Exercise 2: Meta‑Networks
Using the following file, containing 5 networks: networks.txt located in the
public/biol0021/ directory (also available from the course webpage):
-
Load the different networks (using the function
read.table()).
-
Replace the missing edges in all the networks by a standard “I don’t know” value of
0.05.
-
Find the inferred network that is the most correlated to redfly.
-
Is a meta‑network weight‑sum improving the results?
-
Based on the top 5 % edges of the best predictive network, can you suggest a few genes that could be
targeted in order to alter the pathway in which
FBgn0001180 is involved?
-
First, select the top 5 % edges of the best network (the most correlated to redfly) and
make it an igraph object.
-
Plot the induced subgraph made of the target gene and all its neighbours.
-
Compute the z‑score of the eigen‑vector and betweenness centrality measures for our target gene.
-
If one of those scores is too high, then compute those measures for all its regulated neighbours
and suggest some “not‑too‑important” genes to target.