Hi,
I realise this question does not strictly relate to the scope of this
ML, however I was still hoping somebody could help me with this.
I have used greedy modularity maximisation to obtain a partition of
communities for my protein network.
Separately, I have used a semantic based approach to detect the
functional "relatedness" of each pair of proteins in the network. So for
each pair of interactors in the net, I have a value from 0 to 1 telling
me how close those two nodes are in terms of some metric of the
annotation in Gene Ontology.
Now I would like to evaluate the significance of my communities from a
functional point of view, using these scores.
Would it make sense to do the following: for each community, get all the
functional scores for of all its interacting pairs. Then get the same
number of interacting pairs observed in the community, but at random
from the network. The run a Mann–Whitney U between the two vectors of
scores.
Another option would maybe be to randomise x1000 times the original
network (degree conserving), rerun the community finding and compare the
distribution of some functional value for the random networks to the
original net, and then get z-score. The problem is that I don't have a
"summary" metric to compare to some null network models: I only have
similarity scores for pairs of interactors.
Any suggestions would be greatly appreciated.
Thanks
G
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