I would like to add a cautionary note about the above answer here, regarding the nonmonotonocity of q-values (corrected p-values) that would result from the formula.

The source code for the p.adjust function in R is here:

BH = {
    i <- lp:1L
    o <- order(p, decreasing = TRUE)
    ro <- order(o)
    pmin(1, cummin(n/i * p[o]))[ro]}
  

Which fixes this issue in the last line.

You can use a function in R,p.adjust.

pBenjaminiHochberg = min( p * n / r , 1)

where p is the original p-value, n is the number of computed p-values in total and r is the rank of the original p-value when p-values are sorted in ascending order.

It should be possible to do the manipulations manually in Excel, but if you can use R, there is the p.adj function that is more straightforward.

Dear Carlo,

Thank you very much.

Is this right: p-values in ascending order: 1) 0,00002 2) 0,00034 3)0,0024 4)0,0235 5) 0,12424

pBenjaminiHochberg = min(0,00002 * 5 / 1) = 0,0001 pBenjaminiHochberg = min(0,00034 * 5 / 2) = 0,00085 pBenjaminiHochberg = min(0,0024 * 5 / 3) = 0,004 pBenjaminiHochberg = min(0,0235 * 5 / 4) = 0,029375 pBenjaminiHochberg = min(0,12424 * 5 / 4) = 0,12424

Best,

Martin