might be adequate? Thanks for your opinion With kind regards, Andreia On Mon, Feb 1, 2010 at 9:13 PM, Naomi Altman <naomi@stat.psu.edu> wrote: > If you fit the full model then you have no d.f. for testing. In a > randomized complete block design you have replication of the blocks. > If you have this type of replication, of course you should fit an RCBD, but > this was not in your description of the experiment. > > Regards, > Naomi > > > At 06:17 AM 2/1/2010, Andreia Fonseca wrote: > >> Dear Naomi, >> >> Sorry I made a mistake, I really want to fit the interaction model, I was >> tired and I forgot to add the interaction term. If I have two factors I >> have >> to count for the effects of each and the interaction right? Why did I I >> runned of d.? How can I analyze this if the problem is no replicates? Can >> I >> use a random block design? >> >> thanks for your opinion >> >> Kind regards, >> Andreia >> >> On Sat, Jan 30, 2010 at 3:08 AM, Naomi Altman <naomi@stat.psu.edu> wrote: >> >> > The reason you have no d.f. for testing is that you did not fit the >> model >> > below. You fitted the model with a separate mean for each treatment >> > combination, which is equivalent to fitting the model which includes >> > interaction. So you used up all of your d.f. >> > >> > Regards, >> > Naomi >> > >> > >> > At 11:32 AM 1/29/2010, Andreia Fonseca wrote: >> > >> >> Dear list, >> >> >> >> >> >> I am going to analyze data with the design shown below. I do not have >> >> replicates, but is a complete 2x3 factorial design and therefore I >> should >> >> be >> >> able to fit a linear model >> >> >> >> y=mean+Celltype+Treatment+error for each of gene and then do multiple >> >> testing correction, right? So, why after running, >> >> >> >> TS<-paste(raw_files$Cell_Type, raw_files$Treatment, sep=".") >> >> > TS<-factor(TS, levels=c("NS.control1", "NS.H1", >> >> > "NS.H2","S.control2","S.H1"," >> >> S.H2")) >> >> > design<-model.matrix(~0+TS) >> >> > contrasts<-makeContrasts(TSS.H1-TSS.H2, TSNS.H1-TSNS.H2, >> >> > (TSNS.H1-TSNS.H2)-(TSS.H1-TSS.H2),levels=design) >> >> > sr.norm2<-sr.norm[order(featureNames(sr.norm)),] >> >> > qDE.limma<-limmaCtData(sr.norm2,design=design,contrasts=contrasts, >> >> > spacing=1) >> >> > >> >> > I am getting an error message >> >> > >> >> > Error in ebayes(fit = fit, proportion = proportion, stdev.coef.lim = >> >> > stdev.coef.lim) : >> >> > No residual degrees of freedom in linear model fits >> >> >> >> >> >> I get this error? >> >> >> >> should I only make a two sample t-test? and I can't fit a linear model? >> >> >> >> Thanks for your help! >> >> >> >> Kind regards >> >> >> >> Andreia >> >> >> >> >> >> >> >> On Thu, Jan 28, 2010 at 9:54 PM, Heidi Dvinge <heidi@ebi.ac.uk> wrote: >> >> >> >> > Hi Andreia, >> >> > >> >> > > Hello Heidi, >> >> > > >> >> > > my question is about the classification of unreliable estimates e >> >> > > setCategory. I saw the code and it seems that you are estimating >> just >> >> the >> >> > > classical c.i. based in the variation of the data, right? >> >> > >> >> > yep >> >> > >> >> > > I have a new >> >> > > question though concerning qDE.limma, as I told you I have a >> factorial >> >> > > design, so I have created a factorial design form the example data >> >> from >> >> > > HT-qPCR >> >> > > >> >> > > TSNS.control1 TSNS.H1 TSNS.H2 TSS.control2 TSS.H1 TSS.H2 >> >> > > 1 1 0 0 0 0 0 >> >> > > 2 0 1 0 0 0 0 >> >> > > 3 0 0 1 0 0 0 >> >> > > 4 0 0 0 1 0 0 >> >> > > 5 0 0 0 0 1 0 >> >> > > 6 0 0 0 0 0 1 >> >> > > >> >> > > TS<-paste(raw_files$Cell_Type, raw_files$Treatment, sep=".") >> >> > > TS<-factor(TS, levels=c("NS.control1", "NS.H1", >> >> > > "NS.H2","S.control2","S.H1","S.H2")) >> >> > > design<-model.matrix(~0+TS) >> >> > > contrasts<-makeContrasts(TSS.H1-TSS.H2, TSNS.H1-TSNS.H2, >> >> > > (TSNS.H1-TSNS.H2)-(TSS.H1-TSS.H2),levels=design) >> >> > > sr.norm2<-sr.norm[order(featureNames(sr.norm)),] >> >> > > qDE.limma<-limmaCtData(sr.norm2,design=design,contrasts=contrasts, >> >> > > spacing=1) >> >> > > >> >> > > I am getting an error message >> >> > > >> >> > > Error in ebayes(fit = fit, proportion = proportion, stdev.coef.lim >> = >> >> > > stdev.coef.lim) : >> >> > > No residual degrees of freedom in linear model fits >> >> > > >> >> > > >> >> > > is it because this that is not adequate for this design? or I have >> the >> >> > > wrong >> >> > > command? >> >> > > >> >> > Your approach as such seems valid, but the problem is that you have >> no >> >> > replicate arrays. Which means that unfortunately the limma functions >> >> won't >> >> > work, since there's no way to do statistical testing. >> >> > >> >> > Maybe someone on the list can help if we know a little more about >> your >> >> > arrays/intention. It looks e.g. like you don't use your two control >> >> > samples at all? >> >> > >> >> > Cheers >> >> > \Heidi >> >> > >> >> > > thanks Andreia >> >> > > >> >> > > >> >> > > >> >> > > >> >> > > On Wed, Jan 27, 2010 at 11:26 PM, Heidi Dvinge <heidi@ebi.ac.uk> >> >> wrote: >> >> > > >> >> > >> Hello Andreia, >> >> > >> >> >> > >> sorry for the delay in answering. Just to be clear, what >> confidence >> >> > >> interval are you referring to? The confidence intervals plotted in >> >> > >> plotCtOverview? The deviations used to assign categories in then >> >> > >> filtering >> >> > >> the data? >> >> > >> >> >> > >> Cheers >> >> > >> \Heidi >> >> > >> >> >> > >> P.S. By the way, just in case you're new to R, you can always see >> the >> >> > >> source code of a function, by just typing the function name in the >> >> > >> terminal, without "?" before or "()" after. >> >> > >> >> >> > >> > Hi Heidi, >> >> > >> > >> >> > >> > >> >> > >> > sorry is just to say that I have tested to read the file with >> two >> >> > >> columns >> >> > >> > for the two factors and that part works!!! So now I will move on >> >> for >> >> > >> the >> >> > >> > other functions, plotCtOverview works fine! Now can you just >> answer >> >> to >> >> > >> the >> >> > >> > confidence intervals question? >> >> > >> > >> >> > >> > Thanks >> >> > >> > Andreia >> >> > >> > On Wed, Jan 27, 2010 at 10:24 AM, Andreia Fonseca >> >> > >> > <andreia.fonseca@gmail.com>> >> > >> >> wrote: >> >> > >> > >> >> > >> >> Hi Heidi, >> >> > >> >> >> >> > >> >> >> >> > >> >> my question is how should be the format of the files.txt file, >> so >> >> > >> that >> >> > >> >> HTqPCR can read in the information of the two different >> factors, >> >> > >> should >> >> > >> >> it >> >> > >> >> be like the example I wrote below? And what about the >> confidence >> >> > >> >> intervals >> >> > >> >> to filter data, how does the package estimates them? >> >> > >> >> Cheers >> >> > >> >> Andreia >> >> > >> >> >> >> > >> >> >> >> > >> >> On Tue, Jan 26, 2010 at 10:23 PM, Heidi Dvinge < >> heidi@ebi.ac.uk> >> >> > >> wrote: >> >> > >> >> >> >> > >> >>> Hello Andreia, >> >> > >> >>> >> >> > >> >>> actually, HTqPCR can handle multi-factor design, there's just >> no >> >> > >> >>> example >> >> > >> >>> of that in the vignette (will consider adding it in the next >> >> > >> revision). >> >> > >> >>> >> >> > >> >>> The function limmaCtData takes all the arguments that you'd >> use >> >> if >> >> > >> you >> >> > >> >>> were analysing microarray data using lmFit and contrasts.fit >> from >> >> > >> the >> >> > >> >>> limma package. You need to specify the design and contrast >> >> matrix >> >> > >> >>> yourself though. As I recall, the limma user's guide has a >> couple >> >> of >> >> > >> >>> example involving factorial design. >> >> > >> >>> >> >> > >> >>> HTH >> >> > >> >>> \Heidi >> >> > >> >>> >> >> > >> >>> > Dear list, >> >> > >> >>> > >> >> > >> >>> > Soon I will receive data from the qpcr Exicon platform to >> >> analyze >> >> > >> and >> >> > >> >>> I >> >> > >> >>> > have >> >> > >> >>> > been playing around with HTqPCR package, however from the >> >> vignete, >> >> > >> it >> >> > >> >>> > seems >> >> > >> >>> > that is only capable of deleting with data design of one >> >> factor, >> >> > >> how >> >> > >> >>> do >> >> > >> >>> I >> >> > >> >>> > handle it with a factorial design, namely how do I read the >> >> data >> >> > >> and >> >> > >> >>> > create >> >> > >> >>> > the model.matrix, the examples only consider one factor. >> >> Another >> >> > >> >>> question >> >> > >> >>> > is >> >> > >> >>> > concerning part 5 of the vignete, how are the Confidence >> values >> >> > >> >>> estimated? >> >> > >> >>> > is is based in the variance of the data? >> >> > >> >>> > >> >> > >> >>> > In order for you to understand my doubts here is my design >> is a >> >> > >> 2x3 >> >> > >> >>> > design, >> >> > >> >>> > I don't have replicates, but each file has the CT values for >> >> each >> >> > >> >>> gene >> >> > >> >>> > frome >> >> > >> >>> > pooled RNA of 10 patients. >> >> > >> >>> > factor1 factor2 >> >> > >> >>> > file1.txt NS C >> >> > >> >>> > file2.txt NS H1 >> >> > >> >>> > file3.txt NS H2 >> >> > >> >>> > file4.txt S C >> >> > >> >>> > file5.txt S H1 >> >> > >> >>> > file6.txt S H2 >> >> > >> >>> > >> >> > >> >>> > >> >> > >> >>> > Thanks for your help. >> >> > >> >>> > with kind regards, >> >> > >> >>> > Andreia >> >> > >> >>> > >> >> > >> >>> > >> >> > >> >>> > -- >> >> > >> >>> > -------------------------------------------- >> >> > >> >>> > Andreia J. Amaral >> >> > >> >>> > Unidade de Imunologia Clínica >> >> > >> >>> > Instituto de Medicina Molecular >> >> > >> >>> > Universidade de Lisboa >> >> > >> >>> > email: andreiaamaral@fm.ul.pt >> >> > >> >>> > andreia.fonseca@gmail.com >> >> > >> >>> > >> >> > >> >>> > [[alternative HTML version deleted]] >> >> > >> >>> > >> >> > >> >>> > _______________________________________________ >> >> > >> >>> > Bioconductor mailing list >> >> > >> >>> > Bioconductor@stat.math.ethz.ch >> >> > >> >>> > https://stat.ethz.ch/mailman/listinfo/bioconductor >> >> > >> >>> > Search the archives: >> >> > >> >>> > >> >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> > >> >>> >> >> > >> >>> >> >> > >> >>> >> >> > >> >> >> >> > >> >> >> >> > >> >> -- >> >> > >> >> -------------------------------------------- >> >> > >> >> Andreia J. Amaral >> >> > >> >> Unidade de Imunologia Clínica >> >> > >> >> Instituto de Medicina Molecular >> >> > >> >> Universidade de Lisboa >> >> > >> >> email: andreiaamaral@fm.ul.pt >> >> > >> >> andreia.fonseca@gmail.com >> >> > >> >> >> >> > >> > >> >> > >> > >> >> > >> > >> >> > >> > -- >> >> > >> > -------------------------------------------- >> >> > >> > Andreia J. Amaral >> >> > >> > Unidade de Imunologia Clínica >> >> > >> > Instituto de Medicina Molecular >> >> > >> > Universidade de Lisboa >> >> > >> > email: andreiaamaral@fm.ul.pt >> >> > >> > andreia.fonseca@gmail.com >> >> > >> > >> >> > >> >> >> > >> >> >> > >> >> >> > > >> >> > > >> >> > > -- >> >> > > -------------------------------------------- >> >> > > Andreia J. Amaral >> >> > > Unidade de Imunologia Clínica >> >> > > Instituto de Medicina Molecular >> >> > > Universidade de Lisboa >> >> > > email: andreiaamaral@fm.ul.pt >> >> > > andreia.fonseca@gmail.com >> >> > > >> >> > >> >> > >> >> > >> >> >> >> >> >> -- >> >> -------------------------------------------- >> >> Andreia J. Amaral >> >> Unidade de Imunologia Clínica >> >> Instituto de Medicina Molecular >> >> Universidade de Lisboa >> >> email: andreiaamaral@fm.ul.pt >> >> andreia.fonseca@gmail.com >> >> >> >> [[alternative HTML version deleted]] >> >> >> >> _______________________________________________ >> >> Bioconductor mailing list >> >> Bioconductor@stat.math.ethz.ch >> >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> >> Search the archives: >> >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> >> > >> > Naomi S. Altman 814-865-3791 (voice) >> > Associate Professor >> > Dept. of Statistics 814-863-7114 (fax) >> > Penn State University 814-865-1348 (Statistics) >> > University Park, PA 16802-2111 >> > >> > >> >> >> -- >> -------------------------------------------- >> Andreia J. Amaral >> Unidade de Imunologia Clínica >> Instituto de Medicina Molecular >> Universidade de Lisboa >> email: andreiaamaral@fm.ul.pt >> andreia.fonseca@gmail.com >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > > Naomi S. Altman 814-865-3791 (voice) > Associate Professor > Dept. of Statistics 814-863-7114 (fax) > Penn State University 814-865-1348 (Statistics) > University Park, PA 16802-2111 > > -- -------------------------------------------- Andreia J. Amaral Unidade de Imunologia Clínica Instituto de Medicina Molecular Universidade de Lisboa email: andreiaamaral@fm.ul.pt andreia.fonseca@gmail.com [[alternative HTML version deleted]]