>
>
>  BTW, is it better to estimate dispersion in paires (treatment1 / control;
>> treatment2 / control, ...) or rather for a whole dataset in case of 1
>> control / treatment 1-6  (each sample in 2 biol rep).
>>
>
> As you have only two replicates per treatment, you are most likely better
> of if you use your whole data set to get reasonably precise dispersion
> estimates.
>
> However, if there is one treatment where the replicates show stronger
> differences than in the othe treatments, this will increase dispersion and
> hence reduce power for all comparisons. So, better double-check that this
> is not the case, e.g. with the plotPCA function.
>
>
Hi Simon,

Thanks for reply,

I have already double-checked and pooling 16 samples (2 rep X 8 cond)
increases dispersion, looking in the PCA plot.
I decide to divide the set in two: 3 conds + control and other 5 + control
(more distal). When pooling all, first 3 three are indistinguishable for
the control in the PCA and number of DE genes increases for all the
samples, especially the 5 distal, for which  I got many infinite fold
changes...

Best,
Michael








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