text: user9061: If I’m starting with Deseq2 normalized counts what are some preprocessing steps that I should apply to these data before estimating sample correlation using the cor function in R? For example, would it make sense to quantile normalize the data?
  
  user77: You don't absolutely need to perform any further steps before calculating the correlation. However you would likely benefit from the following sort of workflow:
  
  Transform your data with vst() or robust log (see the DESeq2 vignette) so genes are on a more or less similar scale.
  Select the top 300-500 most variable genes
  Use this subset with to compute sample-wise correlation.
  
  This also what follows what you would find in the DESeq2 user guide, which I strongly encourage you to read.
  
original_text: 9061: <p>If I’m starting with Deseq2 normalized counts what are some preprocessing steps that I should apply to these data before estimating sample correlation using the cor function in R? For example, would it make sense to quantile normalize the data?</p>
   77: <p>You don't absolutely need to perform any further steps before calculating the correlation. However you would likely benefit from the following sort of workflow:</p>
  <ul>
  <li>Transform your data with vst() or robust log (see the DESeq2 vignette) so genes are on a more or less similar scale.</li>
  <li>Select the top 300-500 most variable genes</li>
  <li>Use this subset with to compute sample-wise correlation.</li>
  </ul>
  <p>This also what follows what you would find in the DESeq2 user guide, which I strongly encourage you to read.</p>
  