text: user704: I'm planning to run a GSE Analysis on some microarray datasets. Until now what I have done is to pre-processed them using frma algorithm.
  The reason I chose frma instead of gcrma or rma is that I wanted to merge these different dataset into one.
  Here is how I'm planing to proceed:
  i.e
  dataset1 : 3x Controls , 3x Treatment1
  dataset2 : 2x Controls , 2x Treatment2
  dataset3 : 2x Controls , 2x Treatment3
  
  After merging
  7x Controls , 3x Treatment1 , 2x Treatment2 , 2x Treatment3
  
  Then I will execute the GSEA for controls , Treatmen1 , Treatment2 and Treatment3 separably.
  So the question is if it's more reasonable to follow the path that I described or to run the GSEA on the separate datasets (for example for each control ) and then get the intersect of the gene sets of these three repetitions.
  
  user48: It makes more sense to evaluate by separate the pathway or gene set you want and see if in the three datasets result in a coherent message than to merge these datasets, as you will mix batch effects and treatment effects.
  When in several datasets one looks for the same statistic it is usually considered meta analysis, not integration. 
  
original_text: 704: <p>I'm planning to run a GSE Analysis on some microarray datasets. Until now what I have done is to pre-processed them using frma algorithm.</p>
  
  <p>The reason I chose frma instead of gcrma or rma is that I wanted to merge these different dataset into one.</p>
  
  <p>Here is how I'm planing to proceed:</p>
  
  <p>i.e</p>
  
  <pre><code>dataset1 : 3x Controls , 3x Treatment1
  dataset2 : 2x Controls , 2x Treatment2
  dataset3 : 2x Controls , 2x Treatment3
  </code></pre>
  
  <p>After merging</p>
  
  <pre><code>7x Controls , 3x Treatment1 , 2x Treatment2 , 2x Treatment3
  </code></pre>
  
  <p>Then I will execute the GSEA for controls , Treatmen1 , Treatment2 and Treatment3 separably.</p>
  
  <p>So the question is if it's more reasonable to follow the path that I described or to run the GSEA on the separate datasets (for example for each control ) and then get the intersect of the gene sets of these three repetitions.</p>
   48: <p>It makes more sense to evaluate by separate the pathway or gene set you want and see if in the three datasets result in a coherent message than to merge these datasets, as you will mix batch effects and treatment effects.</p>
  
  <p>When in several datasets one looks for the same statistic it is usually considered meta analysis, not integration. </p>
  