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]]

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 Cl?nica > Instituto de Medicina Molecular > Universidade de Lisboa > email: andreiaamaral at fm.ul.pt > andreia.fonseca at gmail.com > > [[alternative HTML version deleted]] > > _______________________________________________ > 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

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 at="" 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 at="" 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 at="" 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 Cl?nica >> >>> > Instituto de Medicina Molecular >> >>> > Universidade de Lisboa >> >>> > email: andreiaamaral at fm.ul.pt >> >>> > andreia.fonseca at gmail.com >> >>> > >> >>> > [[alternative HTML version deleted]] >> >>> > >> >>> > _______________________________________________ >> >>> > 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 >> >>> >> >>> >> >>> >> >> >> >> >> >> -- >> >> -------------------------------------------- >> >> Andreia J. Amaral >> >> Unidade de Imunologia Cl?nica >> >> Instituto de Medicina Molecular >> >> Universidade de Lisboa >> >> email: andreiaamaral at fm.ul.pt >> >> andreia.fonseca at gmail.com >> >> >> > >> > >> > >> > -- >> > -------------------------------------------- >> > Andreia J. Amaral >> > Unidade de Imunologia Cl?nica >> > Instituto de Medicina Molecular >> > Universidade de Lisboa >> > email: andreiaamaral at fm.ul.pt >> > andreia.fonseca at gmail.com >> > >> >> >> > > > -- > -------------------------------------------- > Andreia J. Amaral > Unidade de Imunologia Cl?nica > Instituto de Medicina Molecular > Universidade de Lisboa > email: andreiaamaral at fm.ul.pt > andreia.fonseca at gmail.com >