Hello ResearchGate community,

I am replicating differential expression analysis from a paper (here) 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!