[BioC] Using DESeq2: experimental design and extracting results

[Michael Love]

I left out one detail, below:

On Tue, Jun 10, 2014 at 8:20 PM, Michael Love
<michaelisaiahlove at gmail.com> wrote:
> hi Sridhar,
>
> Let's keep the discussion on the mailing list in case the question is
> relevant to others.
>
> After you have run:
>
> design(dds) <- ~ genotype + time + genotype:time
> dds <- DESeq(dds, test="LRT", reduced=genotype + time)
> res <- results(dds)
>
> The res object will contain the likelihood ratio test results, with
> small p-values for genes which have a genotype effect which is
> different than in the first time period. This tests all time periods
> after the first time period.
>
> You also see:
>
> resultsNames(ddsLRT)
> Intercept time2_vs_time1 time3_vs_time1 time4_vs_time1
> gen2_vs_gen1 time2.gen2 time3.gen2 time4.gen2
>
> The three of these which might be interesting for your experiment are:
>

Before the results line below you should call:

dds <- nbinomWaldTest(dds, betaPrior=FALSE)

> results(dds, name="time2.gen2")
> ...and same for time3.gen2, time4.gen2
>
> which will return a results table with Wald tests of the additional
> genotype effect in time 2 (additional beyond the genotype effect in
> the first time period). This is similar to the first LRT results
> above, except now we are asking for a different effect of genotype in
> a specific time period, not in all time periods.
>
> The other coefficients are the main effect terms. Results tables for
> these can also be built by using the 'name' argument to results().
> They are the intercept term, the effects of the different times over
> the initial time, and the effect for genotype 2 over 1 in the first
> time period. You don't want to use the contrast argument, which is for
> other kinds of models.
>
> Mike
>
> On Tue, Jun 10, 2014 at 3:38 PM, Michael Love
> <michaelisaiahlove at gmail.com> wrote:
>> hi Sridhar,
>>
>> On Tue, Jun 10, 2014 at 3:05 PM, Sridhar A Malkaram
>> <smalkaram at wvstateu.edu> wrote:
>>> Hi,
>>>
>>>
>>> I have been a user of DESeq and recently DESeq2 for my research work.
>>> The latest DESeq2 seem to offer extensive differential testing options
>>> suitable for various experimental designs.
>>>
>>> Recently I wanted to use DESeq for a differential gene expression
>>> analysis between two plant genotypes across 4 different time points.
>>>
>>> I am basically a biologist and am finding hard to grasp the concepts of
>>> testing results. I'd be very grateful if you could help me understand
>>> some concepts (especially resultsNames) related to the DESeq2 package.
>>>
>>>
>>> My experimental design is as below
>>>
>>> design<- ~ genotype + time + genotype:time
>>>
>>> There are two levels in genotype and 4 levels in time.
>>> Basically I'd like to use binomLRT test to check if there is any
>>> difference in gene expression between the genotypes across the time points.
>>>
>>> dds<-DESeq(dds)  (dds is DESeq2 object  obtained from,
>>> dds<-DESeqDataSetFromMatrix(countData=counts, colData=coldata,
>>> design=design)
>>>
>>> and I am using the reduced model for the liklihood test
>>>
>>
>> Here is where things are getting confused. You have already run
>> DESeq() using test="Wald". So it doesn't make sense at this point to
>> instead perform a likelihood ratio test. In our vignette we explain
>> this in the section on the LRT: "The likelihood ratio test can also be
>> specified using the test argument to DESeq, which substitutes
>> nbinomWaldTest with nbinomLRT."
>>
>>> Is the model correct per my research question (is there a (time
>>> influenced) difference  between genotypes)?
>>>
>>
>> Yes. If you want to find those genes which show a time influenced
>> difference between genotypes, this is simply:
>>
>> dds <- DESeq(dds, test="LRT", reduced=genotype + time)
>> res <- results(dds)
>>
>> You can then use heatmaps to inspect the patterns of gene expression
>> for the differentially expressed genes. Visualization with heatmaps
>> are covered in the vignette.
>>
>> If you have other more specific questions about how to generate
>> results tables, I can answer them. With time series experiments, there
>> are many possible combinations to test, but rather than going through
>> all combinations, we recommend that users explore the results with
>> heatmaps.
>>
>> Mike
>>
>>> Thanks,
>>> Sridhar Acharya
>>>
>>>
>>>         [[alternative HTML version deleted]]
>>>
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