I am working on Gene-Gene interaction graphs. I build a graph by adding edges between genes (nodes) which show statistical interaction in predicting a quantitative parameter value (say, brain volume) in a multiple regression model.

For creating a well-connected graph, I have lowered the p-value threshold in order to include as many edges as possible, so it is likely that a proportion of these links are false-positives.

My question is that, if some nodes show a very high degree centrality‎ which is very unlikely to happen in a random Erdős–Rényi model (Poisson distribution), can I make a statistical inference that this node is biologically relevant to the brain size parameter, even if the graph does not show a Erdős–Rényi degree distribution pattern? What is the best statistical test for this, and if there exists one, is it also important to consider multiple-comparisons in this test?


Sourena Soheili-Nezhad, M.D.


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