text: user10368: I have a dataset that looks something like this
  
  
  
  
  
  Treatment1
  Treatment 2
  Control
  
  
  
  
  Sample1
  3.23.
  0.87.
  1
  
  
  Sample2
  1.71.
  1.
  1
  
  
  Sample3
  2.88.
  5.65
  1
  
  
  Sample4
  0.77.
  1.34
  1
  
  
  
  
  The numbers describe a fold change with respect to the control (where the control is always set to 1).
  Does it make sense to compute values such as averages, sums, and differences (e.g. the average fold change for treatment1)? And yes, wary that doing things on log fold-change is not a thing, but how about just fold change?
  By extension, does it also make sense to compute something like Euclidean distances (e.g. fold change vector of Sample1 vs. Sample2) for clustering fold change data? I think clustering on counts/TPM makes more sense, but wasn't sure about doing it on fold changes.
  Something tells me that it's not possible because the fold changes are relative values as opposed to measured numbers. I've opted for looking at correlations (e.g. Spearman) for now though; are there alternative metrics/strategies to consider?
  If there are some good papers talking about clustering and manipulating fold change data I'd be equally grateful, thanks!
  
  user77: 
  Does it make sense to compute values such as averages, sums, and differences (e.g. the average fold change for treatment1)? And yes, wary that doing things on log fold-change is not a thing, but how about just fold change?
  
  This is problematic, if you want to compare treatments then compute the actual fold change and its significance. You can't really base anything off of the fold-change (log or otherwise) alone.
  
  does it also make sense to compute something like Euclidean distances for clustering fold change data
  
  It's not unheard of to do this. The problem is you lose all information about how reliable those fold-change estimates are. This is something maintained when you cluster sample-level information (e.g., TPM or robust log transformed data). Not that using variance-stabilized data for clustering will work better, since it tamps down on how much highly-expressed genes influence your clustering (assuming you don't want that to occur).
  Have a look at the DESeq2 vignette for some nice examples of what good practices are.
  
original_text: 10368: <p>I have a dataset that looks something like this</p>
  <div class="s-table-container">
  <table class="s-table">
  <thead>
  <tr>
  <th></th>
  <th>Treatment1</th>
  <th>Treatment 2</th>
  <th>Control</th>
  </tr>
  </thead>
  <tbody>
  <tr>
  <td>Sample1</td>
  <td>3.23.</td>
  <td>0.87.</td>
  <td>1</td>
  </tr>
  <tr>
  <td>Sample2</td>
  <td>1.71.</td>
  <td>1.</td>
  <td>1</td>
  </tr>
  <tr>
  <td>Sample3</td>
  <td>2.88.</td>
  <td>5.65</td>
  <td>1</td>
  </tr>
  <tr>
  <td>Sample4</td>
  <td>0.77.</td>
  <td>1.34</td>
  <td>1</td>
  </tr>
  </tbody>
  </table>
  </div>
  <p>The numbers describe a fold change with respect to the control (where the control is always set to 1).</p>
  <p>Does it make sense to compute values such as averages, sums, and differences (e.g. the average fold change for treatment1)? And yes, wary that doing things on log fold-change is not a thing, but how about <em>just</em> fold change?</p>
  <p>By extension, does it also make sense to compute something like Euclidean distances (e.g. fold change vector of Sample1 vs. Sample2) for clustering fold change data? I think clustering on counts/TPM makes more sense, but wasn't sure about doing it on fold changes.</p>
  <p>Something tells me that it's not possible because the fold changes are relative values as opposed to measured numbers. I've opted for looking at correlations (e.g. Spearman) for now though; are there alternative metrics/strategies to consider?</p>
  <p>If there are some good papers talking about clustering and manipulating fold change data I'd be equally grateful, thanks!</p>
   77: <blockquote>
  <p>Does it make sense to compute values such as averages, sums, and differences (e.g. the average fold change for treatment1)? And yes, wary that doing things on log fold-change is not a thing, but how about just fold change?</p>
  </blockquote>
  <p>This is problematic, if you want to compare treatments then compute the actual fold change and its significance. You can't really base anything off of the fold-change (log or otherwise) alone.</p>
  <blockquote>
  <p>does it also make sense to compute something like Euclidean distances for clustering fold change data</p>
  </blockquote>
  <p>It's not unheard of to do this. The problem is you lose all information about how reliable those fold-change estimates are. This is something maintained when you cluster sample-level information (e.g., TPM or robust log transformed data). Not that using variance-stabilized data for clustering will work better, since it tamps down on how much highly-expressed genes influence your clustering (assuming you don't want that to occur).</p>
  <p>Have a look at the DESeq2 vignette for some nice examples of what good practices are.</p>
  