Requires modules networkx <= 1.11, numpy, scikit-learn, and argparse.
For a description of the Random Walk with Restart (RWR) algorithm, which this module implements, see the paper by Kohler et al. at http://www.sciencedirect.com/science/article/pii/S0002929708001729.
This module can be used to run two types of experiments:
- A standard random walk with restart from a set of seed nodes, as in the Kohler et al. paper referenced above.
- A random walk with restart, from a set of seed nodes, on a "tissue-specific" network. The network is defined by a "low list" of nodes (i.e. genes) that are not expressed in the tissue of interest.
Examples of both experiments are described in more detail below.
The run_walker.py script can be used to run a random walk. The syntax looks like:
python run_walker.py <input_graph> <seed> [-l <low_list>] [-r <remove_nodes>]
where the input graph is in edge list format, the seed is a list of nodes to start the random walk at, the optional low list is a list of nodes to down-weight for node removal experiments, and the optional node removal list is a list of nodes to remove completely from the network.
The script will write a tab-separated list of nodes and probabilities to stdout, where the probability number represents the probability that a random walk starting at the seed nodes will terminate at the given node.
For more detail about the expected arguments, run python run_walker.py -h.
To help you get up and running, a few simple examples are included in the testdata
folder. To run a standard random walk experiment on a simple example network, run
this command:
python run_walker.py testdata/test_network.ppi testdata/test_seed.txt
Or, to run a "tissue-specific" random walk experiment using the same simple example network, try:
python run_walker.py testdata/test_network.ppi testdata/test_seed.txt -l testdata/test_low_list.txt