Answers.Body: <p>I would use the p-value/FDR which each method returns to rank the gene list for that method in order from 'most likely to be DE' to least likely.  You would then have three ranked lists of genes - you would put the actual rankings in for each gene - 1 for the top ranked, 2 for the next, etc.  You could then carry out, say Spearman's rank correlation on the lists of rankings to assess agreement between the methods.</p>
  
  <p>You could also decide to keep all 3 methods, and accept genes as DE if, for example, at least 2 out of 3 methods give an acceptable p-value/FDR.</p>
  
  <p>From my experience, correlation should be reasonably high at the top of the list, where the methods tend to agree more on the rankings, and when the correlation gets lower, further down the list, would be where I would start to question whether the genes are really differentially expressed.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-08-15T12:04:03.750Z
Answers.Text: This worked! Thank you :)
Answers.UserDisplayName: Lyd
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-08-09T10:01:02.747Z
Answers.LastActivityDate: 2019-08-09T10:01:02.747Z
Answers.UserDisplayName: Jay Moore
Body: <p>I have a lists of DEGs identified by DESeq2, EdgeR and Limma.
  I would like to correlate the the gene rankings in the lists to decide on a package to use in downstream analysis.
  I am havig a few problems with this, would anyone be able to help?</p>
  
  <p>Thanks</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-08T20:16:33.563Z
Comments.Text: Hello Lyd, could you be a bit more specific? I think all these software have their advantages and disadvantages, it's impossible to give you a good advice without more information.
  Also, did you tried to figure it by your own? What are the results of your research and why you are not convinced yet?
Comments.UserDisplayName: Kamil S Jaron
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-09T08:42:06.797Z
Comments.Text: Im doing differential expresion analysis in R. Im comparing KD samples rto control samples using all 3 packages above. It was suggeested to me to do a correlation test between the results but Im no t sure what way to go about it. Not sure what the inputs  should be!
Comments.UserDisplayName: Lyd
Tags: <rna-seq><deseq2><edger><limma><correlation>
Title: Correlate DEGs from DESeq2, EdgeR and Limma results
ThreadText: # Correlate DEGs from DESeq2, EdgeR and Limma results
  
  (Asked by: username_0 on 2019-08-08)
  
  I have a lists of DEGs identified by DESeq2, EdgeR and Limma.
  I would like to correlate the the gene rankings in the lists to decide on a package to use in downstream analysis.
  I am havig a few problems with this, would anyone be able to help?
  
  Thanks
  
  **Tags:** rna-seq, deseq2, edger, limma, correlation
  
  > **Comments:**
  > **username_1**: Hello Lyd, could you be a bit more specific? I think all these software have their advantages and disadvantages, it's impossible to give you a good advice without more information. Also, did you tried to figure it by your own? What are the results of your research and why you are not convinced yet?  
  > **username_0**: Im doing differential expresion analysis in R. Im comparing KD samples rto control samples using all 3 packages above. It was suggeested to me to do a correlation test between the results but Im no t sure what way to go about it. Not sure what the inputs  should be!  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2019-08-09. Score: 1 | Accepted answer
  
  I would use the p-value/FDR which each method returns to rank the gene list for that method in order from 'most likely to be DE' to least likely. You would then have three ranked lists of genes - you would put the actual rankings in for each gene - 1 for the top ranked, 2 for the next, etc. You could then carry out, say Spearman's rank correlation on the lists of rankings to assess agreement between the methods.
  
  You could also decide to keep all 3 methods, and accept genes as DE if, for example, at least 2 out of 3 methods give an acceptable p-value/FDR.
  
  From my experience, correlation should be reasonably high at the top of the list, where the methods tend to agree more on the rankings, and when the correlation gets lower, further down the list, would be where I would start to question whether the genes are really differentially expressed.
  
  > **Comments:**
  > **username_0**: This worked! Thank you :)  
  
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