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I am using four different packages (viz. EBSeq, DESeq2, edgeR, LPEseq) for identification of differential genes. Now I am confused whose fold change value should I take for further downstream processing.

Please give your suggestions.

Thanks in advance.

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closed as primarily opinion-based by David, De Novo, WYSIWYG, Chris Mar 22 at 13:45

Many good questions generate some degree of opinion based on expert experience, but answers to this question will tend to be almost entirely based on opinions, rather than facts, references, or specific expertise. If this question can be reworded to fit the rules in the help center, please edit the question.

  • $\begingroup$ Why do you use four packages for one task? $\endgroup$ – Maxim Kuleshov Oct 24 '18 at 18:48
  • $\begingroup$ Each package is using a different mathematic or statistical model and trying to fit that model to the data. Each model has different sensitivities and specificities, in different types of sample or experimental setup, so it can be useful to use more than one package. They are all asking a similar question in subtly different ways, so some people believe that taking a consensus can provide a more robust conclusion to the analysis. $\endgroup$ – Jonathan Moore Nov 13 '18 at 10:31
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Unless you have a good reason, stick with the ones that the community at large uses most frequently: EdgeR or DESeq2.

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I would typically have an independently calculated fold-change that I use for QC and visualization. I don't currently have a paper to reference to recommend doing some benchmarks for every project, where I would recommend testing DESeq2 / edgeR / limma-voom. However, there are some papers that cite use of that strategy here in this acknowledgement.

In other words, one way that you could tell if a method may not be optimal for your dataset if you have a known perturbation, you can visually confirm the expected change with the directly calculated log2(FPKM + 0.1) value, but that gene isn't identified as differentially expressed with one of the methods. It is kind of messy, but I do briefly mention this sort of troubleshooting strategy here.

In terms of comparing rlog values from DESeq2 and the direct FPKM / fold-change values, I do have this blog post: http://cdwscience.blogspot.com/2019/02/variance-stabilization-and-pseudocounts.html

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