Creating CX2Network objects

Below are factories that facilitate creation of CX2Network objects in different ways:

Raw CX2

The RawCX2NetworkFactory is designed to create a CX2Network instance directly from raw CX2 data.

from ndex2.cx2 import RawCX2NetworkFactory, CX2Network

# Sample raw CX2 data
raw_cx2_data = {...}  # Replace with actual raw CX2 data

# Creating an instance of RawCX2NetworkFactory
factory = RawCX2NetworkFactory()

# Creating a CX2Network from raw CX2 data
cx2_network = factory.get_cx2network(raw_cx2_data)

# cx2_network is now a populated CX2Network instance

NetworkX

The NetworkXToCX2NetworkFactory is designed to convert a NetworkX graph into a CX2Network. This conversion is suitable for transferring network data from NetworkX to the CX2 format.

import networkx as nx
from ndex2.cx2 import NetworkXToCX2NetworkFactory, CX2Network

# Add nodes and edges to networkx_graph...
networkx_graph = nx.Graph()
networkx_graph.add_node(1, size=5)
networkx_graph.add_node(2, size=6)
networkx_graph.add_node(3, size=7)
networkx_graph.add_edge(1, 2) weight=1.0)
networkx_graph.add_edge(2, 3, weight=0.9)

# Creating an instance of NetworkXToCX2NetworkFactory
factory = NetworkXToCX2NetworkFactory()

# Converting NetworkX graph to CX2Network
cx2_network = factory.get_cx2network(networkx_graph)

# cx2_network is now a CX2Network instance representing the NetworkX graph
print(cx2_network.to_cx2())

Pandas

The PandasDataFrameToCX2NetworkFactory enables the conversion of a pandas.DataFrame into a CX2Network. This is useful for integrating pandas.DataFrame data into the CX2 network structure.

Example 1 (setting node ids)

import pandas as pd
from ndex2.cx2 import PandasDataFrameToCX2NetworkFactory, CX2Network

# DataFrame with source, target, and other columns
data = {'source': [1, 2], 'target': [2, 3],
        'weight': [1.0, 0.9],
        'source_size': [5, 6], 'target_size': [6, 7]}
df = pd.DataFrame(data)

# Creating an instance of PandasDataFrameToCX2NetworkFactory
factory = PandasDataFrameToCX2NetworkFactory()

# Converting DataFrame to CX2Network
cx2_network = factory.get_cx2network(df, source_id='source', target_id='target')

# cx2_network is now a CX2Network instance based on the DataFrame data
print(cx2_network.to_cx2())

Example 2 (using node names)

import pandas as pd
from ndex2.cx2 import PandasDataFrameToCX2NetworkFactory, CX2Network

# DataFrame with source, target, and other columns
data = {'source': ['A', 'B'], 'target': ['B', 'C'],
        'weight': [1.0, 0.9],
        'source_size': [5, 6], 'target_size': [6, 7]}
df = pd.DataFrame(data)

# Creating an instance of PandasDataFrameToCX2NetworkFactory
factory = PandasDataFrameToCX2NetworkFactory()

# Converting DataFrame to CX2Network
cx2_network = factory.get_cx2network(df, source_field='source', target_field='target')

# cx2_network is now a CX2Network instance based on the DataFrame data
print(cx2_network.to_cx2())

Warning

Use source_field and target_field when you use node names (e.g. gene symbols, proteins etc.), use source_id and target_id when you want to set specific node IDs (numerical values). It is possible to use both.

Column Naming Convention

Source and target nodes of an edge.
By default, the columns source_name and target_name represent the names of the source and target nodes, respectively and source_id and target_id represent the unique identifiers for the source and target nodes.

It can be changed by setting the parameter source_field and target_field to column names containing source/ target names, and source_id and target_id to column names containing source/target ids. Specifying ids is not necessary.
Node attributes.
Node attributes can be specified with a prefix according to their node type:
Use the parameter source_node_attr_prefix to set prefix for attributes of the source node (default source_, e.g., source_color, the node will have attribute color).

Use the parameter target_node_attr_prefix to set prefix for attributes of the target node (default target_ e.g., target_size, the node will have attribute size).
If the prefixes are used, attributes in the form prefix_x (e.g. source_x) will be used as x, y, and z coordinates of the node

They can also be explicitly specified as a list passed in parameter source_node_attr for edge source node and target_node_attr for edge target node. The same columns can be used for both source and target node attributes (e.g. source_node_attr=['color', 'size'] and target_node_attr=['color', 'size']).
Edge attributes.
Edge attributes can be specified as list in parameter edge_attr. In case the parameter is not set, all columns that were not used as node attributes will be used (e.g., weight for an edge’s weight attribute).

The edge_interaction parameter defines the default interaction type for edges. If not specified in the data frame as edge attribute, this default value is used. If not set, the default interaction is set to interacts-with.

Example with column names passed as parameters

import pandas as pd
from ndex2.cx2 import PandasDataFrameToCX2NetworkFactory

# DataFrame with custom column names for nodes and attributes
data = {'Protein 1': ['NodeA', 'NodeB'], 'Protein 2': ['NodeB', 'NodeC'],
        'node_id1': [100, 200], 'node_id2': [200, 300], 'connection_strength': [0.8, 0.7],
        'color': ['red', 'red'], 's_size': [1, 2], 't_size': [2, 1]}
df = pd.DataFrame(data)

# Creating an instance of PandasDataFrameToCX2NetworkFactory
factory = PandasDataFrameToCX2NetworkFactory()

# Creating CX2Network with custom parameters
cx2_network_custom = factory.get_cx2network(df,
    source_field='Protein 1', target_field='Protein 2',
    source_id='node_id1', target_id='node_id2',
    source_node_attr=['color', 's_size'], target_node_attr=['color', 't_size'],
    edge_interaction='binds-to')

# cx2_network_custom is now a CX2Network instance with custom settings
print(cx2_network_custom.to_cx2())

Warning

Please note that if a node is listed both as a source and a target, or appears multiple times either as a source or a target, its attributes will be updated to reflect the most recent data entry in the dataframe. This means that each node’s attributes will correspond to their latest occurrence in the dataset.

For example, if node ‘A’ appears in row 1 with the attribute color=red, and then appears again in row 5 of the dataframe with the attribute color=blue, the attribute color of this node will be updated to blue.

NiceCXNetwork

See Convert NiceCXNetwork to CX2Netowrk