Body: <p>I haven't found any relevant posts, so here is my question (it's more a question on statistics I believe):</p>
  <p>I have the deseq2 output of DE genes( around 25000 genes) , and among them 1000 are significantly differentiated(and I want to check them). Now, I group these 25000 genes by the epigenetic states of my data(i divide the full genome into 3 groups and I also have 4th group which are regions whose group I couldn’t identify due to lack of coverage etc ). So now I have my 25,000 genes where roughly 12000 belong to group a, 8000 belong to group 4 unidentified, 4500 belong to group b and 500 belong to group c. Now, what i do , from these 25000 genes I define my 4 gene sets where each group is one term so 4 in total, then i take my 1000 DEGs , i rank them and then I use prerank approach to get the enrichment results. I also hesitate to just stick ot hypergeometric test instead, but then not sure what I should consider in this case.</p>
  <p>My question is: How can I check if there are more differentially expressed genes in one group compared to others? I am particularly interested in group A, i expect it to have more DE genes (i don't know if upregulated or downregulated or both mixed).</p>
  <p>So far I've decided to go with hypergeometric test, but if my hypothesis is that one group is overrepresented with DE than others, i'm not sure it's a good approach; I tried GSEA too but because i have very few large gene sets ( where i define by a gene set all the genes that fall into one group), i hesitate if it's a suitable approach.</p>
  <p>I would greatly appreciate any help and tips answers and comments.  Thanks a lot everyone!!</p>
  <p>best regards,
  alexa</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-08-17T09:59:31.337Z
Comments.Text: @M__ I've corrected it. hope it's fine now. thank you
Comments.UserDisplayName: dew
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-08-17T10:28:29.337Z
Comments.Text: Thanks @drew, it's still not absolutely clear but is clear enough.
Comments.UserDisplayName: M__
Tags: <r><deseq2><gsea><go-enrichment><t-test>
Title: enrichment of DE genes within wide regions not pathways, hypergeometric test or GSEA?
ThreadText: # enrichment of DE genes within wide regions not pathways, hypergeometric test or GSEA?
  
  (Asked by: username_0 on 2023-08-16)
  
  I haven't found any relevant posts, so here is my question (it's more a question on statistics I believe):
  
  I have the deseq2 output of DE genes( around 25000 genes) , and among them 1000 are significantly differentiated(and I want to check them). Now, I group these 25000 genes by the epigenetic states of my data(i divide the full genome into 3 groups and I also have 4th group which are regions whose group I couldn’t identify due to lack of coverage etc ). So now I have my 25,000 genes where roughly 12000 belong to group a, 8000 belong to group 4 unidentified, 4500 belong to group b and 500 belong to group c. Now, what i do , from these 25000 genes I define my 4 gene sets where each group is one term so 4 in total, then i take my 1000 DEGs , i rank them and then I use prerank approach to get the enrichment results. I also hesitate to just stick ot hypergeometric test instead, but then not sure what I should consider in this case.
  
  My question is: How can I check if there are more differentially expressed genes in one group compared to others? I am particularly interested in group A, i expect it to have more DE genes (i don't know if upregulated or downregulated or both mixed).
  
  So far I've decided to go with hypergeometric test, but if my hypothesis is that one group is overrepresented with DE than others, i'm not sure it's a good approach; I tried GSEA too but because i have very few large gene sets ( where i define by a gene set all the genes that fall into one group), i hesitate if it's a suitable approach.
  
  I would greatly appreciate any help and tips answers and comments. Thanks a lot everyone!!
  
  best regards,
  alexa
  
  **Tags:** r, deseq2, gsea, go-enrichment, t-test
  
  > **Comments:**
  > **username_0**: @username_1 I've corrected it. hope it's fine now. thank you  
  > **username_1**: Thanks @drew, it's still not absolutely clear but is clear enough.  
  
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  ## 0 answers
  
  