Answers.Body: <p>The DESeq2 documentation has the answers you seek: <a href="https://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#how-can-i-include-a-continuous-covariate-in-the-design-formula" rel="nofollow noreferrer">https://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#how-can-i-include-a-continuous-covariate-in-the-design-formula</a>.</p>
  <p>Also read the section on paired samples. In short you should have subject as a formula term.</p>
  
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Body: <p>I have samples from 45 individuals at 2 time points and these individuals were given a specific diet. The BMI of these individuals were recorded at both time points. The responders to the diet are the ones that show decrease in BMI from T1 to T2. I want to use continuous variable (BMI) to identify genes that are predictors of response to diet. I also have other covariates that need to be controlled like age &amp; gender. The metadata table looks like following</p>
  <p><a href="https://i.sstatic.net/y7znl.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/y7znl.png" alt="enter image description here" /></a></p>
  <p>I am not sure the design that I should go for. The one that I think is appropriate is</p>
  <pre class="lang-r prettyprint-override"><code>dds &lt;- DESeqDataSetFromMatrix(countData=cts,colData=meta,design=~0+Time+Gender+AGE+Time:BMI)
  dds &lt;- DESeq(dds,test=&quot;LRT&quot;, reduced=~Gender+AGE)
  res=results(dds, contrast=list(&quot;T1.BMI&quot;,&quot;T2.BMI&quot;))
  </code></pre>
  <p>Is this the correct way to go ahead and to identify responder genes to the diet?</p>
  <p>All suggestions are very welcome.</p>
  <p>Thank you</p>
  
Tags: <rna-seq><deseq2>
Title: Continuous predictor variable to identify responder genes using DESeq2
ThreadText: # Continuous predictor variable to identify responder genes using DESeq2
  
  (Asked by: username_0 on 2024-02-01)
  
  I have samples from 45 individuals at 2 time points and these individuals were given a specific diet. The BMI of these individuals were recorded at both time points. The responders to the diet are the ones that show decrease in BMI from T1 to T2. I want to use continuous variable (BMI) to identify genes that are predictors of response to diet. I also have other covariates that need to be controlled like age & gender. The metadata table looks like following
  
  [![enter image description here](https://i.sstatic.net/y7znl.png)](https://i.sstatic.net/y7znl.png)
  
  I am not sure the design that I should go for. The one that I think is appropriate is
  
  ```r
  dds <- DESeqDataSetFromMatrix(countData=cts,colData=meta,design=~0+Time+Gender+AGE+Time:BMI)
  dds <- DESeq(dds,test="LRT", reduced=~Gender+AGE)
  res=results(dds, contrast=list("T1.BMI","T2.BMI"))
  
  ```
  
  Is this the correct way to go ahead and to identify responder genes to the diet?
  
  All suggestions are very welcome.
  
  Thank you
  
  **Tags:** rna-seq, deseq2
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-11-04. Score: 0
  
  The DESeq2 documentation has the answers you seek: <https://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#how-can-i-include-a-continuous-covariate-in-the-design-formula>.
  
  Also read the section on paired samples. In short you should have subject as a formula term.
  
  
  ---
  
  