I have a set of eight clusters from a larger gene regulatory network consisting of transcription factors and the genes they regulate (bipartite network). The problem is that most of the genes in all those clusters are uncharacterized genes. I am hence not able to derive much biological information from the network. I use cytoscape for my analysis.
Currently, I did a comparison between the eight clusters for topological parameters like betweenness centrality, closeness centrality etc, just to prove that the clusters are distinct from each other. However, my supervisor said that the clustering algorithm I used to split the network should do that anyway.
I generated random networks and compared the network distribution to prove that the network had a scale free distribution.
I used motif discovery app in Cytoscape to find the number of 3 node motifs in the network, compared that number with the three node motifs generated by the random network and said that there were more three node motifs in the real network statistically (though I do not know how to draw a biological conclusion from this result).
I did use DAVID to get some gene ontology information about the genes that were identified in each cluster which were very few ( out of 400 nodes) approx 130 had some biological info and a max 20 genes per pathway
I am at a loss as to what other analysis I could do on these clusters to get more information in a biological context or atleast compare topologically in some way.
So, this is more an issue relating to uncharacterised genes as opposed to a network analysis issue? Is the species of interest a prokaryote? With minimal information on your genes, you may be limited to doing things like blastX (of the protein sequence of your genes) in order to see to which proteins in other species they align, and thus infer their possible function.