[BioC] edgeR

Gordon K Smyth smyth at wehi.EDU.AU
Thu Apr 10 01:35:09 CEST 2014


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.


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 at 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 at bioconductor.org>
>>> To: bioconductor at r-project.org, jahn.davik at 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 intend...{{dropped:4}}

More information about the Bioconductor mailing list