Agilent G4112A Arrays
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Chuming Chen ▴ 140
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Dear All, I am trying to find out the differentially expressed genes from some Agilent Human Whole Genome (G4112A) Arrays data. I have tried LIMMA package, but LIMMA gave the error message "no residual degrees of freedom in linear model fits" and stopped. My guess is that my data has no replicates in the experiment. Is there any other packages I can use to find differentially expressed genes which does not require replicates in the experiment? Thanks for your help. Chuming
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Hi Prashantha, Thank you for your suggestion. My target file is as below. Although I couldn't fit a linear model, I still wonder whether I can do some statistic on M (log ratio) values and use the p-value to get the differentially expressed genes. SlideNumber FileName Cy3 Cy5 1 B1vsT1.txt B1 T1 2 B2vsT2.txt B2 T2 3 B3vsT3.txt B3 T3 4 B4vsT4.txt B4 T4 5 B5vsT5.txt B5 T5 Chuming Prashantha Hebbar wrote: > Dear Chen, > > You need not to look for any other packages. Since, you do not have > any replicates, do not fit linear model, instead just do normalization > with in arrays and look at the M (log ratio) values. > > Regards, > > Prashantha Hebbar Kiradi, > Dept. of Biotechnology, > Manipal Life Sciences Center, > Manipal University, > Manipal, India > > > --- On *Mon, 1/25/10, Chuming Chen /<chumingchen at="" gmail.com="">/* wrote: > > > From: Chuming Chen <chumingchen at="" gmail.com=""> > Subject: [BioC] Agilent G4112A Arrays > To: bioconductor at stat.math.ethz.ch > Date: Monday, January 25, 2010, 6:32 AM > > Dear All, > > I am trying to find out the differentially expressed genes from > some Agilent Human Whole Genome (G4112A) Arrays data. > > I have tried LIMMA package, but LIMMA gave the error message "no > residual degrees of freedom in linear model fits" and stopped. My > guess is that my data has no replicates in the experiment. > > Is there any other packages I can use to find differentially > expressed genes which does not require replicates in the experiment? > > Thanks for your help. > > Chuming > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > </mc> > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > >
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The more data one has, the fewer assumptions one needs. In the absence of replication, you cannot get p-values without very strong assumptions. e.g. you could assume that the vast majority of the genes do not differentially express, that their M-values have equal variance and that the M-values are normally distributed. Then you could use e.g. the IQR of the M-values to estimate the sd and use this to pick a fold cut-off for DE. You have no reasonable way to estimate FDR with this approach, but it might be slightly better than using 2-fold - or then again, it might not. Without replication, there is no way to know. Regards, Naomi Altman At 08:53 AM 1/25/2010, Chuming Chen wrote: >Hi Prashantha, > >Thank you for your suggestion. My target file is as below. Although >I couldn't fit a linear model, I still wonder whether I can do some >statistic on M (log ratio) values and use the p-value to get the >differentially expressed genes. > >SlideNumber FileName Cy3 Cy5 >1 B1vsT1.txt B1 T1 >2 B2vsT2.txt B2 T2 >3 B3vsT3.txt B3 T3 >4 B4vsT4.txt B4 T4 >5 B5vsT5.txt B5 T5 > >Chuming > > >Prashantha Hebbar wrote: >>Dear Chen, >> >>You need not to look for any other packages. Since, you do not have >>any replicates, do not fit linear model, instead just do >>normalization with in arrays and look at the M (log ratio) values. >> >>Regards, >> >>Prashantha Hebbar Kiradi, >>Dept. of Biotechnology, >>Manipal Life Sciences Center, >>Manipal University, >>Manipal, India >> >> >>--- On *Mon, 1/25/10, Chuming Chen /<chumingchen at="" gmail.com="">/* wrote: >> >> >> From: Chuming Chen <chumingchen at="" gmail.com=""> >> Subject: [BioC] Agilent G4112A Arrays >> To: bioconductor at stat.math.ethz.ch >> Date: Monday, January 25, 2010, 6:32 AM >> >> Dear All, >> >> I am trying to find out the differentially expressed genes from >> some Agilent Human Whole Genome (G4112A) Arrays data. >> >> I have tried LIMMA package, but LIMMA gave the error message "no >> residual degrees of freedom in linear model fits" and stopped. My >> guess is that my data has no replicates in the experiment. >> >> Is there any other packages I can use to find differentially >> expressed genes which does not require replicates in the experiment? >> >> Thanks for your help. >> >> Chuming >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at stat.math.ethz.ch >> </mc> >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > >_______________________________________________ >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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Hi Chuming if you want to work with the approximation that M-values have equal variances, then preprocessing the data with a method that provides variance stabilisation (e.g. vsn) will likely be useful. Furthermore, it might be useful to discard a fraction of genes with low A-values, since they are more likely to be either not expressed, or so weakly expressed that you would find it more difficult to validate them. Best wishes Wolfgang Naomi Altman wrote: > The more data one has, the fewer assumptions one needs. In the absence > of replication, you cannot get p-values without very strong > assumptions. e.g. you could assume that the vast majority of the genes > do not differentially express, that their M-values have equal variance > and that the M-values are normally distributed. Then you could use e.g. > the IQR of the M-values to estimate the sd and use this to pick a fold > cut-off for DE. You have no reasonable way to estimate FDR with this > approach, but it might be slightly better than using 2-fold - or then > again, it might not. Without replication, there is no way to know. > > Regards, > Naomi Altman > > > At 08:53 AM 1/25/2010, Chuming Chen wrote: >> Hi Prashantha, >> >> Thank you for your suggestion. My target file is as below. Although I >> couldn't fit a linear model, I still wonder whether I can do some >> statistic on M (log ratio) values and use the p-value to get the >> differentially expressed genes. >> >> SlideNumber FileName Cy3 Cy5 >> 1 B1vsT1.txt B1 T1 >> 2 B2vsT2.txt B2 T2 >> 3 B3vsT3.txt B3 T3 >> 4 B4vsT4.txt B4 T4 >> 5 B5vsT5.txt B5 T5 >> >> Chuming >> >> >> Prashantha Hebbar wrote: >>> Dear Chen, >>> >>> You need not to look for any other packages. Since, you do not have >>> any replicates, do not fit linear model, instead just do >>> normalization with in arrays and look at the M (log ratio) values. >>> >>> Regards, >>> >>> Prashantha Hebbar Kiradi, >>> Dept. of Biotechnology, >>> Manipal Life Sciences Center, >>> Manipal University, >>> Manipal, India >>> >>> >>> --- On *Mon, 1/25/10, Chuming Chen /<chumingchen at="" gmail.com="">/* wrote: >>> >>> >>> From: Chuming Chen <chumingchen at="" gmail.com=""> >>> Subject: [BioC] Agilent G4112A Arrays >>> To: bioconductor at stat.math.ethz.ch >>> Date: Monday, January 25, 2010, 6:32 AM >>> >>> Dear All, >>> >>> I am trying to find out the differentially expressed genes from >>> some Agilent Human Whole Genome (G4112A) Arrays data. >>> >>> I have tried LIMMA package, but LIMMA gave the error message "no >>> residual degrees of freedom in linear model fits" and stopped. My >>> guess is that my data has no replicates in the experiment. >>> >>> Is there any other packages I can use to find differentially >>> expressed genes which does not require replicates in the experiment? >>> >>> Thanks for your help. >>> >>> Chuming >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at stat.math.ethz.ch >>> </mc> >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >> >> _______________________________________________ >> 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 > > _______________________________________________ > 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 -- Best wishes Wolfgang -- Wolfgang Huber EMBL http://www.embl.de/research/units/genome_biology/huber/contact
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Prashantha and all, Here is the sessional information regarding my analysis of this data set. Can you point out what I might do wrong? Thanks, Chuming > library(limma) > > targets <- readTargets("Targets.txt") > targets SlideNumber Name FileName Cy3 Cy5 1 1 B1vsT1 US23502303_251239134396_S02_44k.txt B1 T1 2 2 B2vsT2 US23502303_251239134397_S01_44k.txt B2 T2 3 3 B3vsT3 US23502303_251239134398_S01_44k.txt B3 T3 4 4 B4vsT4 US23502303_251239134399_S01_44k.txt B4 T4 5 5 B5vsT5 US23502303_251239134400_S01_44k.txt B5 T5 > > RG <- read.maimages(targets, source="agilent") Read US23502303_251239134396_S02_44k.txt Read US23502303_251239134397_S01_44k.txt Read US23502303_251239134398_S01_44k.txt Read US23502303_251239134399_S01_44k.txt Read US23502303_251239134400_S01_44k.txt > > RG <- backgroundCorrect(RG, method="normexp", offset=50) Green channel Corrected array 1 Corrected array 2 Corrected array 3 Corrected array 4 Corrected array 5 Red channel Corrected array 1 Corrected array 2 Corrected array 3 Corrected array 4 Corrected array 5 > > plotDensities(RG) > > MA <- normalizeBetweenArrays(RG,method="vsn") Loading required package: vsn Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material. To view, type 'openVignette()'. To cite Bioconductor, see 'citation("Biobase")' and for packages 'citation(pkgname)'. vsn2: 43931 x 10 matrix (1 stratum). Please use 'meanSdPlot' to verify the fit. > > plotDensities(MA) > > f<-factor(targets$Name) > design<-model.matrix(~0+f) > design fB1vsT1 fB2vsT2 fB3vsT3 fB4vsT4 fB5vsT5 1 1 0 0 0 0 2 0 1 0 0 0 3 0 0 1 0 0 4 0 0 0 1 0 5 0 0 0 0 1 attr(,"assign") [1] 1 1 1 1 1 attr(,"contrasts") attr(,"contrasts")$f [1] "contr.treatment" > colnames(design) <- levels(f) > design B1vsT1 B2vsT2 B3vsT3 B4vsT4 B5vsT5 1 1 0 0 0 0 2 0 1 0 0 0 3 0 0 1 0 0 4 0 0 0 1 0 5 0 0 0 0 1 attr(,"assign") [1] 1 1 1 1 1 attr(,"contrasts") attr(,"contrasts")$f [1] "contr.treatment" > > fit<-lmFit(MA, design) > contrasts.matrix <- makeContrasts(B1vsT1,B2vsT2, B3vsT3, B4vsT4, B5vsT5, levels=design) > > fit2 <- contrasts.fit(fit, contrasts.matrix) > fit2 <- eBayes(fit2) Error in ebayes(fit = fit, proportion = proportion, stdev.coef.lim = stdev.coef.lim) : No residual degrees of freedom in linear model fits > > toptable(fit2) Error in ebayes(fit, ...) : No residual degrees of freedom in linear model fits > Prashantha Hebbar wrote: > Hi Chuming, > > As per your experimental information, you have replicates. Because, > you have samples from same tissue with 2 different region across > all patients. So, you should be able to fit linear model. What I > guess, there is something wrong in your analysis steps. So, better to > send sessional information to list. > > Regards, > Prashantha > > Prashantha Hebbar Kiradi, > Dept. of Biotechnology, > Manipal Life Sciences Center, > Manipal University, > Manipal, India > Email:prashantha.hebbar at manipal.edu > > --- On *Mon, 1/25/10, Wolfgang Huber /<whuber at="" embl.de="">/* wrote: > > > From: Wolfgang Huber <whuber at="" embl.de=""> > Subject: Re: [BioC] Agilent G4112A Arrays > To: "Naomi Altman" <naomi at="" stat.psu.edu=""> > Cc: "Chuming Chen" <chumingchen at="" gmail.com="">, "Prashantha Hebbar" > <prashantha.hebbar at="" yahoo.com="">, bioconductor at stat.math.ethz.ch > Date: Monday, January 25, 2010, 8:06 PM > > Hi Chuming > > if you want to work with the approximation that M-values have > equal variances, then preprocessing the data with a method that > provides variance stabilisation (e.g. vsn) will likely be useful. > > Furthermore, it might be useful to discard a fraction of genes > with low A-values, since they are more likely to be either not > expressed, or so weakly expressed that you would find it more > difficult to validate them. > > Best wishes > Wolfgang > > Naomi Altman wrote: > > The more data one has, the fewer assumptions one needs. In the > absence of replication, you cannot get p-values without very > strong assumptions. e.g. you could assume that the vast majority > of the genes do not differentially express, that their M-values > have equal variance and that the M-values are normally > distributed. Then you could use e.g. the IQR of the M-values to > estimate the sd and use this to pick a fold cut-off for DE. You > have no reasonable way to estimate FDR with this approach, but it > might be slightly better than using 2-fold - or then again, it > might not. Without replication, there is no way to know. > > > > Regards, > > Naomi Altman > > > > > > At 08:53 AM 1/25/2010, Chuming Chen wrote: > >> Hi Prashantha, > >> > >> Thank you for your suggestion. My target file is as below. > Although I couldn't fit a linear model, I still wonder whether I > can do some statistic on M (log ratio) values and use the p-value > to get the differentially expressed genes. > >> > >> SlideNumber FileName Cy3 Cy5 > >> 1 B1vsT1.txt B1 T1 > >> 2 B2vsT2.txt B2 T2 > >> 3 B3vsT3.txt B3 T3 > >> 4 B4vsT4.txt B4 T4 > >> 5 B5vsT5.txt B5 T5 > >> > >> Chuming > >> > >> > >> Prashantha Hebbar wrote: > >>> Dear Chen, > >>> > >>> You need not to look for any other packages. Since, you do not > have any replicates, do not fit linear model, instead just do > normalization with in arrays and look at the M (log ratio) values. > >>> > >>> Regards, > >>> > >>> Prashantha Hebbar Kiradi, > >>> Dept. of Biotechnology, > >>> Manipal Life Sciences Center, > >>> Manipal University, > >>> Manipal, India > >>> > >>> > >>> --- On *Mon, 1/25/10, Chuming Chen /<chumingchen at="" gmail.com=""> <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="chumingchen" at="" gmail.com="">>/* > wrote: > >>> > >>> > >>> From: Chuming Chen <chumingchen at="" gmail.com=""> <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="chumingchen" at="" gmail.com="">> > >>> Subject: [BioC] Agilent G4112A Arrays > >>> To: bioconductor at stat.math.ethz.ch > <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="bioconductor" at="" stat.math.ethz.ch=""> > >>> Date: Monday, January 25, 2010, 6:32 AM > >>> > >>> Dear All, > >>> > >>> I am trying to find out the differentially expressed genes > from > >>> some Agilent Human Whole Genome (G4112A) Arrays data. > >>> > >>> I have tried LIMMA package, but LIMMA gave the error > message "no > >>> residual degrees of freedom in linear model fits" and > stopped. My > >>> guess is that my data has no replicates in the experiment. > >>> > >>> Is there any other packages I can use to find differentially > >>> expressed genes which does not require replicates in the > experiment? > >>> > >>> Thanks for your help. > >>> > >>> Chuming > >>> > >>> _______________________________________________ > >>> Bioconductor mailing list > >>> Bioconductor at stat.math.ethz.ch > <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="Bioconductor" at="" stat.math.ethz.ch=""> > >>> </mc> <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="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 > >>> > >> > >> _______________________________________________ > >> Bioconductor mailing list > >> Bioconductor at stat.math.ethz.ch > <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="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 > <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 > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor at stat.math.ethz.ch > <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="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 > <http: news.gmane.org="" gmane.science.biology.informatics.conductor=""> > > -- > Best wishes > Wolfgang > > > -- > Wolfgang Huber > EMBL > http://www.embl.de/research/units/genome_biology/huber/contact > > >
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Hi Chuming, Would you mind explaining a bit more what the samples are? What you are describing below is either a very simple experiment with 5 biological replicates or a rather strange one that tries to test 5 conditions at the same time with 5 different controls. If the former, then you can set up your design matrix to show this. You would lose the matched nature of your data, but you should get some decent results using limma. If the latter, then whatever statistics you would do on the M values would be a bit strange as they would all represent different treatments. Francois > Hi Prashantha, > > Thank you for your suggestion. My target file is as below. Although I > couldn't fit a linear model, I still wonder whether I can do some > statistic on M (log ratio) values and use the p-value to get the > differentially expressed genes. > > SlideNumber FileName Cy3 Cy5 > 1 B1vsT1.txt B1 T1 > 2 B2vsT2.txt B2 T2 > 3 B3vsT3.txt B3 T3 > 4 B4vsT4.txt B4 T4 > 5 B5vsT5.txt B5 T5 > > Chuming > > > Prashantha Hebbar wrote: >> Dear Chen, >> >> You need not to look for any other packages. Since, you do not have >> any replicates, do not fit linear model, instead just do normalization >> with in arrays and look at the M (log ratio) values. >> >> Regards, >> >> Prashantha Hebbar Kiradi, >> Dept. of Biotechnology, >> Manipal Life Sciences Center, >> Manipal University, >> Manipal, India >> >> >> --- On *Mon, 1/25/10, Chuming Chen /<chumingchen at="" gmail.com="">/* wrote: >> >> >> From: Chuming Chen <chumingchen at="" gmail.com=""> >> Subject: [BioC] Agilent G4112A Arrays >> To: bioconductor at stat.math.ethz.ch >> Date: Monday, January 25, 2010, 6:32 AM >> >> Dear All, >> >> I am trying to find out the differentially expressed genes from >> some Agilent Human Whole Genome (G4112A) Arrays data. >> >> I have tried LIMMA package, but LIMMA gave the error message "no >> residual degrees of freedom in linear model fits" and stopped. My >> guess is that my data has no replicates in the experiment. >> >> Is there any other packages I can use to find differentially >> expressed genes which does not require replicates in the experiment? >> >> Thanks for your help. >> >> Chuming >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at stat.math.ethz.ch >> </mc> >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> > > _______________________________________________ > 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 >
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Hi Francois: The experiment was done five years old. I am just trying to do some analysis. This experiment was performed on human colonic crypts. They were microdissected into two parts - the top 9/10 and the bottom 1/10. Thus in my target file, T or B stands for top or bottom and the number after it represents patients 1-5. I guess it belongs to the strange one you mentioned. I am trying to find out the differentially expressed genes for at least B1 vs T1, B2 vs T2 etc. There is probably no way to find out the differentially expressed genes for other pairs of contrasts. Thanks, Chuming francois at sus.mcgill.ca wrote: > Hi Chuming, > > Would you mind explaining a bit more what the samples are? What you are > describing below is either a very simple experiment with 5 biological > replicates or a rather strange one that tries to test 5 conditions at the > same time with 5 different controls. > > If the former, then you can set up your design matrix to show this. You > would lose the matched nature of your data, but you should get some decent > results using limma. > > If the latter, then whatever statistics you would do on the M values would > be a bit strange as they would all represent different treatments. > > Francois > > >> Hi Prashantha, >> >> Thank you for your suggestion. My target file is as below. Although I >> couldn't fit a linear model, I still wonder whether I can do some >> statistic on M (log ratio) values and use the p-value to get the >> differentially expressed genes. >> >> SlideNumber FileName Cy3 Cy5 >> 1 B1vsT1.txt B1 T1 >> 2 B2vsT2.txt B2 T2 >> 3 B3vsT3.txt B3 T3 >> 4 B4vsT4.txt B4 T4 >> 5 B5vsT5.txt B5 T5 >> >> Chuming >> >> >> Prashantha Hebbar wrote: >> >>> Dear Chen, >>> >>> You need not to look for any other packages. Since, you do not have >>> any replicates, do not fit linear model, instead just do normalization >>> with in arrays and look at the M (log ratio) values. >>> >>> Regards, >>> >>> Prashantha Hebbar Kiradi, >>> Dept. of Biotechnology, >>> Manipal Life Sciences Center, >>> Manipal University, >>> Manipal, India >>> >>> >>> --- On *Mon, 1/25/10, Chuming Chen /<chumingchen at="" gmail.com="">/* wrote: >>> >>> >>> From: Chuming Chen <chumingchen at="" gmail.com=""> >>> Subject: [BioC] Agilent G4112A Arrays >>> To: bioconductor at stat.math.ethz.ch >>> Date: Monday, January 25, 2010, 6:32 AM >>> >>> Dear All, >>> >>> I am trying to find out the differentially expressed genes from >>> some Agilent Human Whole Genome (G4112A) Arrays data. >>> >>> I have tried LIMMA package, but LIMMA gave the error message "no >>> residual degrees of freedom in linear model fits" and stopped. My >>> guess is that my data has no replicates in the experiment. >>> >>> Is there any other packages I can use to find differentially >>> expressed genes which does not require replicates in the experiment? >>> >>> Thanks for your help. >>> >>> Chuming >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at stat.math.ethz.ch >>> </mc> >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >>> >>> >>> >> _______________________________________________ >> 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 >> >> > > > >
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Hi Cunming, I see. You will indeed be stuck at looking at M values for any single patient. One way would be to put them all in limma first to get some basic statistics and then just look at the M values for the pairs of interest. Then you can ask for say how many have an M value over a given threshold or something like that. Francois On Jan 25, 2010, at 9:50 AM, Chuming Chen wrote: > Hi Francois: > > The experiment was done five years old. I am just trying to do some > analysis. > > This experiment was performed on human colonic crypts. They were > microdissected into two parts - the top 9/10 and the bottom 1/10. > Thus in my target file, T or B stands for top or bottom and the > number after it represents patients 1-5. I guess it belongs to the > strange one you mentioned. > > I am trying to find out the differentially expressed genes for at > least B1 vs T1, B2 vs T2 etc. There is probably no way to find out > the differentially expressed genes for other pairs of contrasts. > > Thanks, > > Chuming > > francois at sus.mcgill.ca wrote: >> Hi Chuming, >> >> Would you mind explaining a bit more what the samples are? What you >> are >> describing below is either a very simple experiment with 5 biological >> replicates or a rather strange one that tries to test 5 conditions >> at the >> same time with 5 different controls. >> >> If the former, then you can set up your design matrix to show this. >> You >> would lose the matched nature of your data, but you should get some >> decent >> results using limma. >> >> If the latter, then whatever statistics you would do on the M >> values would >> be a bit strange as they would all represent different treatments. >> >> Francois >> >> >>> Hi Prashantha, >>> >>> Thank you for your suggestion. My target file is as below. >>> Although I >>> couldn't fit a linear model, I still wonder whether I can do some >>> statistic on M (log ratio) values and use the p-value to get the >>> differentially expressed genes. >>> >>> SlideNumber FileName Cy3 Cy5 >>> 1 B1vsT1.txt B1 T1 >>> 2 B2vsT2.txt B2 T2 >>> 3 B3vsT3.txt B3 T3 >>> 4 B4vsT4.txt B4 T4 >>> 5 B5vsT5.txt B5 T5 >>> >>> Chuming >>> >>> >>> Prashantha Hebbar wrote: >>> >>>> Dear Chen, >>>> >>>> You need not to look for any other packages. Since, you do not have >>>> any replicates, do not fit linear model, instead just do >>>> normalization >>>> with in arrays and look at the M (log ratio) values. >>>> >>>> Regards, >>>> >>>> Prashantha Hebbar Kiradi, >>>> Dept. of Biotechnology, >>>> Manipal Life Sciences Center, >>>> Manipal University, >>>> Manipal, India >>>> >>>> >>>> --- On *Mon, 1/25/10, Chuming Chen /<chumingchen at="" gmail.com="">/* >>>> wrote: >>>> >>>> >>>> From: Chuming Chen <chumingchen at="" gmail.com=""> >>>> Subject: [BioC] Agilent G4112A Arrays >>>> To: bioconductor at stat.math.ethz.ch >>>> Date: Monday, January 25, 2010, 6:32 AM >>>> >>>> Dear All, >>>> >>>> I am trying to find out the differentially expressed genes from >>>> some Agilent Human Whole Genome (G4112A) Arrays data. >>>> >>>> I have tried LIMMA package, but LIMMA gave the error message "no >>>> residual degrees of freedom in linear model fits" and stopped. >>>> My >>>> guess is that my data has no replicates in the experiment. >>>> >>>> Is there any other packages I can use to find differentially >>>> expressed genes which does not require replicates in the >>>> experiment? >>>> >>>> Thanks for your help. >>>> >>>> Chuming >>>> >>>> _______________________________________________ >>>> Bioconductor mailing list >>>> Bioconductor at stat.math.ethz.ch >>>> </mc> >>>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>>> Search the archives: >>>> http://news.gmane.org/ >>>> gmane.science.biology.informatics.conductor >>>> >>>> >>>> >>> _______________________________________________ >>> 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 >>> >>> >> >> >> >>
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@prashantha-hebbar-3526
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Hi Chuming, I have over looked your previous mail. It seems, there is nothing wrong. So, better to follow Wolfgang and Naomi suggestions. Regards, Prashantha Prashantha Hebbar Kiradi, Dept. of Biotechnology, Manipal Life Sciences Center, Manipal University, Manipal, India Email:prashantha.hebbar@manipal.edu --- On Thu, 1/28/10, Chuming Chen <chumingchen@gmail.com> wrote: From: Chuming Chen <chumingchen@gmail.com> Subject: Re: [BioC] Agilent G4112A Arrays To: "Prashantha Hebbar" <prashantha.hebbar@yahoo.com> Cc: bioconductor@stat.math.ethz.ch Date: Thursday, January 28, 2010, 4:50 AM Prashantha and all, Here is the sessional information regarding my analysis of this data set. Can you point out what I might do wrong? Thanks, Chuming > library(limma) > > targets <- readTargets("Targets.txt") > targets SlideNumber���Name� � � � � � � � � � � � � � FileName Cy3 Cy5 1� � � � ���1 B1vsT1 US23502303_251239134396_S02_44k.txt� B1� T1 2� � � � ���2 B2vsT2 US23502303_251239134397_S01_44k.txt� B2� T2 3� � � � ���3 B3vsT3 US23502303_251239134398_S01_44k.txt� B3� T3 4� � � � ���4 B4vsT4 US23502303_251239134399_S01_44k.txt� B4� T4 5� � � � ���5 B5vsT5 US23502303_251239134400_S01_44k.txt� B5� T5 > > RG <- read.maimages(targets, source="agilent") Read US23502303_251239134396_S02_44k.txt Read US23502303_251239134397_S01_44k.txt Read US23502303_251239134398_S01_44k.txt Read US23502303_251239134399_S01_44k.txt Read US23502303_251239134400_S01_44k.txt > > RG <- backgroundCorrect(RG, method="normexp", offset=50) Green channel Corrected array 1 Corrected array 2 Corrected array 3 Corrected array 4 Corrected array 5 Red channel Corrected array 1 Corrected array 2 Corrected array 3 Corrected array 4 Corrected array 5 > > plotDensities(RG) > > MA <- normalizeBetweenArrays(RG,method="vsn") Loading required package: vsn Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material. To view, type 'openVignette()'. To cite Bioconductor, see 'citation("Biobase")' and for packages 'citation(pkgname)'. vsn2: 43931 x 10 matrix (1 stratum). Please use 'meanSdPlot' to verify the fit. > > plotDensities(MA) > > f<-factor(targets$Name) > design<-model.matrix(~0+f) > design fB1vsT1 fB2vsT2 fB3vsT3 fB4vsT4 fB5vsT5 1� � ���1� � ���0� � ���0� � ���0� � ���0 2� � ���0� � ���1� � ���0� � ���0� � ���0 3� � ���0� � ���0� � ���1� � ���0� � ���0 4� � ���0� � ���0� � ���0� � ���1� � ���0 5� � ���0� � ���0� � ���0� � ���0� � ���1 attr(,"assign") [1] 1 1 1 1 1 attr(,"contrasts") attr(,"contrasts")$f [1] "contr.treatment" > colnames(design) <- levels(f) > design B1vsT1 B2vsT2 B3vsT3 B4vsT4 B5vsT5 1� � � 1� � � 0� � � 0� � � 0� � � 0 2� � � 0� � � 1� � � 0� � � 0� � � 0 3� � � 0� � � 0� � � 1� � � 0� � � 0 4� � � 0� � � 0� � � 0� � � 1� � � 0 5� � � 0� � � 0� � � 0� � � 0� � � 1 attr(,"assign") [1] 1 1 1 1 1 attr(,"contrasts") attr(,"contrasts")$f [1] "contr.treatment" > > fit<-lmFit(MA, design) > contrasts.matrix <- makeContrasts(B1vsT1,B2vsT2, B3vsT3, B4vsT4, B5vsT5, levels=design) > > fit2 <- contrasts.fit(fit, contrasts.matrix) > fit2 <- eBayes(fit2) Error in ebayes(fit = fit, proportion = proportion, stdev.coef.lim = stdev.coef.lim) : No residual degrees of freedom in linear model fits > > toptable(fit2) Error in ebayes(fit, ...) : No residual degrees of freedom in linear model fits > Prashantha Hebbar wrote: > Hi Chuming, >� As per your experimental information, you have replicates. Because, you have samples from same tissue with 2 different region across all patients..� So, you should be able to fit linear model. What I guess, there is something wrong in your analysis steps. So, better to send sessional information to list. >� Regards, > Prashantha > > Prashantha Hebbar Kiradi, > Dept. of Biotechnology, > Manipal Life Sciences Center, > Manipal University, > Manipal, India > Email:prashantha.hebbar@manipal.edu > > --- On *Mon, 1/25/10, Wolfgang Huber /<whuber@embl.de>/* wrote: > > >� ���From: Wolfgang Huber <whuber@embl.de> >� ���Subject: Re: [BioC] Agilent G4112A Arrays >� ���To: "Naomi Altman" <naomi@stat.psu.edu> >� ���Cc: "Chuming Chen" <chumingchen@gmail.com>, "Prashantha Hebbar" >� ���<prashantha.hebbar@yahoo.com>, bioconductor@stat.math.ethz.ch >� ���Date: Monday, January 25, 2010, 8:06 PM > >� ���Hi Chuming > >� ���if you want to work with the approximation that M-values have >� ���equal variances, then preprocessing the data with a method that >� ���provides variance stabilisation (e.g. vsn) will likely be useful. > >� ���Furthermore, it might be useful to discard a fraction of genes >� ���with low A-values, since they are more likely to be either not >� ���expressed, or so weakly expressed that you would find it more >� ���difficult to validate them. > >� � � ���Best wishes >� � � ���Wolfgang > >� ���Naomi Altman wrote: >� ���> The more data one has, the fewer assumptions one needs.� In the >� ���absence of replication, you cannot get p-values without very >� ���strong assumptions.� e.g. you could assume that the vast majority >� ���of the genes do not differentially express, that their M-values >� ���have equal variance and that the M-values are normally >� ���distributed.� Then you could use e.g. the IQR of the M-values to >� ���estimate the sd and use this to pick a fold cut-off for DE.� You >� ���have no reasonable way to estimate FDR with this approach, but it >� ���might be slightly better than using 2-fold - or then again, it >� ���might not.� Without replication, there is no way to know. >� ���> >� ���> Regards, >� ���> Naomi Altman >� ���> >� ���> >� ���> At 08:53 AM 1/25/2010, Chuming Chen wrote: >� ���>> Hi Prashantha, >� ���>> >� ���>> Thank you for your suggestion. My target file is as below. >� ���Although I couldn't fit a linear model, I still wonder whether I >� ���can do some statistic on M (log ratio) values and use the p-value >� ���to get the differentially expressed genes. >� ���>> >� ���>> SlideNumber� � FileName� � Cy3� � Cy5 >� ���>> 1� � B1vsT1.txt� � B1� � T1 >� ���>> 2� � B2vsT2.txt� � B2� � T2 >� ���>> 3� � B3vsT3.txt� � B3� � T3 >� ���>> 4� � B4vsT4.txt� � B4� � T4 >� ���>> 5� � B5vsT5.txt� � B5� � T5 >� ���>> >� ���>> Chuming >� ���>> >� ���>> >� ���>> Prashantha Hebbar wrote: >� ���>>> Dear Chen, >� ���>>> >� ���>>> You need not to look for any other packages. Since, you do not >� ���have any replicates, do not fit linear model, instead just do >� ���normalization with in arrays and look at the M (log ratio) values. >� ���>>> >� ���>>> Regards, >� ���>>> >� ���>>> Prashantha Hebbar Kiradi, >� ���>>> Dept. of Biotechnology, >� ���>>> Manipal Life Sciences Center, >� ���>>> Manipal University, >� ���>>> Manipal, India >� ���>>> >� ���>>> >� ���>>> --- On *Mon, 1/25/10, Chuming Chen /<chumingchen@gmail.com>� ���<http: us.mc1101.mail.yahoo.com="" mc="" compose?to="chumingchen@gmail" .com="">>/* >� ���wrote: >� ���>>> >� ���>>> >� ���>>>� ���From: Chuming Chen <chumingchen@gmail.com>� ���<http: us.mc1101.mail.yahoo.com="" mc="" compose?to="chumingchen@gmail" .com="">> >� ���>>>� ���Subject: [BioC] Agilent G4112A Arrays >� ���>>>� ���To: bioconductor@stat.math.ethz.ch >� ���<http: us.mc1101.mail.yahoo.com="" mc="" compose?to="bioconductor@stat" .math.ethz.ch=""> >� ���>>>� ���Date: Monday, January 25, 2010, 6:32 AM >� ���>>> >� ���>>>� ���Dear All, >� ���>>> >� ���>>>� ���I am trying to find out the differentially expressed genes >� ���from >� ���>>>� ���some Agilent Human Whole Genome (G4112A) Arrays data. >� ���>>> >� ���>>>� ���I have tried LIMMA package, but LIMMA gave the error >� ���message "no >� ���>>>� ���residual degrees of freedom in linear model fits" and >� ���stopped. My >� ���>>>� ���guess is that my data has no replicates in the experiment. >� ���>>> >� ���>>>� ���Is there any other packages I can use to find differentially >� ���>>>� ���expressed genes which does not require replicates in the >� ���experiment? >� ���>>> >� ���>>>� ���Thanks for your help. >� ���>>> >� ���>>>� ���Chuming >� ���>>> >� ���>>>� ���_______________________________________________ >� ���>>>� ���Bioconductor mailing list >� ���>>>� ���Bioconductor@stat.math.ethz.ch >� ���<http: us.mc1101.mail.yahoo.com="" mc="" compose?to="Bioconductor@stat" .math.ethz.ch=""> >� ���>>>� ���</mc>� ���<http: us.mc1101.mail.yahoo.com="" mc="" compose?to="Bioconductor@stat" .math.ethz.ch="">> >� ���>>>� ���https://stat.ethz.ch/mailman/listinfo/bioconductor >� ���>>>� ���Search the archives: >� ���>>>� � � � http://news.gmane.org/gmane.science.biology.informatics.conductor >� ���>>> >� ���>> >� ���>> _______________________________________________ >� ���>> Bioconductor mailing list >� ���>> Bioconductor@stat.math.ethz.ch >� ���<http: us.mc1101.mail.yahoo.com="" mc="" compose?to="Bioconductor@stat" .math.ethz.ch=""> >� ���>> https://stat.ethz.ch/mailman/listinfo/bioconductor >� ���>> Search the archives: >� ���http://news.gmane.org/gmane.science.biology.informatics.conductor >� ���<http: news..gmane.org="" gmane.science.biology.informatics.conduc="" tor=""> >� ���> >� ���> 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 >� ���> >� ���> _______________________________________________ >� ���> Bioconductor mailing list >� ���> Bioconductor@stat.math.ethz.ch >� ���<http: us.mc1101.mail.yahoo.com="" mc="" compose?to="Bioconductor@stat" .math.ethz.ch=""> >� ���> https://stat.ethz.ch/mailman/listinfo/bioconductor >� ���> Search the archives: >� ���http://news.gmane.org/gmane.science.biology..informatics.conductor >� ���<http: news.gmane.org="" gmane.science.biology.informatics.conductor=""> > >� ���--� ���Best wishes >� � � � � Wolfgang > > >� ���-- >� ���Wolfgang Huber >� ���EMBL >� ���http://www.embl.de/research/units/genome_biology/huber/contact > > > [[alternative HTML version deleted]]
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Prashantha and everybody comments on my question: Thank you very much! Chuming Prashantha Hebbar wrote: > Hi Chuming, > > I have over looked your previous mail. It seems, there is nothing > wrong. So, better to follow Wolfgang and Naomi suggestions. > > Regards, > Prashantha > > Prashantha Hebbar Kiradi, > Dept. of Biotechnology, > Manipal Life Sciences Center, > Manipal University, > Manipal, India > Email:prashantha.hebbar at manipal.edu > > > --- On *Thu, 1/28/10, Chuming Chen /<chumingchen at="" gmail.com="">/* wrote: > > > From: Chuming Chen <chumingchen at="" gmail.com=""> > Subject: Re: [BioC] Agilent G4112A Arrays > To: "Prashantha Hebbar" <prashantha.hebbar at="" yahoo.com=""> > Cc: bioconductor at stat.math.ethz.ch > Date: Thursday, January 28, 2010, 4:50 AM > > Prashantha and all, > > Here is the sessional information regarding my analysis of this > data set. > > Can you point out what I might do wrong? > > Thanks, > > Chuming > > > library(limma) > > > > targets <- readTargets("Targets.txt") > > targets > SlideNumber Name FileName Cy3 Cy5 > 1 1 B1vsT1 US23502303_251239134396_S02_44k.txt B1 T1 > 2 2 B2vsT2 US23502303_251239134397_S01_44k.txt B2 T2 > 3 3 B3vsT3 US23502303_251239134398_S01_44k.txt B3 T3 > 4 4 B4vsT4 US23502303_251239134399_S01_44k.txt B4 T4 > 5 5 B5vsT5 US23502303_251239134400_S01_44k.txt B5 T5 > > > > RG <- read.maimages(targets, source="agilent") > Read US23502303_251239134396_S02_44k.txt > Read US23502303_251239134397_S01_44k.txt > Read US23502303_251239134398_S01_44k.txt > Read US23502303_251239134399_S01_44k.txt > Read US23502303_251239134400_S01_44k.txt > > > > RG <- backgroundCorrect(RG, method="normexp", offset=50) > Green channel > Corrected array 1 > Corrected array 2 > Corrected array 3 > Corrected array 4 > Corrected array 5 > Red channel > Corrected array 1 > Corrected array 2 > Corrected array 3 > Corrected array 4 > Corrected array 5 > > > > plotDensities(RG) > > > > MA <- normalizeBetweenArrays(RG,method="vsn") > Loading required package: vsn > Loading required package: Biobase > > Welcome to Bioconductor > > Vignettes contain introductory material. To view, type > 'openVignette()'. To cite Bioconductor, see > 'citation("Biobase")' and for packages 'citation(pkgname)'. > > vsn2: 43931 x 10 matrix (1 stratum). Please use 'meanSdPlot' to > verify the fit. > > > > plotDensities(MA) > > > > f<-factor(targets$Name) > > design<-model.matrix(~0+f) > > design > fB1vsT1 fB2vsT2 fB3vsT3 fB4vsT4 fB5vsT5 > 1 1 0 0 0 0 > 2 0 1 0 0 0 > 3 0 0 1 0 0 > 4 0 0 0 1 0 > 5 0 0 0 0 1 > attr(,"assign") > [1] 1 1 1 1 1 > attr(,"contrasts") > attr(,"contrasts")$f > [1] "contr.treatment" > > > colnames(design) <- levels(f) > > design > B1vsT1 B2vsT2 B3vsT3 B4vsT4 B5vsT5 > 1 1 0 0 0 0 > 2 0 1 0 0 0 > 3 0 0 1 0 0 > 4 0 0 0 1 0 > 5 0 0 0 0 1 > attr(,"assign") > [1] 1 1 1 1 1 > attr(,"contrasts") > attr(,"contrasts")$f > [1] "contr.treatment" > > > > > fit<-lmFit(MA, design) > > contrasts.matrix <- makeContrasts(B1vsT1,B2vsT2, B3vsT3, B4vsT4, > B5vsT5, levels=design) > > > > fit2 <- contrasts.fit(fit, contrasts.matrix) > > fit2 <- eBayes(fit2) > Error in ebayes(fit = fit, proportion = proportion, stdev.coef.lim > = stdev.coef.lim) : > No residual degrees of freedom in linear model fits > > > > toptable(fit2) > Error in ebayes(fit, ...) : > No residual degrees of freedom in linear model fits > > > > > Prashantha Hebbar wrote: > > Hi Chuming, > > As per your experimental information, you have replicates. > Because, you have samples from same tissue with 2 different region > across all patients. So, you should be able to fit linear model. > What I guess, there is something wrong in your analysis steps. So, > better to send sessional information to list. > > Regards, > > Prashantha > > > > Prashantha Hebbar Kiradi, > > Dept. of Biotechnology, > > Manipal Life Sciences Center, > > Manipal University, > > Manipal, India > > Email:prashantha.hebbar at manipal.edu > > > > --- On *Mon, 1/25/10, Wolfgang Huber /<whuber at="" embl.de="">/* wrote: > > > > > > From: Wolfgang Huber <whuber at="" embl.de=""> > > Subject: Re: [BioC] Agilent G4112A Arrays > > To: "Naomi Altman" <naomi at="" stat.psu.edu=""> > > Cc: "Chuming Chen" <chumingchen at="" gmail.com="">, "Prashantha Hebbar" > > <prashantha.hebbar at="" yahoo.com="">, bioconductor at stat.math.ethz.ch > > Date: Monday, January 25, 2010, 8:06 PM > > > > Hi Chuming > > > > if you want to work with the approximation that M-values have > > equal variances, then preprocessing the data with a method that > > provides variance stabilisation (e.g. vsn) will likely be > useful. > > > > Furthermore, it might be useful to discard a fraction of genes > > with low A-values, since they are more likely to be either not > > expressed, or so weakly expressed that you would find it more > > difficult to validate them. > > > > Best wishes > > Wolfgang > > > > Naomi Altman wrote: > > > The more data one has, the fewer assumptions one needs. > In the > > absence of replication, you cannot get p-values without very > > strong assumptions. e.g. you could assume that the vast > majority > > of the genes do not differentially express, that their M-values > > have equal variance and that the M-values are normally > > distributed. Then you could use e.g. the IQR of the M-values to > > estimate the sd and use this to pick a fold cut-off for DE. You > > have no reasonable way to estimate FDR with this approach, > but it > > might be slightly better than using 2-fold - or then again, it > > might not. Without replication, there is no way to know. > > > > > > Regards, > > > Naomi Altman > > > > > > > > > At 08:53 AM 1/25/2010, Chuming Chen wrote: > > >> Hi Prashantha, > > >> > > >> Thank you for your suggestion. My target file is as below. > > Although I couldn't fit a linear model, I still wonder whether I > > can do some statistic on M (log ratio) values and use the > p-value > > to get the differentially expressed genes. > > >> > > >> SlideNumber FileName Cy3 Cy5 > > >> 1 B1vsT1.txt B1 T1 > > >> 2 B2vsT2.txt B2 T2 > > >> 3 B3vsT3.txt B3 T3 > > >> 4 B4vsT4.txt B4 T4 > > >> 5 B5vsT5.txt B5 T5 > > >> > > >> Chuming > > >> > > >> > > >> Prashantha Hebbar wrote: > > >>> Dear Chen, > > >>> > > >>> You need not to look for any other packages. Since, you > do not > > have any replicates, do not fit linear model, instead just do > > normalization with in arrays and look at the M (log ratio) > values. > > >>> > > >>> Regards, > > >>> > > >>> Prashantha Hebbar Kiradi, > > >>> Dept. of Biotechnology, > > >>> Manipal Life Sciences Center, > > >>> Manipal University, > > >>> Manipal, India > > >>> > > >>> > > >>> --- On *Mon, 1/25/10, Chuming Chen /<chumingchen at="" gmail.com=""> > > <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="chumingchen" at="" gmail.com="">>/* > > wrote: > > >>> > > >>> > > >>> From: Chuming Chen <chumingchen at="" gmail.com=""> > > <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="chumingchen" at="" gmail.com="">> > > >>> Subject: [BioC] Agilent G4112A Arrays > > >>> To: bioconductor at stat.math.ethz.ch > > > <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="bioconductor" at="" stat.math.ethz.ch=""> > > >>> Date: Monday, January 25, 2010, 6:32 AM > > >>> > > >>> Dear All, > > >>> > > >>> I am trying to find out the differentially expressed > genes > > from > > >>> some Agilent Human Whole Genome (G4112A) Arrays data. > > >>> > > >>> I have tried LIMMA package, but LIMMA gave the error > > message "no > > >>> residual degrees of freedom in linear model fits" and > > stopped. My > > >>> guess is that my data has no replicates in the > experiment. > > >>> > > >>> Is there any other packages I can use to find > differentially > > >>> expressed genes which does not require replicates in the > > experiment? > > >>> > > >>> Thanks for your help. > > >>> > > >>> Chuming > > >>> > > >>> _______________________________________________ > > >>> Bioconductor mailing list > > >>> Bioconductor at stat.math.ethz.ch > > > <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="Bioconductor" at="" stat.math.ethz.ch=""> > > >>> </mc> > > <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="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 > > >>> > > >> > > >> _______________________________________________ > > >> Bioconductor mailing list > > >> Bioconductor at stat.math.ethz.ch > > > <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="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 > > > <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 > > > > > > _______________________________________________ > > > Bioconductor mailing list > > > Bioconductor at stat.math.ethz.ch > > > <http: us.mc1101.mail.yahoo.com="" mc="" compose?to="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 > > > <http: news.gmane.org="" gmane.science.biology.informatics.conductor=""> > > > > -- Best wishes > > Wolfgang > > > > > > -- > > Wolfgang Huber > > EMBL > > http://www.embl.de/research/units/genome_biology/huber/contact > > > > > > > >
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