Usage

This notebook provides the steps to build a network starting from a list of genes, and using the default functionalities of NeKo.

[1]:

%%time
from neko.core.network import Network
from neko._visual.visualize_network import NetworkVisualizer
import omnipath as op

CPU times: user 6.12 s, sys: 1.61 s, total: 7.73 s
Wall time: 5.23 s

1. Define the list of genes of interest

[2]:

genes = ["SRC", "NOTCH1", "FAK", "CDH1", "CDH2", "VIM", "MAP4K4", "LATS1", "LATS2", "PTK2B"]

2. Create a network using as input the gene set.

Note

NB! If no resource is specified, the default used resource by the package is omnipath.all_interactions(). To add new resources, please see the Add resources tutorial.

2A. Import genes as network nodes

[3]:

new_net1 = Network(genes, resources = 'omnipath')

[4]:

#Print node dataframe
new_net1.nodes

[4]:

	Genesymbol	Uniprot	Type
0	SRC	P12931	NaN
1	NOTCH1	P46531	NaN
2	PTK2	Q05397	NaN
3	CDH1	P12830	NaN
4	CDH2	P19022	NaN
5	VIM	P08670	NaN
6	MAP4K4	O95819	NaN
7	LATS1	O95835	NaN
8	LATS2	Q9NRM7	NaN
9	PTK2B	Q14289	NaN

2B. Explore if the nodes are connected without introducing new intermidiate nodes

[5]:

%%time
new_net1.connect_nodes(only_signed=True, consensus_only=True)

CPU times: user 169 ms, sys: 170 µs, total: 169 ms
Wall time: 168 ms

2C. Visualize the network with the following command.

The network picture is also exported in the working repository, in pdf format.

[6]:

visualizer = NetworkVisualizer(new_net1, color_by='effect', noi=True)
visualizer.render()

../_images/notebooks_1_network_building_11_0.svg

2D. Connect disconnected nodes by importing intermediate nodes.

The parameter maxlen sets the maximum length of the paths to be searched of.

The parameter algorithm sets the base algorithm to use when looking for paths in the resources. It can be ‘bfs’ (Breadth-First Search) or ‘dfs’ (Depth-First Search). The first one is faster but does not always retrieve the shortest path, while the second one retrieves all the possible shortest paths at higher computational cost.

The parameter only_signed forces the algorithm to look for just signed interactions.

The parameter consensus retrieves those interactions with a sign consensus between the references (when possible).

The parameter connect_with_bias, changes the base algorithm, making it look for possible connection between all the disconnected node in the network when a new bridge-gene is introduced in the network, biasing the final network topology.

[7]:

%%time
new_net1.complete_connection(maxlen=3, algorithm="bfs", only_signed=True, connect_with_bias=False, consensus=True)

CPU times: user 9.08 s, sys: 6.68 ms, total: 9.08 s
Wall time: 9.08 s

3. Visualize final network

[8]:

#Visualize a specific nodes and
visualizer1 = NetworkVisualizer(new_net1,color_by='effect', noi=True)
visualizer1.render()

../_images/notebooks_1_network_building_15_0.svg

[9]:

#Visualize a specific nodes and
visualizer1 = NetworkVisualizer(new_net1, predefined_node = "SRC",color_by='effect')
visualizer1.render()

../_images/notebooks_1_network_building_16_0.svg

[10]:

#We can access to the edges of the network
new_net1.edges

[10]:

	source	target	Type	Effect	References
0	P12931	Q05397	None	stimulation	Adhesome:10085298;Adhesome:10592173;Adhesome:1...
1	P12830	P12931	None	stimulation	ACSN:16039586;ACSN:16099633;ACSN:17143292;ACSN...
2	P12931	P19022	None	inhibition	ACSN:15782139;ACSN:16371504;ACSN:16492141;ACSN...
3	P12931	Q14289	None	stimulation	Adhesome:10329689;Adhesome:10521452;Adhesome:1...
4	Q14289	Q05397	None	stimulation	Adhesome:16760434;HPRD:16760434;KEA:12960434;K...
...	...	...	...	...	...
170	P17612	P50552	None	stimulation	Adhesome:10851246;Adhesome:10922374;Adhesome:8...
171	Q13153	O95863	None	stimulation	InnateDB:15833848;KEA:15833848;PhosphoSite:158...
172	Q13153	Q99759	None	stimulation	NaN
173	P00519	Q13188	None	stimulation	PhosphoSite:22590567;ProtMapper:22590567;ProtM...
174	Q13315	P00519	None	stimulation	ACSN:11804596;ACSN:11877377;ACSN:12607003;ACSN...

175 rows × 5 columns