How to correct alpha, and not p-values themselves, for visualization purposes I have a set of differentially methylated/expressed/whatever entities with p-values attached (example below).

entity_name    p-value    magnitude
entity1        0.04459    0.68
entity2        0.02283    0.99
...
entity_n       0.78       0.025


Typically, I apply the p.adjust function in R with the "fdr" (Benjamini-Hochberg) approach to leave me with p-values adjusted to control the FDR.

adjusted &lt;- p.adjust(mydata,"fdr")


However, I am interested in showing a volcano plot with the unadjusted p-values, and two alpha levels: 0.05 and one that corresponds to the correction. What is the best way to get this alpha? Is it appropriate to set the "corrected alpha" to the lowest original p-value that doesn't pass FDR correction?
The only way to get the alpha levels is to determine what they will be with p.adjust(), since they will depend on the distribution of your unadjusted p values. The general steps you should follow will be:


Add a column of adjusted p-values to your dataframe (mydata$padj = p.adjust(mydata, method="BH"), which is the same as FDR and saves a character).
Use which and max to determine your two alpha threshold (e.g., max(mydata$pvalue[mydata$padj &lt; 0.05])


Then you can adjust your plots however you like (presumably with some horizontal lines at the various alphas). Whether you take the smallest non-significant value or the largest significant value is up to you, just describe what "dots on the line" represent.