text: user3186: I'm trying to compare the gene expression data reported in the studies documented in GEO for a specific gene expressed in a tissue of my interest in Homo sapiens.
  I compared the values reported in 4 studies, in two studies the expression value of gene A lies between 15-30, in the other two studies the range lies between 1-4.
  I would like to ask for advice on whether it is meaningful to compare the data from different studies. What are the factors that one should keep in mind while making such comparisons? 
  Any help would be highly appreciated
  
  user77: There's going to be a batch effect between studies, so unless you have some sort of constant group that you can use for between-study normalization you're going to completely muck up your results from using data from multiple studies.
  More specifically, this is what packages like sva and the combat() command in it are used to correct. Note that you should go back to the raw .CEL or .fastq files if you want to process multiple studies, in order to at least ensure that everything is processed in the same way.
  
  user174: It doesn't make sense to compare gene expression data from different studies unless you have a negative spiked-in control such as sequins.
  The most obvious thing you will need to do is normlizate the data for various bias such as batch effects, sequencing platforms, sequencing depth, bioinformatic workflows and more ... Your task will be quite complicated as you must justify how your normalization would work for data from different studies.
  You should check how the raw data was generated (methods section), convince yourself, and then apply gene quantification yourself from the raw files.
  
original_text: 3186: <p>I'm trying to compare the gene expression data reported in the studies documented in GEO for a specific gene expressed in a tissue of my interest in Homo sapiens.
  I compared the values reported in 4 studies, in two studies the expression value of gene A lies between 15-30, in the other two studies the range lies between 1-4.</p>
  
  <p>I would like to ask for advice on whether it is meaningful to compare the data from different studies. What are the factors that one should keep in mind while making such comparisons? </p>
  
  <p>Any help would be highly appreciated</p>
   77: <p>There's going to be a batch effect between studies, so unless you have some sort of constant group that you can use for between-study normalization you're going to completely muck up your results from using data from multiple studies.</p>
  
  <p>More specifically, this is what packages like <code>sva</code> and the <code>combat()</code> command in it are used to correct. Note that you should go back to the raw .CEL or .fastq files if you want to process multiple studies, in order to at least ensure that everything is processed in the same way.</p>
   174: <p>It doesn't make sense to compare gene expression data from different studies unless you have a negative spiked-in control such as <a href="https://www.sequin.xyz/" rel="nofollow noreferrer">sequins</a>.</p>
  
  <p>The most obvious thing you will need to do is normlizate the data for various bias such as batch effects, sequencing platforms, sequencing depth, bioinformatic workflows and more ... Your task will be quite <strong>complicated</strong> as you must justify how your normalization would work for data from different studies.</p>
  
  <p>You should check how the raw data was generated (methods section), convince yourself, and then apply gene quantification yourself from the raw files.</p>
  