Requires modules networkx, 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. This is described in more detail in our paper, which is currently in review.
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
If you use the Walker module, please cite:
Zhang H, Schaefer M, Crawford J, Kiel C, Serrano L, and Cowen LJ. "Studying Gene Prioritization in Tissue-Specific Networks: A Case Study with the BRAF Oncogene." In review. (2016)