<p>You can specify the exact comparisons you want in the <code>results()</code> function. So:</p>

<pre><code>dds = DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = directory, design= ~ condition)
dds = DESeq(dds)
res = results(dds, contrast("condition", "treatment1", "control1"))
</code></pre>

<p>Note that <code>condition</code> should not be, "<code>stim001-control1</code>, <code>stim001-control2</code>, etc.", but instead, "<code>stim001control</code>, <code>stim001control</code>, <code>stim001control</code>, <code>stim001treatment</code>, etc.". Don't put minus signs in your levels and don't number your samples in them (they're not in different groups.</p>

<p>The last command would be repeated for each of the comparisons. Note that this will still be quite slow due to the number of samples and the number of groups. With so many samples, you might just make a separate <code>sampleTable</code> for each of the comparisons you want to make. Each would then only contain the samples relevant for that comparison, so 60 total in each.</p>

<p>Alternatively, if you want to fit all of the groups at once then it's likely that limma/voom will prove to have better performance. It uses different math that happens to be quicker with large models.</p>

<p>BTW, if the slowness is coming from <code>DESeqDataSetFromHTSeqCount()</code> then you can mere everything on the command line into a single matrix. A couple lines of python or even the <code>join</code> command can do that quickly enough.</p>

CC BY-SA 3.0
2017-11-04T19:09:02.183Z
2017-11-04T19:09:02.183Z
Devon Ryan
<p>I am currently performing a large RNA-seq analysis from mice PBMCs. The dataset contains around 6,000 transcriptomic profiles and I would like to use DESeq2 to identify the sets of differentially expressed genes in the different conditions. In total, I have 100 biological stimulations, and for each stimulation I have 30 control samples and 30 samples treated with a molecule of interests (so is have: (30 controls+30 treated) * 100 stimulations = 6,000 samples). </p>

<p>I want to identify, for each stimulation, the sets of differentially expressed genes between control samples and treated samples. I do not want to compare samples from the different stimulations. In total, I would like to have thus 100 lists of differentially expressed genes.</p>

<p>I have started to use deseq2 to identify these lists but deseq2 is spending lot of time to perform comparisons on which I am not interested with (comparisons between the biological stimulations).</p>

<p>For now, I have a table sampleTable which looks like that:</p>

<p><a href="https://i.sstatic.net/K95Y5.png" rel="noreferrer"><img src="https://i.sstatic.net/K95Y5.png" alt="enter image description here"></a></p>

<p>I am using DEseq2 using the following command:</p>

<pre><code>DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = directory, design= ~ condition)
</code></pre>

<p>Could you please help me with that? How can I specify to deseq2 to not perform the comparisons between the biological stimulations, but rather between control and treated sample within each stimulation ?</p>

<p>Thank you and best,</p>

CC BY-SA 3.0
2017-11-04T12:57:42.240Z
I am not sure that you can do that... why are you not interested between control and treated of the different stimulations?
Henry
<rna-seq><differential-expression><deseq2>
How can I specify to DEseq2 to only perform comparisons on which I am interested with?
# How can I specify to DEseq2 to only perform comparisons on which I am interested with?

(Asked by: username_0 on 2017-11-04)

I am currently performing a large RNA-seq analysis from mice PBMCs. The dataset contains around 6,000 transcriptomic profiles and I would like to use DESeq2 to identify the sets of differentially expressed genes in the different conditions. In total, I have 100 biological stimulations, and for each stimulation I have 30 control samples and 30 samples treated with a molecule of interests (so is have: (30 controls+30 treated) * 100 stimulations = 6,000 samples). 
I want to identify, for each stimulation, the sets of differentially expressed genes between control samples and treated samples. I do not want to compare samples from the different stimulations. In total, I would like to have thus 100 lists of differentially expressed genes.
I have started to use deseq2 to identify these lists but deseq2 is spending lot of time to perform comparisons on which I am not interested with (comparisons between the biological stimulations).
For now, I have a table sampleTable which looks like that:

I am using DEseq2 using the following command:
DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = directory, design= ~ condition)

Could you please help me with that? How can I specify to deseq2 to not perform the comparisons between the biological stimulations, but rather between control and treated sample within each stimulation ?
Thank you and best,

**Tags:** rna-seq, differential-expression, deseq2
> **Comments:**
> **username_1**: I am not sure that you can do that... why are you not interested between control and treated of the different stimulations?  

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## 1 answers

### Answer by username_2 on 2017-11-04. Score: 3

You can specify the exact comparisons you want in the results() function. So:
dds = DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = directory, design= ~ condition)
dds = DESeq(dds)
res = results(dds, contrast("condition", "treatment1", "control1"))

Note that condition should not be, "stim001-control1, stim001-control2, etc.", but instead, "stim001control, stim001control, stim001control, stim001treatment, etc.". Don't put minus signs in your levels and don't number your samples in them (they're not in different groups.
The last command would be repeated for each of the comparisons. Note that this will still be quite slow due to the number of samples and the number of groups. With so many samples, you might just make a separate sampleTable for each of the comparisons you want to make. Each would then only contain the samples relevant for that comparison, so 60 total in each.
Alternatively, if you want to fit all of the groups at once then it's likely that limma/voom will prove to have better performance. It uses different math that happens to be quicker with large models.
BTW, if the slowness is coming from DESeqDataSetFromHTSeqCount() then you can mere everything on the command line into a single matrix. A couple lines of python or even the join command can do that quickly enough.


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