Body: <p>I am replicating differential expression analysis from a paper (<a href="https://www.researchgate.net/publication/325322361_Overexpression_of_VaWRKY14_increases_drought_tolerance_in_Arabidopsis_by_modulating_the_expression_of_stress-related_genes" rel="nofollow noreferrer">here</a>) on RNA-Seq data. I extracted the regulated genes using <code>DESeq2::results</code> with a threshold of 1 for log2 fold change and 0.05 for the p-value. The results are very different from that of the paper. The paper does not mention much information about their statistical methods except for this paragraph:</p>
  <blockquote>
  <p>Finally, the libraries were sequenced using the BGISEQ-500 platform. Differentially expressed genes were identified based on fold change ≥ 2 and diverge probability ≥ 0.8. The functional categories of the differently expressed genes and the pathway were obtained using MapMan as the classification source.</p>
  </blockquote>
  <p>Now, I have no idea what they mean by &quot;diverge probability&quot; but it looks like another method to get the regulated genes. My questions are:</p>
  <p><strong>Question 1:</strong> What could be the method they are using in their analysis?</p>
  <p><strong>Question 2:</strong> Are the results from the &quot;diverge probability&quot; method different from the ones we get using <code>DESeq2::results</code>?</p>
  <p><strong>Question 3:</strong> If the answer to Question 2 is yes, how do we get from one to the other?</p>
  <p>I appreciate any insights or experiences you can share regarding this approach. Thank you!</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-12-14T16:05:18.673Z
Comments.Text: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
Comments.UserDisplayName: Community
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-12-14T20:31:30.970Z
Comments.Text: I'm pretty informed about stats but haven't a clue what "diverge probabilities" are. Are you referring to Kullback–Leibler divergence by any chance? Whilst I understand the question, at present its unanswerable but the metric doesn't appear to correspond to a known critical value approach.
Comments.UserDisplayName: M__
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-12-15T11:15:46.193Z
Comments.Text: @M__ I tried to add more info and edit my questions to be more clear. Hope that helps, and let me know if you want me to add more info.
Comments.UserDisplayName: Hadeer Ibrahiem
Tags: <r><rna-seq><differential-expression>
Title: What method uses "diverge probability" in RNA-Seq differential expression analysis?
ThreadText: # What method uses "diverge probability" in RNA-Seq differential expression analysis?
  
  (Asked by: username_0 on 2023-12-14)
  
  I am replicating differential expression analysis from a paper ([here](https://www.researchgate.net/publication/325322361_Overexpression_of_VaWRKY14_increases_drought_tolerance_in_Arabidopsis_by_modulating_the_expression_of_stress-related_genes)) on RNA-Seq data. I extracted the regulated genes using `DESeq2::results` with a threshold of 1 for log2 fold change and 0.05 for the p-value. The results are very different from that of the paper. The paper does not mention much information about their statistical methods except for this paragraph:
  
  > Finally, the libraries were sequenced using the BGISEQ-500 platform. Differentially expressed genes were identified based on fold change ≥ 2 and diverge probability ≥ 0.8. The functional categories of the differently expressed genes and the pathway were obtained using MapMan as the classification source.
  
  Now, I have no idea what they mean by "diverge probability" but it looks like another method to get the regulated genes. My questions are:
  
  **Question 1:** What could be the method they are using in their analysis?
  
  **Question 2:** Are the results from the "diverge probability" method different from the ones we get using `DESeq2::results`?
  
  **Question 3:** If the answer to Question 2 is yes, how do we get from one to the other?
  
  I appreciate any insights or experiences you can share regarding this approach. Thank you!
  
  **Tags:** r, rna-seq, differential-expression
  
  > **Comments:**
  > **username_1**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  
  > **username_2**: I'm pretty informed about stats but haven't a clue what "diverge probabilities" are. Are you referring to Kullback–Leibler divergence by any chance? Whilst I understand the question, at present its unanswerable but the metric doesn't appear to correspond to a known critical value approach.  
  > **username_0**: @username_2 I tried to add more info and edit my questions to be more clear. Hope that helps, and let me know if you want me to add more info.  
  
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  ## 0 answers
  
  