HTqPCR and limma and factorial design with no replicates
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@andreia-fonseca-3796
Last seen 7.8 years ago
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]]
Microarray qPCR Classification limma HTqPCR ASSIGN Microarray qPCR Classification limma • 1.2k views
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Naomi Altman ★ 6.0k
@naomi-altman-380
Last seen 3.6 years ago
United States
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 at="" 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 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 > > > > > > > > > > > >-- >-------------------------------------------- >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 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
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