<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>

CC BY-SA 4.0
2023-12-14T16:05:18.673Z
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.
Community
CC BY-SA 4.0
2023-12-14T20:31:30.970Z
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.
M__
CC BY-SA 4.0
2023-12-15T11:15:46.193Z
@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.
Hadeer Ibrahiem
<r><rna-seq><differential-expression>
What method uses "diverge probability" in RNA-Seq differential expression analysis?
# 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**: @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.  

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## 0 answers

