Answers.Body: <ol>
  <li>Yes, but there will be a batch effect.</li>
  <li>Not that I know if, you'll need to use sva or something along those lines to handle the batch effect.</li>
  <li>Crossing your fingers and hoping the batch effect is small is pretty much your only other option if the data isn't setup in a way that you can use sva or similar. So yeah, redoing the experiment may be the only real option.</li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-06-03T07:34:50.160Z
Answers.LastActivityDate: 2021-06-03T07:34:50.160Z
Answers.UserDisplayName: Devon Ryan
Body: <p>I want to do differential expression analysis with DESEQ2. I have three read counts files downloaded from GEO (small RNAseq based) where the number of miRNAs and id is nearly the same. These studies have used the same tool for alignment but different tools for counting aligned reads.</p>
  <p>My question is</p>
  <ol>
  <li>Can I combine these read count files</li>
  <li>Is there any recommended meta-analysis based tool for this purpose</li>
  <li>Is there any other way to do this other then redoing RNA-seq analysis.</li>
  </ol>
  <p>Thank you</p>
  
Tags: <rna-seq><reads><meta-analysis>
Title: Combining read counts from three separate GEO studies
ThreadText: # Combining read counts from three separate GEO studies
  
  (Asked by: username_0 on 2021-06-02)
  
  I want to do differential expression analysis with DESEQ2. I have three read counts files downloaded from GEO (small RNAseq based) where the number of miRNAs and id is nearly the same. These studies have used the same tool for alignment but different tools for counting aligned reads.
  
  My question is
  
  1. Can I combine these read count files
  2. Is there any recommended meta-analysis based tool for this purpose
  3. Is there any other way to do this other then redoing RNA-seq analysis.
  
  Thank you
  
  **Tags:** rna-seq, reads, meta-analysis
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-06-03. Score: 1 | Accepted answer
  
  1. Yes, but there will be a batch effect.
  2. Not that I know if, you'll need to use sva or something along those lines to handle the batch effect.
  3. Crossing your fingers and hoping the batch effect is small is pretty much your only other option if the data isn't setup in a way that you can use sva or similar. So yeah, redoing the experiment may be the only real option.
  
  
  ---
  
  