[bip] testing for clustered-ness on non-random background

[Brent Pedersen]

hi, this isn't a python question per se, but it seems like it might be
a good place to ask.
so i'd like to take a class of genes on a chromosome and see if they
are "clustered".
is there a good way to do this given that the genes are _already_
clustered/non-randomly distributed
along the chromosome due to the centromere, local duplications, etc?
i've thought of:
+ encoding a chromosome as binary with 1 if it's a gene of interest
and 0 for any other gene
and then taking a moving average and finding peaks that fall outside
of 95% limits generated
by monte-carlo. this has the problem (or perhaps benefit) that it
doesn't account for base pair
position, just relative gene position.

+ using geospatial measures like moran's I or geary's C--though those
are generally 2 dimensional,
i think they could be modified to handle distribution along the 1d
chromsome. then i could take something
like the global geary's C for the genome and comparing to the geary's
C for the genes in question.

any literature on this?
thanks for any pointers.
-brent