Thank you Mark,

My absence of clearness is because I'm having trouble to figure out how to
deal with the data.

at your first question, actually there is a missing command line:

> disp <- estimateCommonDisp(b)
> disp$common.dispersion = 0.0001004979

*> disp <- estimateGLMCommonDisp(b)*
> disp$common.dispersion =  3.999943

I was doing some tests about dispersions, I'm still not certain wich one
should I use.

The second question:

I have 2 comtrols:
MO6h MO24h (these are my controls through time)
and 2 treatments for both times:
La6h La24h (La treatment)
Lm6h Lm24h (Lm treatment)

My biological questions are, 1) do I have modulation for each treatment
through time and 2) there are differences for the same time for both
treatments?
I do not know how should I use my controls for both situations. For the
situation that I'm looking for each time point I could use a intercept
design for each time point, like:

design <- model.matrix(~group, data=c$samples)

But I do not know if that is correct and how to analyse each treatment
dealing with the controls. The best situation would be to deal first with
the controls and them the treated samples through time, any ideia?

--
Fabricio K. Marchini
Adjunct Researcher

Bioinformatics and Computational Biology Laboratory
Functional Genomics Laboratory
CV - CNPq <http://lattes.cnpq.br/4462407232591810> / Web
site<http://marchinifk.me>

Instituto Carlos Chagas / FIOCRUZ-PR
Phone +55 41 3316 3236
Fax +55 41 3316 3267
R. Prof. Algacyr Munhoz Mader, 3775 -CIC
Curitiba - PR - Brasil, 81350-010


On Wed, Oct 10, 2012 at 5:40 PM, Mark Robinson <mark.robinson@imls.uzh.ch>wrote:

> Hi Fabricio,
>
> I suggest you check (at least) 2 things:
>
> 1.
> > disp <- estimateCommonDisp(b)
> > disp$common.dispersion = 0.0001004979
>
> > disp$common.dispersion =  3.999943
>
> Your example only makes 1 call to estimateCommonDisp(), but you have 2
> drastically different values.  Are you reporting these as the estimated
> values, or are you actually running this command and *setting* the common
> dispersion?  It's not clear from your message.
>
> You may also want to study some of the GLM-based case studies in:
>
> http://www.bioconductor.org/packages/2.11/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf
>
> For example, the standard GLM work flow would be similar to that on Page 7.
>
>
> 2.
> > lrt <- glmLRT(b,fit,coef=fit$design)
>
> The docs for the 'coef' argument (?glmLRT) say:
> ----
>    coef: integer or character vector indicating which coefficients of
>          the linear model are to be tested equal to zero.  Values must
>          be columns or column names of ‘design’. Defaults to the last
>          coefficient.  Ignored if ‘contrast’ is specified.
> ----
> As you can see, the function is expecting something very different to what
> give as your 'coef' argument. Maybe you want 'coef=2:6', if you are looking
> for any difference between your 6 groups.  Of course, maybe you actually
> want to split your factors into 2 … one of ("La","Lm","MO") and one of
> ("6h","24h") and construct a design matrix accordingly.  But, this is also
> not clear from your message.
>
> Hope that helps,
> Mark
>
>
> ----------
> Prof. Dr. Mark Robinson
> Bioinformatics
> Institute of Molecular Life Sciences
> University of Zurich
> Winterthurerstrasse 190
> 8057 Zurich
> Switzerland
>
> v: +41 44 635 4848
> f: +41 44 635 6898
> e: mark.robinson@imls.uzh.ch
> o: Y11-J-16
> w: http://tiny.cc/mrobin
>
> ----------
> http://www.fgcz.ch/Bioconductor2012
>
>
>
> On 10.10.2012, at 06:41, Fabricio Marchini wrote:
>
> > Hi,
> >
> > I'm using EdgeR to analyse a proteomic data with peptide counting. I have
> > limited experience on R/EdgeR/Statistics so I appreciate some help.
> > Using the follow code:
> >
> > a=file[,2:64]
> >
> >
> b=DGEList(counts=a,group=rep(c("La6h","La24h","Lm6h","Lm24h","MO6h","MO24h"
> > ),c(10,11,10,11,10,11)), lib.size=colSums(a))
> >
> > b <- calcNormFactors(b)
> >
> > times <-
> rep(c("La6h","La24h","Lm6h","Lm24h","MO6h","MO24h"),c(10,11,10,11,
> > 10,11))
> >
> > times <-
> factor(times,levels=c("La6h","La24h","Lm6h","Lm24h","MO6h","MO24h"
> > ))
> >
> > design <- model.matrix(~factor(times))
> >
> > disp <- estimateCommonDisp(b)
> >
> > fit <- glmFit(b,design,dispersion=disp$common.dispersion)
> >
> > lrt <- glmLRT(b,fit,coef=fit$design)
> > disp$common.dispersion = 0.0001004979
> >
> > All proteins (3430) had a p.value of 0.
> >
> > I tried also with
> >
> > fit <- glmFit(b,design,dispersion=disp$common.dispersion)
> >
> > lrt <- glmLRT(b,fit,coef=fit$design)
> > disp$common.dispersion =  3.999943
> >
> > and that gave me all the proteins with p.value lower than 6.29E-05.
> >
> > That gave a signal that I'm doing something wrong or because of both
> common
> > dispersions my data is not a appropriate for the analysis.
> >
> > Any suggestions or corrections?
> >
> > --
> > Fabricio K. Marchini
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
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> http://news.gmane.org/gmane.science.biology.informatics.conductor
>
>

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