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
>
>
>

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