[BioC] DESeq nbinomGLMTest specific comparisons

James Floyd j.a.floyd at qmul.ac.uk
Mon Jan 13 15:18:24 CET 2014

Yes, I've made the transition to DESeq2 - thanks for the reply.


On 10/01/2014 17:18, "Ryan C. Thompson" <rct at thompsonclan.org> wrote:

>In general, you would subset the CountDataSet to only include the
>samples from the two conditions you care about. Do this after
>estimating dispersions and before fitNbinomGLMs.
>But more generally, I agree that the real solution is "use DESeq2".
>On Thu 09 Jan 2014 03:41:39 AM PST, James Floyd wrote:
>> Hi,
>> Thanks for your reply, and sorry for the delay. As always, I got
>> distracted by other projects Š
>> I am looking to perform a DE analysis for a disease with three states,
>> while correcting for smoking status. So I was using Š :
>> fit0 = fitNbinomGLMs( cdsFull, count ~ smoking )
>> fit1 = fitNbinomGLMs( cdsFull, count ~ smoking + condition )
>> Š then comparing the models to see whether adding condition improves the
>> fit of the model and has a significant effect (as described in the
>> vignette).
>> However, if I wanted to make a specific comparison of, say, the two most
>> extreme disease states (state A against C, ignoring B), how would I go
>> about doing this?
>> Thanks for letting me know about DESeq2, I was unaware of it.
>> Cheers,
>> Jamie
>> On 27/11/2013 17:21, "Steve Lianoglou" <lianoglou.steve at gene.com> wrote:
>>> Hi,
>>> On Tue, Nov 26, 2013 at 8:21 AM, James Floyd <j.a.floyd at qmul.ac.uk>
>>>> Hi,
>>>> I notice in the DESeq
>>>> st/doc/DESeq.pdf> there is a "Fixme" comment about including how to
>>>> specific comparisons using nbinomGLMTest (page 12). It would be great
>>>> someone with experience doing this could let me know how it is done.
>>> Can you be more specific of what you'd like to test? Perhaps you can
>>> setup your problem for us and point out what particular comparison
>>> you'd like to make, and someone can help you better.
>>> Furthermore, if you are using DESeq, you might consider shifting over
>>> to DESeq2. It offers a number of benefits over DESeq, and (I believe)
>>> this is where the DESeq authors are focussing the lion's share of
>>> their attention anyhow.
>>> -steve
>>> --
>>> Steve Lianoglou
>>> Computational Biologist
>>> Genentech
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