Hi Mark,

Thank you for help.

my session info is:
--------------
> sessionInfo()
R version 2.12.0 (2010-10-15)
Platform: x86_64-unknown-linux-gnu (64-bit)

locale:
[1] C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] edgeR_2.0.5

loaded via a namespace (and not attached):
 [1] limma_3.6.9
......

your guess was correct,i'll update R.

Best Regards,
Rabe

On Tue, Dec 20, 2011 at 6:36 PM, Mark Robinson <mark.robinson@imls.uzh.ch>wrote:

> Dear Rabe,
>
> Please read the posting guide:
> http://bioconductor.org/help/mailing-list/posting-guide/
>
> Again, you give very little detail.  My guess is you are using an old
> version of R/edgeR, but you ignored my suggestion to give more information.
>  That code example runs fine on my environment:
>
> > sessionInfo()
> R version 2.14.0 (2011-10-31)
> Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
>
> locale:
> [1] de_CH.UTF-8/de_CH.UTF-8/de_CH.UTF-8/C/de_CH.UTF-8/de_CH.UTF-8
>
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
> [1] BiocInstaller_1.2.1 edgeR_2.4.1
>
> loaded via a namespace (and not attached):
> [1] limma_3.10.0 tools_2.14.0
>
>
> Mark
>
> On 20.12.2011, at 10:25, Asma rabe wrote:
>
> > Hi Mark,
> >
> > Thank you for help
> > When i tried:
> >
> > nlibs <- 5
> >     ntags <- 100
> >     dispersion.true <- 0.1
> >
> >     # Make first a factor with 5 levels
> >     x <- factor(1:5)
> >     design <- model.matrix(~x)
> >
> >     # Generate count data
> >     y <- rnbinom(ntags*nlibs,mu=8,size=
> > 1/dispersion.true)
> >     y <- matrix(y,ntags,nlibs)
> >     colnames(y) <- paste("s",1:5,sep="")
> >     rownames(y) <- paste("Gene",1:ntags,sep="")
> >     d <- DGEList(y)
> >
> >     # Normalize
> >     d <- calcNormFactors(d)
> >
> >     # Fit the NB GLMs
> >     fit <- glmFit(d, design, dispersion=0.1)
> >
> >     # Likelihood ratio test for differences b/w groups
> >     results <- glmLRT(d, fit, coef=2:5)
> >     topTags(results)
> > -------------------
> > i got same errorr:
> >
> > Error in abs(object$table$logFC) :
> >   Non-numeric argument to mathematical function
> >
> > ----------
> > On Tue, Dec 20, 2011 at 5:51 PM, Mark Robinson <
> mark.robinson@imls.uzh.ch> wrote:
> > Hi Rabe,
> >
> > You could make it easier by giving us more information.  It's always a
> good plan to describe your objects and send the output of  sessionInfo().
>  Since I don't know what is in all of your objects (e.g.
> 'my_groups_factor', 'my_data', etc.), I'll make some guesses given what
> you've described.
> >
> > Below is a snippet of code (taken almost entirely from the glmFit/glmLRT
> documentation -- have you read this?) that hard-codes dispersion at 0.1 for
> 5 samples from different groups with no replication:
> >
> > --------------
> >     nlibs <- 5
> >     ntags <- 100
> >     dispersion.true <- 0.1
> >
> >     # Make first a factor with 5 levels
> >     x <- factor(1:5)
> >     design <- model.matrix(~x)
> >
> >     # Generate count data
> >     y <- rnbinom(ntags*nlibs,mu=8,size=1/dispersion.true)
> >     y <- matrix(y,ntags,nlibs)
> >     colnames(y) <- paste("s",1:5,sep="")
> >     rownames(y) <- paste("Gene",1:ntags,sep="")
> >     d <- DGEList(y)
> >
> >     # Normalize
> >     d <- calcNormFactors(d)
> >
> >     # Fit the NB GLMs
> >     fit <- glmFit(d, design, dispersion=0.1)
> >
> >     # Likelihood ratio test for differences b/w groups
> >     results <- glmLRT(d, fit, coef=2:5)
> >     topTags(results)
> > --------------
> >
> > Perhaps, you can modify this to suit your purpose.
> >
> > Of course, as I hope you can appreciate, this is not as ideal as having
> biological replicates and estimating the dispersion through the
> sophisticated methods available ...
> >
> > Regards,
> > Mark
> >
> >
> >
> > On 20.12.2011, at 09:31, Asma rabe wrote:
> >
> > > Hi Mark,
> > >
> > > Thank you for help.
> > >
> > > when i tried :
> > >
> > > >mm<-model.matrix(~0+my_groups_factor)
> > > > fit<-glmfit(my_data,mm,dispresion_value)
> > > > fit<-glmLRT(my_data,fit)
> > > > topTags(fit)
> > > ---------------
> > >
> > > i got:
> > >
> > > Error in abs(object$table$logFC) :
> > >   Non-numeric argument to mathematical function
> > > ----------------
> > > Any idea?
> > >
> > > Thank you,
> > > Rabe
> > >
> > >
> > > On Tue, Dec 20, 2011 at 5:02 AM, Mark Robinson <
> mark.robinson@imls.uzh.ch> wrote:
> > > Hi Rabe,
> > >
> > > Some comments below.
> > >
> > >
> > > On 19.12.2011, at 10:57, Asma rabe wrote:
> > >
> > > > Hi All,
> > > >
> > > > I have RNA-seq data for different time points 0hr,2hr,4hr,6hrs and
> 8hrs.
> > > >
> > > > I would like to use edgeR to get genes that are differentially
> expressed
> > > > throughout the experiment.
> > > > In a recent edgeR user's manual ,it has been suggested to use a
> dispersion
> > > > value of 0.1 in case of no replicates for organisms that are
> genetically
> > > > similar to humans.
> > > > so it is possible to be used for mouse data for example,what about
> other
> > > > organisms like yeast ??
> > >
> > > Yes, it is possible.  But, as mentioned in the manual, none of these
> solutions are ideal.
> > >
> > >
> > > > As exact test is used to get DE genes across 2 samples and i need to
> get DE
> > > > genes across my time course experiment,i used GLM.
> > > >
> > > > when i  used GLM model to find DE genes across my samples i ran the
> > > > following commands:
> > > >
> > > > mm<-model.matrix(~0+my_groups_factor)
> > > > fit<-glmfit(my_data,mm,dispresion_value)
> > > > fit<-glmLRT(data,fit)
> > > > topTags(fit)
> > > >
> > > > -----
> > > > i got:
> > > >
> > > > coefficient: group8
> > > >                 logConc         logFC          PValue            FDR
> > > > gene1         .....                 .....               ....
> > > >    ....
> > > > gene2         ..                       ....
> > > > ....                   ....
> > > > ..
> > > > .
> > > > ..
> > > >
> > > >
> > > > ---
> > > > I have a basic question(sorry i'm not statistician)
> > > > Why the last gorup 8hrs was chooosen to be the coef.??
> > >
> > > Have a look at the documentation for glmLRT and specifically the
> 'coef' argument:
> > >
> > > ?glmLRT
> > >
> > > ----
> > > coef:     … By default, the
> > >          last column of the design matrix is dropped to form the
> > >          design matrix for the null model.
> > > ----
> > >
> > > You have 5 levels for your time course (therefore, design matrix has 5
> columns).  To test for any difference between groups, I would suggest
> reparameterizing your design matrix and then specifying the 'coef'
> argument.  Something like the following:
> > >
> > > mm <- model.matrix(~my_groups_factor)
> > > fit <- glmFit(my_data,mm,dispresion_value)
> > > fit <- glmLRT(data,fit,coef=2:5)
> > > topTags(fit)
> > >
> > > Hope that helps.
> > >
> > > Regards,
> > > Mark
> > >
> > >
> > > >
> > > > Thank you in advance.
> > > > Best Regards,
> > > > Rabe
> > >
> > >
> > > ----------
> > > Prof. Dr. Mark Robinson
> > > Bioinformatics
> > > Institute of Molecular Life Sciences
> > > University of Zurich
> > > Winterthurerstrasse 190
> > > 8057 Zurich
> > > Switzerland
> > >
> > > v: +41 44 635 4848
> > > f: +41 44 635 6898
> > > e: mark.robinson@imls.uzh.ch
> > > o: Y32-J-34
> > > w: http://tiny.cc/mrobin
> > >
> > >
> > >
> >
> > ----------
> > Prof. Dr. Mark Robinson
> > Bioinformatics
> > Institute of Molecular Life Sciences
> > University of Zurich
> > Winterthurerstrasse 190
> > 8057 Zurich
> > Switzerland
> >
> > v: +41 44 635 4848
> > f: +41 44 635 6898
> > e: mark.robinson@imls.uzh.ch
> > o: Y32-J-34
> > w: http://tiny.cc/mrobin
> >
> >
>
> ----------
> Prof. Dr. Mark Robinson
> Bioinformatics
> Institute of Molecular Life Sciences
> University of Zurich
> Winterthurerstrasse 190
> 8057 Zurich
> Switzerland
>
> v: +41 44 635 4848
> f: +41 44 635 6898
> e: mark.robinson@imls.uzh.ch
> o: Y32-J-34
> w: http://tiny.cc/mrobin
>
>

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