More information on network analysis with multi_graph.pkl

[ri23]

Dear @bgyori ,
Than you so much for the information.
One more thing, since the description on github is scanty regarding the handling of multi_graph.pkl file, Could you kindly
elaborate on this please.
What is mentioned in the tutorial page is :
TIP (optional): to perform a connectivity analysis as described in our publication programmatically: the GeneWalk network (networkx format) itself is also output as a multi_graph.pkl file (pickle binary format), which can be loaded into Python for further analysis.More details on the output can be found on our GitHub page.
Kindly help, I am new to network analysis.
regards.

Originally posted by @amitpande74 in #30 (comment)

[ri23]

Hi @amitpande74
Regarding the multi_graph.pkl:
You can load it into python as follows:

import networkx as nx
import os
folder = '/home/amit/genewalk/chigozie/context1/'
fmg = 'multi_graph.pkl'
with open(os.path.join(folder,fmg), 'rb') as f:
       MG = pkl.load(f)

The GeneWalk Network, loaded as MG above, is a networkx.MultiGraph object, documentation here:
https://networkx.org/documentation/stable/reference/classes/multigraph.html
Intuitively, this networkx.MultiGraph is a little bit similar to a Python dictionary
The genes and GO terms are the nodes in the network. You can see all the nodes listed by running
MG.nodes
If you have a gene of interest from your input gene list 'MYC' (the genes are encoded as hgnc gene symbols in the network) or GO term ID ('GO:1234567'), then you can see all their connected neighboring nodes through:
MG['MYC'] and MG['GO:1234567']
The edge labels tell you the type of edges in the network as retrieved from Pathway Commons and GO ontology/annotation files.
Hope this helps!
Robert

Originally posted by @ri23 in #30 (comment)