I have some reasonable idea of what directional means are in the context of spatial statistics, but I am stumped by this use of the term in the methods section of this computational biology paper (emphasis mine):
The raw ES values were normalized to account for variable numbers of shRNAs across different genes by dividing the raw ES by the directional mean of a size-matched null distribution generated by 100,000 random permutations of a hairpin set of the same size.
To sum up the relevant part: one is supposed to compute the directional mean of a distribution obtained from a set of scores (no vectors involved).
How would one go about getting a directional mean from a set of values (or associated density function)?
Edit: one piece of information that could be relevant: the score values whose distribution is being plotted and whose "directional mean" is supposed to be extracted, are obtained through a goodness-of-fit test applied to a subset of values ("the hairpin set") out of the whole ranked list (the microarray data, converted to a list of differential expression values).
I also doubt the spatial aspect of the microarray data could play any role here (it's long been eliminated through pre-treatment).