Cytoscape cola code#

'source': str(source), # the source node id (edge comes from this node) 'target': str(target), # the target node id (edge goes to this node) 'directed': True, If int(source) <= NUM_NODES and int(target) <= NUM_NODES: # jump the first two lines, they contain some info for i, line in enumerate(f):Ĭontinue # get the source node and the target node The nodes are represented by their id with open( 'raw_data/out.subelj_jdk_jdk', 'r') as f: The normalized in-degree number as a score to help us better visualize the nodes:Įdges = # the final subset of edges # each line represents an edge between two nodes. Additionally,įor each node, we calculate the in-degree number which is the number of incoming edges. We also include additional rendering options. For each edge, it is mandatory to specify the Once we know the nodes, we select the edges between them. 'selected': False, # the node is not selected 'selectable': True, # we can select the node 'locked': False, # the node position is not immutable 'grabbable': True # we can grab and move the node 'group': 'nodes', # it belongs in the group of nodes 'removed': False,

'classes': package # the keyword 'classes' is used to group the nodes in classes 'id': str(i + 1), # the string representation of the unique node ID 'idInt': i + 1, # the numeric representation of the unique node ID 'name': 'cls: ' + class_name + ' pkg: ' + package, # the name of the node used for printing 'query': True, Package = '.'.join(full_name.split( '.')) # the package where the class belongs # stop when the limit is reached if i = NUM_NODES:įull_name = str(line) # the fill nameĬlass_name = full_name.split( '.') # only the class name Packages = # the set of packages for each node # each line represents one node with open( 'raw_data/ent.subelj_jdk_', 'r') as f: NUM_NODES = 50 # how many nodes to subselect Along with this, we include some attributes that define the rendering of the For each node, we extract the name of the class it represents and We start by selecting a subset of 50 nodes. Thus, we transform the original data set by using this The Cytoscape JS library expects the data to be stored in a JSON file with a For this reason, just for demonstration purposes, we selectĪ subset of 50 nodes and all the edges between them. Is an immense number to load and render in the browser. The data set is stored in a CSV file format and it contains 6,434 nodes and 150,985 edges, which The dataset we use is the class dependency network of the JDK 1.6.0.7 framework downloaded from theĮach node represents one class and an edge between them means there is a dependency between the classes. Which is a common playground to start with Cytoscape.

Cytoscape cola code#

To demonstrate the capabilities of the Graph Visualization Libraries we use theĪs a starting point, we use the code provided in the following In a similar way, we show how the interactive graph visualization can be beneficial in discovering data patterns. Visualization gives you answers to questions you didn’t know you had. Which is quite aligned with the following quote: 'The Importance of Interactive Data Visualization', Of interactive visualization, we already discussed in one of the previous blogs, This time we go step further and demonstrate what means an interactive graph visualization. We elaborated more on the common data formats, the existing and popularĭata repositories, the JavaScript libraries as well as some GUI-based tools. We dived in the Graph-Visualization ecosystem. When I look into the source code of exportNetworkToCytoscape on github, I found it first calculate a dist measure on TOM, and then apply the threshold to the dist measure.JavaScript: Graph Visualization using Cytoscape JS The node with big size will be considered hub genes.But the hub genes I selected from the visualization step in cytoscape is completely different from hub genes chosen based on module membership. In cytoscape, I map the degree of nodes to node size, which means that the more connection one node has, the bigger size it will be. An edge can exists when the weight between two nodes is above the threshold. The tutorial first calculate TOM, select node in the network, choosing a threshold of the edge and then export to cytoscape.

I'm using the exportNetworkToCytoscape function to export the module of interest to cytoscape.