Answers.Body: <p>The <code>makeContrasts</code> function is from edgeR so it does not help here. If you want to compare one group vs the average of several others here is a suggestion of how this could be done. It uses a design without intercept so all groups are easily accessable as coefficients in <code>resultsNames(dds)</code>:</p>
  <pre class="lang-r prettyprint-override"><code>suppressMessages(library(DESeq2))
  
  #/ Example with four groups
  set.seed(1)
  dds &lt;- makeExampleDESeqDataSet(m=12)
  dds$condition &lt;- factor(as.character(rep(LETTERS[1:4], each=3)))
  
  #/ Use a design without intercept so all four groups are accessable as coefficients
  design(dds) &lt;- ~0+condition
  dds &lt;- DESeq(dds)
  #&gt; estimating size factors
  #&gt; estimating dispersions
  #&gt; gene-wise dispersion estimates
  #&gt; mean-dispersion relationship
  #&gt; final dispersion estimates
  #&gt; fitting model and testing
  
  #/ results using listValues to get A vs the average ov B,C,D
  res &lt;- results(dds, contrast=list(c(&quot;conditionA&quot;), c(&quot;conditionB&quot;, &quot;conditionC&quot;, &quot;conditionD&quot;)), listValues=c(1, -1/4))
  
  Created on 2022-06-21 by the reprex package (v2.0.1)
  
  </code></pre>
  <p>Determining whether or not it is appropriate to do this in a particular situation is up to you. You have to know what question you want to answer for your work. I cannot help with that.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-06-21T08:14:00.880Z
Answers.LastActivityDate: 2022-06-21T09:39:12.213Z
Answers.LastEditDate: 2022-06-21T09:39:12.213Z
Answers.UserDisplayName: Prontus Hieronymus
Body: <p>This <a href="https://f1000research.com/articles/9-1444" rel="nofollow noreferrer">article</a> talked about various deseq2 designs etc. One of the designs I would like to use is explained as this:</p>
  <p>Control versus treatment average</p>
  <pre><code>makeContrasts((treatmentI+treatmentII+treatmentIII)/3-treatmentCTL,
    levels=colnames(design))
  </code></pre>
  <p>I'm not sure how to incorporate this in deseq2 run which step?</p>
  <p>Is it after running this</p>
  <pre><code>dds &lt;- DESeq(dds)
  </code></pre>
  <p>Can I call the <code>makeContrasts</code> function?</p>
  
Tags: <deseq2>
Title: deseq2 makeContrasts function
ThreadText: # deseq2 makeContrasts function
  
  (Asked by: username_0 on 2022-06-20)
  
  This [article](https://f1000research.com/articles/9-1444) talked about various deseq2 designs etc. One of the designs I would like to use is explained as this:
  
  Control versus treatment average
  
  ```
  makeContrasts((treatmentI+treatmentII+treatmentIII)/3-treatmentCTL,
    levels=colnames(design))
  
  ```
  
  I'm not sure how to incorporate this in deseq2 run which step?
  
  Is it after running this
  
  ```
  dds <- DESeq(dds)
  
  ```
  
  Can I call the `makeContrasts` function?
  
  **Tags:** deseq2
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-06-21. Score: 3 | Accepted answer
  
  The `makeContrasts` function is from edgeR so it does not help here. If you want to compare one group vs the average of several others here is a suggestion of how this could be done. It uses a design without intercept so all groups are easily accessable as coefficients in `resultsNames(dds)`:
  
  ```r
  suppressMessages(library(DESeq2))
  
  #/ Example with four groups
  set.seed(1)
  dds <- makeExampleDESeqDataSet(m=12)
  dds$condition <- factor(as.character(rep(LETTERS[1:4], each=3)))
  
  #/ Use a design without intercept so all four groups are accessable as coefficients
  design(dds) <- ~0+condition
  dds <- DESeq(dds)
  #> estimating size factors
  #> estimating dispersions
  #> gene-wise dispersion estimates
  #> mean-dispersion relationship
  #> final dispersion estimates
  #> fitting model and testing
  
  #/ results using listValues to get A vs the average ov B,C,D
  res <- results(dds, contrast=list(c("conditionA"), c("conditionB", "conditionC", "conditionD")), listValues=c(1, -1/4))
  
  Created on 2022-06-21 by the reprex package (v2.0.1)
  
  ```
  
  Determining whether or not it is appropriate to do this in a particular situation is up to you. You have to know what question you want to answer for your work. I cannot help with that.
  
  
  ---
  
  