p-value adjustment/FDR control for data with few changes

Hi everyone,

I am running some omics experiments where I compare a treated and an untreated condition. In order to compare them, I am computing a t-test and my output are fold changes and p-values. In this dataset, I only have very vew changes (this is to be expected for this dataset) but I would still like to run p-value adjustment to properly control my false-discovery-rate.
The usual adjustment I run is Benjamini-Hochberg correction. However, in this case the criteria for BH adjustment are not fulfilled, as my p-value histogram shows a flat distribution (no flank to the left for low p-values).
How do I still perform proper FDR control for these cases? Do you maybe have any advice?

Thanks in advance for your help!


Ambassador to the humans
I don't think the "flank to the left" is a requirement. But if the p-value distribution is flat then you might not get anything significant after a B-H adjustment.
You are correct, it needs to be uniform, which means it can also be flat. However, if I adjust them for some experiments my adjusted p-values then become 1 and if I plot a volcano plot, it's basically a flat line. Is there any way to deal with this data or do I need to increase my sample number?