[BioC] Using DESeq2: experimental design and extracting results

Michael Love michaelisaiahlove at gmail.com
Wed Jun 11 02:20:35 CEST 2014

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:

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:

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.


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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