[BioC] Questions about edgeR
Mark Robinson
mark.robinson at imls.uzh.ch
Tue Dec 20 10:36:12 CET 2011
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 at 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 at 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 at 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 at 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 at imls.uzh.ch
o: Y32-J-34
w: http://tiny.cc/mrobin
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