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Highly biased experiment showing funny histogram of p values for all tests. Suggestions?

Dear all, I am struggling (as usual) with a really important/expensive experiment we manage to complete. Having done this in different days, plus given the fact we are not expecting a strong phenotype (we are treating cells with a molecule that shouldn't change these ones that much), I would imagine not so many genes differentially expressed. Perfect. I initially corrected the experiment for batch effect. If I plot the histogram of p values I get something like this: enter image description here Which is pretty odd I guess, given the fact I get a lot of unfiltered values in my final experiment that are around pval=1.

then, I decided to remove low counts and then,

dds$condition <- relevel(dds$condition, ref = "B")
dds <- DESeq(dds)

results_A_vs_B<-results(dds, c("condition", "A", "B"))

it's even worse, I guess:

enter image description here

So there's no doubt there's something wrong here but not sure how to get rid of it.

Do you have any ideas?

Thanks

deseq2 rna-seq filtering

I cannot see the figures, is it me or something went wrong?

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