Hello all,

I have some RNA-seq data from an unusual biological system, and as a result I need to look for genes that are differentially expressed genes between two samples, one of which was *super* stressed. My DESeq2 results said that more than 70% of genes had an adjusted p-value below 0.1!  I was expecting a lot of differentially expressed genes, but this seems very high. Is there such a thing as 'too many' DEGs? Is there a way I can test whether my data fall into this category? Does anyone know of any solutions to this issue? Thanks in advance!
This question has been asked before, see previous discussion at [Question: Differential expression for two very different samples][1]. As [swbarnes2][2] said, one assumption of DESeq2 and edgeR is that not too many genes are differentially expressed. edgeR allows the user to mitigate this issue with the argument `logratioTrim` in the function `calcNormFactors()` - see [here][3]. I don't know DESeq2 has a similar option - I believe it doesn't.

Regarding cut-offs: the author of DESeq2 argues that instead of applying a cut-off after a regular test for differential expression (that is, a test  against the null hypothesis of zero LFC), it is better to test if the LFC is above a certain threshold. He argues that the later approach keeps the nominal fdr, while the former doens't. See discussions on DESeq2 vignette and [paper][4], and at the BioConductor forum [here][5] and here. Use the arguments `lfcThreshold` and `altHypothesis` from `results()` to set the desired LFC threshold and test.


  [1]: https://www.biostars.org/p/262676/
  [2]: https://www.biostars.org/u/4407/
  [3]: https://support.bioconductor.org/p/94661/
  [4]: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8
  [5]: https://support.bioconductor.org/p/85509/