I am doing some standard differential analysis on dnase-seq data between two conditions using deseq2. My results show a huge number of differential regions with around 80% peaks having adj pvalue <0.01. Since I was not expecting such a huge difference between conditions, I think there might be something wrong with my data.
After searching for similar issues, I have come across posts which mention that this could results from replicates being very similar and not capturing enough biological variability. Looking at IGV tracks for my replicates for each condition (3 reps per condition) shows this to be true for my data. My replicates for each condition look extremely similar and almost look like technical replicates (the person who did the experiment is not around, but I am getting a bit suspicious if they really are biological replicates). When I visually compare the differential peaks in IGV, they are usually areas with very small peaks/low reads and show small differences between conditions which probably wouldn’t have been called significant if the replicates were better. Most of my differential analysis results seem to be just noise.
For now I am just ignoring the adj p value cutoff (as pretty much everything has extremely low pvalues) and using a very high logfc cutoff which gives better results. However, I was wondering if there is anything else I can do with the DeSeq2 or edgeR pipeline to account for this lack of variability in replicates so noisy regions are not called significant?