Question: meta analysis of p values from deseq2 output

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tonja.r •

**470**wrote:I have several RNA-seq studies where the same null hypothesis was tested. I analyzed each study with DESeq and as output have p-values and FDR values. I would like to do a meta analysis. By default DESeq2 produces two-sided p-values. For combine.test() function in R I need one sided p-values. So, my idea is just to do divide FDR values by 2: and use them in combine.test(FDR/2). And to get back to two-sided test, I multiply by two the combined p values. Would it be theoretically the right approach?

Hint: The one-sided value isn't half the two-sided value. For example, if a two-sided value is 0.01 then one of the one-sided values will be ~1 and the other significant. So you'd need to decide which side to take.

97kI guess I am either misunderstanding you or I am initially totally wrong. Assume, z-scores are given (so, normal distribution), to calculate two-sided p-values one would do:

`two.sided.p = 2*pnorm(-abs(z))`

and apply`combine.test(two.sided.p/2)`

, right?From DESeq2 paper:

So, I could divide the FDR values by 2 to get one-sided p-values, couldn't I?

30k• written 4.8 years ago by tonja.r •470The one-sided p-value is half the two-sided one if the test statistics distribution is symmetric around 0: e.g. Assuming a Gaussian distribution, P(|x|>5)=P(x<-5)+P(x>5) and because of symmetry, P(x<-5)=P(x>5)=0.5*P(|x|>5). What I think Devon is referring to is that then the one-sided value of the other alternative hypothesis is 1-0.5*P(|x|>5) e.g. P(x<5)= 1-P(X>5).

23kIt is referred to the second part of the question,namely to "And to get back to two-sided test, I multiply by two the combined p values.", isn't it? In this paper I found following:

Or do you mean I need to take the log2FC to account for the direction? It the gene is up or down regulated?

30k• written 4.8 years ago by tonja.r •470combine.test() implements Fisher's method and Stouffer's method to combine p-values. Fisher's statistic follows a chi-square distribution which is not symmetric so you can't multiply the resulting p-value by 2 in this case. With Stouffer's method, you can multiply the resulting p-value by 2 because you're dealing with a symmetric distribution (the Z transform statistic follows a normal distribution).

23kYes, that's exactly what I'm referring to, since there are two one-sided p-values, depending on the alternative hypothesis in question.

97kIf we divide a two tailed p-value from DESeq2 in two, are we thereby selecting the one-tailed p-value that corresponds to the alternative hypothesis of gene expression changing in the direction it did? Is this appropriate, selecting the alternative hypotheses that relate to the direction of change?

I am also trying to combine p values from multiple independent RNASeq datasets and would like to use Stouffer's method, but want to be sure of using the correct source of p-values.

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