Hi,

On Wed, Apr 10, 2013 at 1:38 AM, daniel.aguirre
<daniel.aguirre@cbm.uam.es>wrote:
[snip]

I was expecting that (having no replicates, for several conditions of a
> single factor/variable) using a dispersion model built from all my samples
> will increase the number of DE genes found with respect to a single
> pairwise analysis.
>
>  However I found the contrary; e.g. comparing Condition A vs. B (one
> replicate each) [using DESeqSummarizedExperimentFromH**TSeqCount with A
> and B only], I find 56 DE genes (pairwise comparison), when I build the
> dispersion model etc. including other two samples the comparison between A
> and B yields only 13 [here using DESeqSummarizedExperimentFromH**TSeqCount
> with A,B,C,D following the proposed pipeline and then retrieving results
> for A vs B].
>
> Would you care to comment on this? Could you give me a hint as to why this
> happens and I was wrong to assume te contrary)
>

I haven't been following this thread very closely, but briefly skimming the
emails it would seem as if Michael already suggested why this would be the
case. In a previous email he wrote:

"""If you have only one replicate per condition, you can treat the
samples as replicates in order to calculate dispersion. """

I guess you are using a "blind" method (not sure if the nomenclature
changed in DESeq2) to estimate your dispersions in this scenario. To do so,
all of your data are assumed to be replicates of *the same condition* and
the dispersion is estimated from them. Can you see why this scenario would
result in inflated dispersion estimates "across the board"?

If so, then there's your answer as to why you are getting fewer DE genes
when you are using no-replicate data across a larger number of conditions
to estimate the dispersion -- the dispersion estimates are higher as you
add more samples from different conditions but are forced to assume that
they are coming from the same condition.

HTH,
-steve

-- 
Steve Lianoglou
Defender of The Thesis
 | Memorial Sloan-Kettering Cancer Center
 | Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact

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