I need a sort of review of all (or most of) available methods to create null distributions (as reference) to use to compare a result. For ex: if I want to validate a network I can create a set of 1000 random networks starting from the initial dataset, by shuffling 1000 times my data. Then I obtain a null distribution of the 1000 nets toward which I can compare my own net (bootstrap) to score the significance (if my net is in the tails of the null distributions). Since this is not the best solution according to the data I start with, I need to know all the available methods used to create null distributions to use as a reference. Any suggestion about papers, links etc. useful to me to choose the best method?
PS: I have to create the null distribution starting from a synthetic dataset (a dataset totally created in vitro) so I need to know how to build it first.
Thanks in advance