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DESeq2 LFC thresholding strategy and its affect on p-value adjustment

Dear all,

I am not a statistician and my knowledge of statistics in limited. I have been working on a project where we used RNA seq, and analysed the results with DESeq2 method. I have been experimenting with log 2 fold change thresholding and noticed that p values change whether I set the threshold for lfc inside the results function or I do thresholding manually afterwards. To make it more clear, this is the code I used:

(i)

res<- results(dds,
                          contrast = c("treatment", "AICAr", "ctrl"),
                          lfcThreshold = 1, alpha = 0.05)

(ii)

res<- results(dds,
                          contrast = c("treatment", "AICAr", "ctrl"))
res <- res[abs(res$log2FoldChange) > 1 & padj < 0.05, ]

First strategy seems very conservative - give much larger p-values, and much smaller number of genes that are significantly different. Can someone explain why is this so? Also, what do you think is the better approach?

rna-seq deseq2 log fold change p-value
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updated by andres.firrincieli andres.firrincieli • written by ivn25 • ivn25

1 answer

Hi,

here is the answer: link

Hope this helps

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written by andres.firrincieli andres.firrincieli

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