text: user285: This is regarding the single factor design For example if I have Age or other continuous numerical variable how to provide that into the design formula.
  For this post do i need to 'You could dichotomise your continuous variables into meaningful groups' or it can go without it?
  My simple design 
  
  
  dds <- DESeqDataSetFromMatrix(countData=rpkm_ordered, colData=coldata, design= ~ Age)
  
  dds <- estimateSizeFactors(dds)
  
  dds <- estimateDispersions(dds)
  
  dds  <- DESeq(dds, parallel = TRUE)
  
  Here in case of metadata/coldata I m giving a single numerical value which is Blast percentage. In case of sample I have like 5 sub-types from M0 to  M5.
  Now for the interpretation part How do I interpret the result?
  Would it be as such
  the expression differences between the my subtypes due to 'Age'
  I'm bit confused since in my coldata I'm not providing any information regarding my subtypes. So when I run this
  resultsNames(dds)  I get this 
  
  
  [1] "Intercept"     "Age"
  
  So if I would like to know if there is a difference between which subtype due to this Age variable how do i know or my question is violating the statistical design and rationale here?
  To know the differences in subtype I have do which is proving the FAB which are basically my sub-types information where I have tested pairwise.
  I would like to know if I give any numerical variable to my design how do I interpret output?
  Any suggestion or help would be really appreciated.
  
  user1540: Your design is ~ Age so you can only test how Age affects each gene and it is assumed that the effect is linear. If you would like to test the effect of sub type on gene expression you'll have to introduce it to the design e.g. ~ Age + Sub.Type (or whatever the column name is). This will not test how the effect of Age is different between between the subtypes, if you would like to test this you will have to include the interaction term in the design formula: ~ Age + Sub.Type + Age:Sub.Type, this design have a lot of parameters and you will need a lot of samples to have a reliable test.
  Hope it helped
  
original_text: 285: <p>This is regarding the single factor design For example if I have Age or other continuous numerical variable how to provide that into the design formula.</p>
  <p>For this <a href="https://support.bioconductor.org/p/130612/#9145075" rel="nofollow noreferrer">post</a> do i need to 'You could dichotomise your continuous variables into meaningful groups' or it can go without it?</p>
  <pre><code>My simple design 
  
  
  dds &lt;- DESeqDataSetFromMatrix(countData=rpkm_ordered, colData=coldata, design= ~ Age)
  
  dds &lt;- estimateSizeFactors(dds)
  
  dds &lt;- estimateDispersions(dds)
  
  dds  &lt;- DESeq(dds, parallel = TRUE)
  </code></pre>
  <p>Here in case of metadata/coldata I m giving a single numerical value which is Blast percentage. In case of sample I have like 5 sub-types from M0 to  M5.</p>
  <p>Now for the interpretation part How do I interpret the result?</p>
  <p>Would it be as such</p>
  <p>the expression differences between the my subtypes due to 'Age'</p>
  <p>I'm bit confused since in my coldata I'm not providing any information regarding my subtypes. So when I run this</p>
  <pre><code>resultsNames(dds)  I get this 
  
  
  [1] &quot;Intercept&quot;     &quot;Age&quot;
  </code></pre>
  <p>So if I would like to know if there is a difference between which subtype due to this Age variable how do i know or my question is violating the statistical design and rationale here?</p>
  <p>To know the differences in subtype I have do which is proving the <code>FAB</code> which are basically my sub-types information where I have tested pairwise.</p>
  <p>I would like to know if I give any numerical variable to my design how do I interpret output?</p>
  <p>Any suggestion or help would be really appreciated.</p>
   1540: <p>Your design is <code>~ Age</code> so you can only test how Age affects each gene and it is assumed that the effect is linear. If you would like to test the effect of sub type on gene expression you'll have to introduce it to the design e.g. <code>~ Age + Sub.Type</code> (or whatever the column name is). This will not test how the effect of Age is different between between the subtypes, if you would like to test this you will have to include the interaction term in the design formula: <code>~ Age + Sub.Type + Age:Sub.Type</code>, this design have a lot of parameters and you will need a lot of samples to have a reliable test.</p>
  <p>Hope it helped</p>
  