I have an issue with my differential miRNA expression analysis project. I have two different comparisons treatment1 (T1) vs control and treatment2 (T2) vs control and I performed standard differential expression analysis with DESeq2 and the analysis resulted in 5 significantly differentialy expressed miRNA between T1 and Control and 0 significant miRNA between T2 and controls (padj<0.05). Thus I started to investage what could have gone wrong and thus I plotted the p-value histograms which looked as follows:
Alright, thus i followed the instructions on following website https://www.huber.embl.de/users/klaus/Teaching/DESeq2Predoc2014.html where they use fdrtool to estimate the true null model variance. I did this for both datasets T1vC and T2v C according to the link above. output of the result for one dataset (the second one looks similar): https://paste.pics/4SXS5
So I redid the testing etc acording to the embl protocol from huber lab and now to my main question: new p-value histogram is more what one would expect https://paste.pics/4SXU4 However out of about 550 tested miRNAs I get 68 significantly differentially expressed miRNAs for 1 dataset T1vC and about 120 significant miRNAs for T2vC. And this number just seems way too high, no? I would never expect 1/5 of tested miRNAs to be significantly differentially expressed, or would you?
Which results should & can I trust more?
I appreciate every help, as I am not a statistican...
edit: Thanks for the input. Added 'fdrtool' as a tag