Dear Gordon,

Thank you for your answers!


> The problem that you've stated (which of these models best describes my
> data) applies to univariate data, for fitting models to a single vector of
> counts.  For RNA-seq experiments, you are fitting glms to many data
> vectors.  You will get different answers for different genes, so you have
> to think a bit differently.
>
I see. But if the variable A gives 100 times more differentially expressed
genes (p-value < 0.05) than the variable E, and 10 times more than the
variable E:A under E*A model is it possible to say that A explains my count
data better?

>
>
> to test for which genes the interaction is significant.  If the test is
> significant, then the E*A model is necessary and best.  If not, one of the
> simpler models is satisfactory.  You could subset to genes for which the
> interaction is not significant, then try
>
>
>>
>> 1. Am I right that the $coefficients of a "DGELRT" object are log
>> likelihood values?
>>
>
> No.  They are coefficients from the linear model fitted, as explained in
> the documentation: help("glmFit").
>
Is there a possibility to have the log likelihood values?

>
>  2. Does anyone have a suggestion how can I apply AIC (Akaike Information
>> Criterion) test or Likelihood ratio (LR) test on edgeR GLM results to
>> choose the best model?
>>
>
> Answered above.  We have experimented with AIC, but have not found it
> particularly successful.
>
I see. I was wondering also, because the AIC test have to be calculated for
every genes, than how can I say something about the whole dataset...

>
>  3. Is there a possibility to plot the multiple GLM regression for a gene?
>> (I would like plot something like this: http://www.jerrydallal.com/**
>> LHSP/pix/regpix4.gif <http://www.jerrydallal.com/LHSP/pix/regpix4.gif> )
>>
>
> I have no idea what this plot represents.  In any case, I guess this is
> for one data vector.  For RNA-seq data, you would have to view 20,000 of
> these plots, not a very productive way to go.
>
I would like to have a regression plot only for some genes - just for
illustration. I have 2 replicates for each treatment that means 8 count
values for each gene. I would like to draw a 3D regression for those 8
datapoints, while x and z axes would represent A and E variables (with 2-2
possible state only) and y would show the count number. Maybe I should
calculate the regression for this plot not by edgeR but with glm.nb.

Thanks a lot!
Best wishes,
Eszter


-- 
Eszter Ari
Institut für Populationsgenetik
Vetmeduni Vienna
Veterinärplatz 1
1210 Wien, Austria

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