fdrtool corrected p-value histograms look weird
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hele7 ▴ 20
@hele7-15035
Last seen 15 months ago
Estonia

Hi,

I'm performing DE analysis on the miRNA secretome of two cell lines. I'm comparing cells, fraction1,  fraction 2 of the secretome for both of them, resulting in 3 comparisons: fraction1/cells; fraction2/cells; fraction1/fraction2. 

For cell line 1 the p-value histogram before correction look fine (grey barplots), however after p-value correction with fdrtool the distribution is far from perfect (purple barplots) https://ibb.co/ecvUd7 Shouldn't the corrected p-value histograms have the same uniform distribution as uncorrected p-value histograms? Do I need to be worried when using the corrected p-values in defining the DE miRNAs?

Cell line 2 p-value histograms look more problematic https://ibb.co/dby4BS and unfortunately I don't have any idea what could be the reason?? The input gene list has been prefiltered with

keep <- rowMeans (counts(dds)) > 1

resulting in having 469 miRNAs included in the analysis. The dispersion plot can be found here: https://ibb.co/gKwman 

There is definitely smth wrong with my analysis, and considering the histograms, I can't use these corrected p-values to call my DE miRNAs. Yet, I'm unable to track down what the problem could be...Can somebody please look into this? Being not 100% at home with DE analysis/DESeq2, I would appreciate all the help I can get :) Let me know if any piece of code is needed to track down the problem...

pvalue adjusted pvalue fdrtool • 781 views
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Entering edit mode

hi,

This is an fdrtool question, more than a DESeq2 question. I'm changing the tag.

For cell line 1, there is no reason to use p-value correction. For cell line 2, it does seem that the test is being conservative but after fdrtool the histograms don't look much better. I'd stick with the original adjusted p-values produced by DESeq2.

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