My apologies -- I did not mean to sound confrontational, though I realize I
probably did.  Email is bad at that.  I guess I was fishing for an expert's
opinion on whether one of the two approaches is preferable (i.e. is one
approach thought to be more powerful, better control of false positives,
more meaningful, less prone to statistical artifact, less assumptions to be
broken, etc.)

Gordon, thank you (again) for all of your time and energy spent answering
our questions!

-Aaron


On Wed, Apr 9, 2014 at 7:35 PM, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> Aaron,
>
> One reason for your surprise may be that you have read a lot of things
> into my reply that weren't there.
>
> I simply showed how to use to software to collate results from multiple
> contrasts.  The contrasts could be anything including interactions.
>
> Gordon
>
>
> On Tue, 8 Apr 2014, Aaron Mackey wrote:
>
>  I'm surprised that the given answer here was to check whether two fold
>> changes are themselves significant or not, rather than what I tend to
>> think
>> of as a "contrast of contrasts" scenario to test whether the two
>> contrasts'
>> fold changes are significantly different from another between groups (i.e.
>> testing for an interaction term)?  A transcript that fails to pass the
>> threshold for dt1 may be insignificant at that particular threshold, yet
>> still indistinguishable from the dt2 response that does happen to pass the
>> threshold.  The absence of significance is not evidence for non-response.
>> The OP seemed to be asking for a statistical test to look for differences
>> in response between the two groups.  Another problem with !dt1 & dt2 is
>> that dt1 and dt2 may both be "true" but of opposite signs (though I see
>> this addressed in a later email).
>>
>> So in a nutshell: is there statistical reason to prefer independent FDR
>> threshold tests vs. direct testing for an interaction term?
>>
>> Thanks,
>> -Aaron
>>
>>
>> On Mon, Apr 7, 2014 at 8:22 PM, Gordon K Smyth <smyth@wehi.edu.au> wrote:
>>
>>  If you want to select transcript that are DE for one contrast but not
>>> another, first test each contrast:
>>>
>>>   lrt1 <- glmLRT(fit, contrast=mycontrast1)
>>>   lrt2 <- glmLRT(fit, contrast=mycontrast2)
>>>
>>> Then apply significance thresholds:
>>>
>>>   dt1 <- decideTestsDGE(lrt1)
>>>   dt2 <- decideTestsDGE(lrt2)
>>>
>>> Then select the transcripts you want:
>>>
>>>   selected <- !dt1 & dt2
>>>
>>> Best wishes
>>> Gordon
>>>
>>>
>>>  Date: Sun,  6 Apr 2014 03:32:49 -0700 (PDT)
>>>
>>>> From: "Jahn Davik [guest]" <guest@bioconductor.org>
>>>> To: bioconductor@r-project.org, jahn.davik@bioforsk.no
>>>> Subject: [BioC] edgeR
>>>>
>>>>
>>>> Hi there,
>>>>
>>>>
>>>  I have a question regarding edgeR - or it might actually be a more
>>>
>>>> general statistical question. In any case, I am using edgeR to analyse
>>>> my
>>>> read counts and really would appreciate help.
>>>>
>>>>
>>>  My experimental setup is:
>>>
>>>>
>>>>
>>>  Two genotypes (B and S)
>>>
>>>> Two treatments ('trt' vs 'ntrt')
>>>> Two time points (0hs 8hs).
>>>> (Three bio reps)
>>>>
>>>> Now, I would like to identify reads that are specific to either of the
>>>> genotypes as their response to the treatment over the time points.
>>>>
>>>
>>>  I expect that I can do pairwise comparisons like:
>>>
>>>  'B_tr_0hs' vs 'B_trt_8hs'), and 'B_ntr_0hs' vs 'B_ntrt_8hs'), and
>>>
>>>> continuing doing the same with the S-genotype. Subsequently, using a
>>>> suitable tool, I could filter out the transcripts for, say, B's
>>>> response to
>>>> treatment over these two time points that are not found in B. It is,
>>>> however, a little tedious so my question here is whether this can be
>>>> modeled and extracted in edgeR's GLM ?
>>>>
>>>> regards
>>>> JD
>>>>
>>>
> ______________________________________________________________________
> The information in this email is confidential and inte...{{dropped:10}}

