Stepwise connection: a focus on the INE algorithm

This notebook provides the steps to build a network starting from a list of genes, and using the Iterative Neighbor Expansion (INE) algorithm.

%%time
from neko.core.network import Network
from neko._visual.visualize_network import NetworkVisualizer
from neko.inputs import Universe, signor
from neko._annotations.gene_ontology import Ontology
import omnipath as op
import pandas as pd

CPU times: user 3.62 s, sys: 758 ms, total: 4.38 s
Wall time: 4.72 s

1. Define the list of genes of interest

genes = ["PIK3CA","MAP2K1","GSK3B","MAPK14","CTNNB1","AKT1","MAP3K7"]

output_nodes_prosurvival = ['CCND1','MYC','TCF7L2']

2. Specify SIGNOR resource

resources = signor()  # this function accept only tab separated values
resources.build()

3. Create a network using as input the gene set.

3A. Import genes as network nodes

new_net = Network(initial_nodes = genes, resources=resources.interactions)

3B. Connect nodes

The INE algorith connects the input nodes by looking and introducing all their immediate neighbors into the network. If the nodes remain unconnected, the immediate neighbors of the newly introduced nodes are added into the network. The process ends either when a complete network is reached or until the user-specified threshold of expansion steps (i.e., max_len) is specified.

The algorithm also allows the definition of outputs, which are nodes that should be connected to the network with only incoming edges. For instance, these nodes might represent phenotypic markers (e.g., proliferation) of interest.

At the end of the algorithm, any nodes that do not have a source in the edge dataframe and are not in the output node are removed..

%%time
new_net.connect_as_atopo(max_len=1, strategy="radial",outputs=output_nodes_prosurvival)

CPU times: user 6.91 s, sys: 6.27 ms, total: 6.92 s
Wall time: 6.92 s

new_net.edges

	source	target	Type	Effect	References	Provenance
0	Q16539	SIGNOR-PF24	None	bimodal	20626350; 12181443	None
1	Q16539	P04150	None	stimulation	20660302; 15817653	None
2	Q16539	P11362	None	inhibition	20626350	None
3	Q16539	Q15750	None	inhibition	19393267; 14592977	None
4	Q16539	P49841	None	inhibition	18451303; 17726008	None
...	...	...	...	...	...	...
261	P35222	P01106	None	stimulation	16510874	None
263	P49840	P01106	None	inhibition	16023596	None
266	Q16539	P24385	None	inhibition	8702807	None
269	P01106	P24385	None	stimulation	12835716	None
270	SIGNOR-PF1	P01106	None	stimulation	11018017	None

178 rows × 6 columns

visualizer = NetworkVisualizer(new_net, color_by='effect', noi=True)
visualizer.render()

visualizer = NetworkVisualizer(new_net, color_by='effect', noi=True, predefined_node="CCND1")
visualizer.render()

visualizer = NetworkVisualizer(new_net, color_by='effect', noi=True, predefined_node="MYC")
visualizer.render()

visualizer = NetworkVisualizer(new_net, color_by='effect', noi=True, predefined_node="TCF7L2")
visualizer.render()